Relevant bibliographies by topics / Variational Autoencoders (VAEs)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Variational Autoencoders (VAEs)'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Variational Autoencoders (VAEs).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Variational Autoencoders (VAEs)"

1

Singh, Aman, and Tokunbo Ogunfunmi. "An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications." Entropy 24, no. 1 (2021): 55. http://dx.doi.org/10.3390/e24010055.

Full text
Abstract:
Autoencoders are a self-supervised learning system where, during training, the output is an approximation of the input. Typically, autoencoders have three parts: Encoder (which produces a compressed latent space representation of the input data), the Latent Space (which retains the knowledge in the input data with reduced dimensionality but preserves maximum information) and the Decoder (which reconstructs the input data from the compressed latent space). Autoencoders have found wide applications in dimensionality reduction, object detection, image classification, and image denoising applications. Variational Autoencoders (VAEs) can be regarded as enhanced Autoencoders where a Bayesian approach is used to learn the probability distribution of the input data. VAEs have found wide applications in generating data for speech, images, and text. In this paper, we present a general comprehensive overview of variational autoencoders. We discuss problems with the VAEs and present several variants of the VAEs that attempt to provide solutions to the problems. We present applications of variational autoencoders for finance (a new and emerging field of application), speech/audio source separation, and biosignal applications. Experimental results are presented for an example of speech source separation to illustrate the powerful application of variants of VAE: VAE, β-VAE, and ITL-AE. We conclude the paper with a summary, and we identify possible areas of research in improving performance of VAEs in particular and deep generative models in general, of which VAEs and generative adversarial networks (GANs) are examples.
APA, Harvard, Vancouver, ISO, and other styles
2

Lyu, Zhuoyue, Safinah Ali, and Cynthia Breazeal. "Introducing Variational Autoencoders to High School Students." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12801–9. http://dx.doi.org/10.1609/aaai.v36i11.21559.

Full text
Abstract:
Generative Artificial Intelligence (AI) models are a compelling way to introduce K-12 students to AI education using an artistic medium, and hence have drawn attention from K-12 AI educators. Previous Creative AI curricula mainly focus on Generative Adversarial Networks (GANs) while paying less attention to Autoregressive Models, Variational Autoencoders (VAEs), or other generative models, which have since become common in the field of generative AI. VAEs' latent-space structure and interpolation ability could effectively ground the interdisciplinary learning of AI, creative arts, and philosophy. Thus, we designed a lesson to teach high school students about VAEs. We developed a web-based game and used Plato's cave, a philosophical metaphor, to introduce how VAEs work. We used a Google Colab notebook for students to re-train VAEs with their hand-written digits to consolidate their understandings. Finally, we guided the exploration of creative VAE tools such as SketchRNN and MusicVAE to draw the connection between what they learned and real-world applications. This paper describes the lesson design and shares insights from the pilot studies with 22 students. We found that our approach was effective in teaching students about a novel AI concept.
APA, Harvard, Vancouver, ISO, and other styles
3

Nugroho, Herminarto, Meredita Susanty, Ade Irawan, Muhamad Koyimatu, and Ariana Yunita. "Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System." Jurnal Ilmu Komputer dan Informasi 13, no. 1 (2020): 9. http://dx.doi.org/10.21609/jiki.v13i1.761.

Full text
Abstract:
This paper proposes a fully convolutional variational autoencoder (VAE) for features extraction from a large-scale dataset of fire images. The dataset will be used to train the deep learning algorithm to detect fire and smoke. The features extraction is used to tackle the curse of dimensionality, which is the common issue in training deep learning with huge datasets. Features extraction aims to reduce the dimension of the dataset significantly without losing too much essential information. Variational autoencoders (VAEs) are powerfull generative model, which can be used for dimension reduction. VAEs work better than any other methods available for this purpose because they can explore variations on the data in a specific direction.
APA, Harvard, Vancouver, ISO, and other styles
4

Papadopoulos, Dimitris, and Vangelis D. Karalis. "Variational Autoencoders for Data Augmentation in Clinical Studies." Applied Sciences 13, no. 15 (2023): 8793. http://dx.doi.org/10.3390/app13158793.

Full text
Abstract:
Sample size estimation is critical in clinical trials. A sample of adequate size can provide insights into a given population, but the collection of substantial amounts of data is costly and time-intensive. The aim of this study was to introduce a novel data augmentation approach in the field of clinical trials by employing variational autoencoders (VAEs). Several forms of VAEs were developed and used for the generation of virtual subjects. Various types of VAEs were explored and employed in the production of virtual individuals, and several different scenarios were investigated. The VAE-generated data exhibited similar performance to the original data, even in cases where a small proportion of them (e.g., 30–40%) was used for the reconstruction of the generated data. Additionally, the generated data showed even higher statistical power than the original data in cases of high variability. This represents an additional advantage for the use of VAEs in situations of high variability, as they can act as noise reduction. The application of VAEs in clinical trials can be a useful tool for decreasing the required sample size and, consequently, reducing the costs and time involved. Furthermore, it aligns with ethical concerns surrounding human participation in trials.
APA, Harvard, Vancouver, ISO, and other styles
5

Kiran, Vadduri Uday. "HAVAE – An Advanced Approach for Malware Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 2740–46. http://dx.doi.org/10.22214/ijraset.2024.59303.

Full text
Abstract:
Abstract: The dynamic situation of cybersecurity necessitates continuous adaptation to the evolving and sophisticated nature of malware. This study proposes an innovative approach to enhancing threat detection methodologies by combining Adversarial Autoencoders (AAEs) and Variational Autoencoders (VAEs) for unsupervised malware detection. AAEs, with their EncoderDecoder structure and adversarial techniques, are integrated with VAEs to discern latent representations which are crucial for discriminating between malware and harmless software. This model, referred to as Hybrid Adversarial-Variational Autoencoder (HAVAE), takes advantage of both of their strengths architectures, capturing nuanced features within a latent space through unsupervised learning. The HAVAE model employs the Reparameterization Technique, crucial for sampling latent variables, ensuring the generation of realistic samples while retaining discriminative attributes essential for accurate malware identification. Through comprehensive evaluations across diverse datasets, the efficiency of HAVAE is assessed using metrics encompassing precision, recall, and F1-score. The evaluation underscores the model's robust ability to detect malicious software effectively, emphasizing its potential as a versatile cybersecurity tool. This innovative approach represents a revolution in cybersecurity, utilizing the strength of unsupervised learning techniques, AAEs, and VAEs. The findings signify a significant advancement in adaptive and resilient malware detection systems, illuminating pathways for improved threat identification and mitigation in the ever-evolving cybersecurity landscape
APA, Harvard, Vancouver, ISO, and other styles
6

Battey, C. J., Gabrielle C. Coffing, and Andrew D. Kern. "Visualizing population structure with variational autoencoders." G3 Genes|Genomes|Genetics 11, no. 1 (2021): 1–11. http://dx.doi.org/10.1093/g3journal/jkaa036.

Full text
Abstract:
Abstract Dimensionality reduction is a common tool for visualization and inference of population structure from genotypes, but popular methods either return too many dimensions for easy plotting (PCA) or fail to preserve global geometry (t-SNE and UMAP). Here we explore the utility of variational autoencoders (VAEs)—generative machine learning models in which a pair of neural networks seek to first compress and then recreate the input data—for visualizing population genetic variation. VAEs incorporate nonlinear relationships, allow users to define the dimensionality of the latent space, and in our tests preserve global geometry better than t-SNE and UMAP. Our implementation, which we call popvae, is available as a command-line python program at github.com/kr-colab/popvae. The approach yields latent embeddings that capture subtle aspects of population structure in humans and Anopheles mosquitoes, and can generate artificial genotypes characteristic of a given sample or population.
APA, Harvard, Vancouver, ISO, and other styles
7

Potu, Rakshitha Reddy, Naalla Sushma, Baru Shiva Kumar, and Aruna Kumari Kumbhagiri. "Real Image Restoration Using VAEs." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 889–98. http://dx.doi.org/10.22214/ijraset.2022.43964.

Full text
Abstract:
Abstract— Old photos are an integral part of everybody’s life; they remind us of how one person has spent their life. As people used hard copies of photos before, those photos suffered severe degradation. This degradation in real-time images is intricate, causing thetypical restoration that might be solved through supervised learning to fail to generalize due to the domain gap between synthetic and real images. Therefore, this method uses various autoencoders to restore and colourize old images. Furthermore, this model uses a unique triplet domain translation network on real images and synthetic photo pairs. Precisely, VAEs, which are variational autoencoders, are trained to transform old pictures and clean pictures into two latent spaces. Therefore, the translation between these two latent spaces is comprehended with simulated paired data. This translation generalizes well to authentic images because the domain gap is encompassed in the close-packed latent space. Moreover, to manoeuvre numerous degradations present in one old picture, this model designs aworld branchwith a partial non-local block targeting the structured faults, like scrapes and dirt marks, and an area branch targeting unstructured faults, like noisesand fuzziness. Two branches are blended within the latent space, resulting in an improved ability to renew old pictures from numerous defects. Additionally, it applies another face refinementnetwork to revive fine details of faces within the old pictures, thus generating photos with amplified quality. Another autoencoder is encoded with colour images, and then the decoder decodes the features extracted from the encoder. Once a model is trained, testing is performed to colourize the photographs. Keywords— Degraded pictures, Variational Auto Encoders, Domain gap, Triplet domaintranslation.
APA, Harvard, Vancouver, ISO, and other styles
8

Yang, FengLei, Fei Liu, and ShanShan Liu. "Collaborative Filtering Based on a Variational Gaussian Mixture Model." Future Internet 13, no. 2 (2021): 37. http://dx.doi.org/10.3390/fi13020037.

Full text
Abstract:
Collaborative filtering (CF) is a widely used method in recommendation systems. Linear models are still the mainstream of collaborative filtering research methods, but non-linear probabilistic models are beyond the limit of linear model capacity. For example, variational autoencoders (VAEs) have been extensively used in CF, and have achieved excellent results. Aiming at the problem of the prior distribution for the latent codes of VAEs in traditional CF is too simple, which makes the implicit variable representations of users and items too poor. This paper proposes a variational autoencoder that uses a Gaussian mixture model for latent factors distribution for CF, GVAE-CF. On this basis, an optimization function suitable for GVAE-CF is proposed. In our experimental evaluation, we show that the recommendation performance of GVAE-CF outperforms the previously proposed VAE-based models on several popular benchmark datasets in terms of recall and normalized discounted cumulative gain (NDCG), thus proving the effectiveness of the algorithm.
APA, Harvard, Vancouver, ISO, and other styles
9

Akkari, Nissrine, Fabien Casenave, Elie Hachem, and David Ryckelynck. "A Bayesian Nonlinear Reduced Order Modeling Using Variational AutoEncoders." Fluids 7, no. 10 (2022): 334. http://dx.doi.org/10.3390/fluids7100334.

Full text
Abstract:
This paper presents a new nonlinear projection based model reduction using convolutional Variational AutoEncoders (VAEs). This framework is applied on transient incompressible flows. The accuracy is obtained thanks to the expression of the velocity and pressure fields in a nonlinear manifold maximising the likelihood on pre-computed data in the offline stage. A confidence interval is obtained for each time instant thanks to the definition of the reduced dynamic coefficients as independent random variables for which the posterior probability given the offline data is known. The parameters of the nonlinear manifold are optimized as the ones of the decoder layers of an autoencoder. The parameters of the conditional posterior probability of the reduced coefficients are the ones of the encoder layers of the same autoencoder. The optimization of both sets of the encoder and the decoder parameters is obtained thanks to the application of a variational Bayesian method, leading to variational autoencoders. This Reduced Order Model (ROM) is not a regression model over the offline pre-computed data. The numerical resolution of the ROM is based on the Chorin projection method. We apply this new nonlinear projection-based Reduced Order Modeling (ROM) for a 2D Karman Vortex street flow and a 3D incompressible and unsteady flow in an aeronautical injection system.
APA, Harvard, Vancouver, ISO, and other styles
10

Wang, Ziyang. "Addressing Posterior Collapse in Variational Autoencoders with β-VAE". Highlights in Science, Engineering and Technology 57 (11 липня 2023): 161–67. http://dx.doi.org/10.54097/hset.v57i.9995.

Full text
Abstract:
Posterior collapse is a pervasive issue in Variational Autoencoders (VAEs) that leads to the learned latent representations becoming trivial and devoid of meaningful information. To address this problem, this paper presents a novel β-VAE approach, which incorporates a hyperparameter β to strike an optimal balance between the reconstruction loss and the KL divergence loss. By conducting a comprehensive series of experiments and drawing comparisons with existing methods, robust evidence is provided that the proposed β-VAE method effectively mitigates posterior collapse and yields more expressive and informative latent representations.The experimental setup involves various architectures and datasets to demonstrate the versatility and efficacy of the β-VAE approach in diverse settings. Additionally, ablation studies are performed to investigate the impact of different β values on the model's performance, elucidating the role of this hyperparameter in controlling the trade-off between reconstruction quality and latent representation expressiveness. Furthermore, the disentanglement properties of the learned latent space are analyzed, which is a crucial aspect of VAEs, especially when applied to complex, real-world data.In-depth analysis of the results offers valuable insights into the underlying mechanisms of β-VAE, thereby contributing to a more profound understanding of VAEs and their inherent limitations. The findings not only establish the effectiveness of the β-VAE method in preventing posterior collapse but also pave the way for future research on improving VAEs' performance in various applications. Potential future work could explore alternative techniques for balancing the competing objectives of reconstruction and latent representation learning or delve into the theoretical properties of β-VAE, providing a more rigorous foundation for this approach.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Variational Autoencoders (VAEs)"

1

Nilsson, Mårten. "Augmenting High-Dimensional Data with Deep Generative Models." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969.

Full text
Abstract:
Data augmentation is a technique that can be performed in various ways to improve the training of discriminative models. The recent developments in deep generative models offer new ways of augmenting existing data sets. In this thesis, a framework for augmenting annotated data sets with deep generative models is proposed together with a method for quantitatively evaluating the quality of the generated data sets. Using this framework, two data sets for pupil localization was generated with different generative models, including both well-established models and a novel model proposed for this purpose. The unique model was shown both qualitatively and quantitatively to generate the best data sets. A set of smaller experiments on standard data sets also revealed cases where this generative model could improve the performance of an existing discriminative model. The results indicate that generative models can be used to augment or replace existing data sets when training discriminative models.<br>Dataaugmentering är en teknik som kan utföras på flera sätt för att förbättra träningen av diskriminativa modeller. De senaste framgångarna inom djupa generativa modeller har öppnat upp nya sätt att augmentera existerande dataset. I detta arbete har ett ramverk för augmentering av annoterade dataset med hjälp av djupa generativa modeller föreslagits. Utöver detta så har en metod för kvantitativ evaulering av kvaliteten hos genererade data set tagits fram. Med hjälp av detta ramverk har två dataset för pupillokalisering genererats med olika generativa modeller. Både väletablerade modeller och en ny modell utvecklad för detta syfte har testats. Den unika modellen visades både kvalitativt och kvantitativt att den genererade de bästa dataseten. Ett antal mindre experiment på standardiserade dataset visade exempel på fall där denna generativa modell kunde förbättra prestandan hos en existerande diskriminativ modell. Resultaten indikerar att generativa modeller kan användas för att augmentera eller ersätta existerande dataset vid träning av diskriminativa modeller.
APA, Harvard, Vancouver, ISO, and other styles
2

Eskandari, Aram. "VAE-clustering of neural signals and their association to cytokines." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273627.

Full text
Abstract:
In this thesis we start by reproducing previous experiments by Zanos et al., where they have shown that it is possible to associate neural signals with specific cytokines. One future aim of this project is to send synthetic neural signals through the efferent arc of the vagus nerve and observe reactions without the corresponding catalyst of the symptoms. We use a variational autoencoder (VAE) in our experiment to create a model able to generate new neural signals, and we introduce a novel clustering technique called VAE-clustering, which will be used to cluster neural signals with their associated cytokines. The focus of this paper is the implementation of this method and applying it on the neural signals. Running VAE-clustering on the MNIST dataset shows it to be viable for finding detailed properties of a dataset. We also find that using a VAE as a generative model for neural signals is a good way for recreating detailed waveforms.<br>I detta examensarbete börjar vi med att reproducera tidigare experiment av Zanos et al., där dom visat att det är möjligt att associera nervsignaler med specifika cytokiner. Ett framtida mål med detta projekt är att skicka syntetiska nervsignaler till kroppen för att observera reaktioner utan motsvarande katalysator av symptomen. Vi använder en variational autoencoder (VAE) i våra experiment för att skapa en modell kapabel till att generera nya nervsignaler, och vi introducerar en ny klusterings-teknik kallad VAE-klustring, vilken kommer att användas för att klustra nervsignaler med dess associerade cytokiner. Fokuset i detta arbete ligger i implementationen av denna metod och applicerandet på nervsignaler. Efter att ha kört VAE-klustring på MNIST dataset fann vi att det det är användbart för att hitta detaljerade egenskaper hos ett dataset. Vi har även funnit att användningen av en VAE som en generativ modell för nervsignaler är ett bra sätt att återskapa detaljerade vågformer.
APA, Harvard, Vancouver, ISO, and other styles
3

Trentin, Matteo. "Estensione a due stadi di modelli VAE per la generazione di immagini." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19138/.

Full text
Abstract:
Una particolare applicazione delle tecniche di deep learning riguarda la generazione di nuovi contenuti, come ad esempio audio o immagini; un approccio popolare a questo campo consiste nei Variational Autoencoder, o VAE. In questa tesi vengono inizialmente presentati alcuni concetti base di deep learning e spiegato il funzionamento dei VAE; successivamente viene analizzato un miglioramento recentemente proposto in letteratura a questo tipo di modello, il Two-Stage VAE, e ne vengono verificati i vantaggi dal punto di vista della qualità generativa; viene poi mostrata una possibile e originale estensione condizionale al Two-Stage VAE, con relativi risultati sperimentali su due diversi dataset.
APA, Harvard, Vancouver, ISO, and other styles
4

Reinholdsen, Fredrik. "A Blind Constellation Agnostic VAE Channel Equalizer and Non Data-Assisted Synchronization." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-86062.

Full text
Abstract:
High performance and high bandwidth wireless digital communication underlies much of modern society. Due to its high value to society, new and improved digital communication technologies, allowing even higher speeds, better coverage, and lower latency are constantly being developed. The field of Machine Learning has exploded in recent years, showing incredible promise and performance at many tasks in a wide variety of fields. Channel Equalization and synchronization are critical parts of any wireless communication system, to ensure coherence between the transmitter and receiver, and to compensate for the often severe channel conditions. This study mainly explores the use of a Variational Autoencoder (VAE) architecture, presented in a previous study, for blind channel equalization without access to pilot symbols or ground-truth data. This thesis also presents a new, non data-assisted method of carrier frequency synchronization based around the k-means clustering algorithm. The main addition of this thesis however is a constellation agnostic implementation of the reference VAE architecture, for equalization of all rectangular QAM constellations. The approach significantly outperforms the traditional blind adaptive Constant Modulus algorithm (CMA) on all tested constellations and signal to noise ratios (SNRs), nearly equaling the performance of a non-blind Least Mean Squares (LMS) based Decision Feedback Equalizer (DFE).
APA, Harvard, Vancouver, ISO, and other styles
5

Carlsson, Filip, and Philip Lindgren. "Deep Scenario Generation of Financial Markets." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273631.

Full text
Abstract:
The goal of this thesis is to explore a new clustering algorithm, VAE-Clustering, and examine if it can be applied to find differences in the distribution of stock returns and augment the distribution of a current portfolio of stocks and see how it performs in different market conditions. The VAE-clustering method is as mentioned a newly introduced method and not widely tested, especially not on time series. The first step is therefore to see if and how well the clustering works. We first apply the algorithm to a dataset containing monthly time series of the power demand in Italy. The purpose in this part is to focus on how well the method works technically. When the model works well and generates proper results with the Italian Power Demand data, we move forward and apply the model on stock return data. In the latter application we are unable to find meaningful clusters and therefore unable to move forward towards the goal of the thesis. The results shows that the VAE-clustering method is applicable for time series. The power demand have clear differences from season to season and the model can successfully identify those differences. When it comes to the financial data we hoped that the model would be able to find different market regimes based on time periods. The model is though not able distinguish different time periods from each other. We therefore conclude that the VAE-clustering method is applicable on time series data, but that the structure and setting of the financial data in this thesis makes it to hard to find meaningful clusters. The major finding is that the VAE-clustering method can be applied to time series. We highly encourage further research to find if the method can be successfully used on financial data in different settings than tested in this thesis.<br>Syftet med den här avhandlingen är att utforska en ny klustringsalgoritm, VAE-Clustering, och undersöka om den kan tillämpas för att hitta skillnader i fördelningen av aktieavkastningar och förändra distributionen av en nuvarande aktieportfölj och se hur den presterar under olika marknadsvillkor. VAE-klusteringsmetoden är som nämnts en nyinförd metod och inte testad i stort, särskilt inte på tidsserier. Det första steget är därför att se om och hur klusteringen fungerar. Vi tillämpar först algoritmen på ett datasätt som innehåller månatliga tidsserier för strömbehovet i Italien. Syftet med denna del är att fokusera på hur väl metoden fungerar tekniskt. När modellen fungerar bra och ger tillfredställande resultat, går vi vidare och tillämpar modellen på aktieavkastningsdata. I den senare applikationen kan vi inte hitta meningsfulla kluster och kan därför inte gå framåt mot målet som var att simulera olika marknader och se hur en nuvarande portfölj presterar under olika marknadsregimer. Resultaten visar att VAE-klustermetoden är väl tillämpbar på tidsserier. Behovet av el har tydliga skillnader från säsong till säsong och modellen kan framgångsrikt identifiera dessa skillnader. När det gäller finansiell data hoppades vi att modellen skulle kunna hitta olika marknadsregimer baserade på tidsperioder. Modellen kan dock inte skilja olika tidsperioder från varandra. Vi drar därför slutsatsen att VAE-klustermetoden är tillämplig på tidsseriedata, men att strukturen på den finansiella data som undersöktes i denna avhandling gör det svårt att hitta meningsfulla kluster. Den viktigaste upptäckten är att VAE-klustermetoden kan tillämpas på tidsserier. Vi uppmuntrar ytterligare forskning för att hitta om metoden framgångsrikt kan användas på finansiell data i andra former än de testade i denna avhandling
APA, Harvard, Vancouver, ISO, and other styles
6

Lousseief, Elias. "MahlerNet : Unbounded Orchestral Music with Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264993.

Full text
Abstract:
Modelling music with mathematical and statistical methods in general, and with neural networks in particular, has a long history and has been well explored in the last decades. Exactly when the first attempt at strictly systematic music took place is hard to say; some would say in the days of Mozart, others would say even earlier, but it is safe to say that the field of algorithmic composition has a long history. Even though composers have always had structure and rules as part of the writing process, implicitly or explicitly, following rules at a stricter level was well investigated in the middle of the 20th century at which point also the first music writing computer program based on mathematics was implemented. This work in computer science focuses on the history of musical composition with computers, also known as algorithmic composition, using machine learning and neural networks and consists of two parts: a literature survey covering in-depth the last decades in the field from which is drawn inspiration and experience to construct MahlerNet, a neural network based on the previous architectures MusicVAE, BALSTM, PerformanceRNN and BachProp, capable of modelling polyphonic symbolic music with up to 23 instruments. MahlerNet is a new architecture that uses a custom preprocessor with musical heuristics to normalize and filter the input and output files in MIDI format into a data representation that it uses for processing. MahlerNet, and its preprocessor, was written altogether for this project and produces music that clearly shows musical characteristics reminiscent of the data it was trained on, with some long-term structure, albeit not in the form of motives and themes.<br>Matematik och statistik i allmänhet, och maskininlärning och neurala nätverk i synnerhet, har sedan långt tillbaka använts för att modellera musik med en utveckling som kulminerat under de senaste decennierna. Exakt vid vilken historisk tidpunkt som musikalisk komposition för första gången tillämpades med strikt systematiska regler är svårt att säga; vissa skulle hävda att det skedde under Mozarts dagar, andra att det skedde redan långt tidigare. Oavsett vilket, innebär det att systematisk komposition är en företeelse med lång historia. Även om kompositörer i alla tider följt strukturer och regler, medvetet eller ej, som en del av kompositionsprocessen började man under 1900-talets mitt att göra detta i högre utsträckning och det var också då som de första programmen för musikalisk komposition, baserade på matematik, kom till. Den här uppsatsen i datateknik behandlar hur musik historiskt har komponerats med hjälp av datorer, ett område som också är känt som algoritmisk komposition. Uppsatsens fokus ligger på användning av maskininlärning och neurala nätverk och består av två delar: en litteraturstudie som i hög detalj behandlar utvecklingen under de senaste decennierna från vilken tas inspiration och erfarenheter för att konstruera MahlerNet, ett neuralt nätverk baserat på de tidigare modellerna MusicVAE, BALSTM, PerformanceRNN och BachProp. MahlerNet kan modellera polyfon musik med upp till 23 instrument och är en ny arkitektur som kommer tillsammans med en egen preprocessor som använder heuristiker från musikteori för att normalisera och filtrera data i MIDI-format till en intern representation. MahlerNet, och dess preprocessor, är helt och hållet implementerade för detta arbete och kan komponera musik som tydligt uppvisar egenskaper från den musik som nätverket tränats på. En viss kontinuitet finns i den skapade musiken även om det inte är i form av konkreta teman och motiv.
APA, Harvard, Vancouver, ISO, and other styles
7

Branca, Danilo. "Generazione di attributi facciali mediante Feature-wise Linear Modulation." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20361/.

Full text
Abstract:
L’aspetto dell’apprendimento automatico su cui si sta lavorando di più, negli ultimi anni, è quello della generazione di dati, come ad esempio suoni, testi e immagini. Un aspetto interessante nel campo della generazione è la possibilità di condizionare il modo in cui la rete neurale genera nuovi dati. Recentemente è stata introdotta la tecnica del Feature-wise Linear Modulation, abbreviato “FiLM”, usata per influenzare in modo adattivo l’output di una rete neurale basandosi su un input arbitrario, applicando una trasformazione affine sulle features intermedie della rete. Lo scopo dell’elaborato è mostrare l’integrazione di livelli FiLM all'interno di un modello Variational Autoencoder (VAE). Il modello così ottenuto verrà analizzato per le sue capacità di ricostruzione dell’input e di generazione di nuovi volti umani, sulla base di specifici attributi. Il modello verrà allenato sui volti presenti nel dataset CelebA e ne verrà valutata la capacità di ricostruzione e generazione attraverso la metrica della Fréchet Inception Distance (FID). Inoltre verrà condotto un piccolo esperimento per valutare la capacità del FID di discriminare alcuni attributi.
APA, Harvard, Vancouver, ISO, and other styles
8

Di, Felice Marco. "Unsupervised anomaly detection in HPC systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

Find full text
Abstract:
Alla base di questo studio vi è l'analisi di tecniche non supervisionate applicate per il rilevamento di stati anomali in sistemi HPC, complessi calcolatori capaci di raggiungere prestazioni dell'ordine dei PetaFLOPS. Nel mondo HPC, per anomalia si intende un particolare stato che induce un cambiamento delle prestazioni rispetto al normale funzionamento del sistema. Le anomalie possono essere di natura diversa come il guasto che può riguardare un componente, una configurazione errata o un'applicazione che entra in uno stato inatteso provocando una prematura interruzione dei processi. I datasets utilizzati in un questo progetto sono stati raccolti da D.A.V.I.D.E., un reale sistema HPC situato presso il CINECA di Casalecchio di Reno, o sono stati generati simulando lo stato di un singolo nodo di un virtuale sistema HPC analogo a quello del CINECA modellato secondo specifiche funzioni non lineari ma privo di rumore. Questo studio propone un approccio inedito, quello non supervisionato, mai applicato prima per svolgere anomaly detection in sistemi HPC. Si è focalizzato sull'individuazione dei possibili vantaggi indotti dall'uso di queste tecniche applicate in tale campo. Sono stati realizzati e mostrati alcuni casi che hanno prodotto raggruppamenti interessanti attraverso le combinazioni di Variational Autoencoders, un particolare tipo di autoencoder probabilistico con la capacità di preservare la varianza dell'input set nel suo spazio latente, e di algoritmi di clustering, come K-Means, DBSCAN, Gaussian Mixture ed altri già noti in letteratura.
APA, Harvard, Vancouver, ISO, and other styles
9

Hameed, Khurram. "Computer vision based classification of fruits and vegetables for self-checkout at supermarkets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2519.

Full text
Abstract:
The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide range of areas such as the development of autonomous vehicles, object recognition, Content Based Image Retrieval (CBIR), image segmentation and terrestrial analysis from space (i.e. crop estimation). Despite significant prior research, the area of object recognition still has many topics to be explored. This PhD thesis focuses on using advanced machine learning approaches to enable the automated recognition of fresh produce (i.e. fruits and vegetables) at supermarket self-checkouts. This type of complex classification task is one of the most recently emerging applications of advanced computer vision approaches and is a productive research topic in this field due to the limited means of representing the features and machine learning techniques for classification. Fruits and vegetables offer significant inter and intra class variance in weight, shape, size, colour and texture which makes the classification challenging. The applications of effective fruit and vegetable classification have significant importance in daily life e.g. crop estimation, fruit classification, robotic harvesting, fruit quality assessment, etc. One potential application for this fruit and vegetable classification capability is for supermarket self-checkouts. Increasingly, supermarkets are introducing self-checkouts in stores to make the checkout process easier and faster. However, there are a number of challenges with this as all goods cannot readily be sold with packaging and barcodes, for instance loose fresh items (e.g. fruits and vegetables). Adding barcodes to these types of items individually is impractical and pre-packaging limits the freedom of choice when selecting fruits and vegetables and creates additional waste, hence reducing customer satisfaction. The current situation, which relies on customers correctly identifying produce themselves leaves open the potential for incorrect billing either due to inadvertent error, or due to intentional fraudulent misclassification resulting in financial losses for the store. To address this identified problem, the main goals of this PhD work are: (a) exploring the types of visual and non-visual sensors that could be incorporated into a self-checkout system for classification of fruits and vegetables, (b) determining a suitable feature representation method for fresh produce items available at supermarkets, (c) identifying optimal machine learning techniques for classification within this context and (d) evaluating our work relative to the state-of-the-art object classification results presented in the literature. An in-depth analysis of related computer vision literature and techniques is performed to identify and implement the possible solutions. A progressive process distribution approach is used for this project where the task of computer vision based fruit and vegetables classification is divided into pre-processing and classification techniques. Different classification techniques have been implemented and evaluated as possible solution for this problem. Both visual and non-visual features of fruit and vegetables are exploited to perform the classification. Novel classification techniques have been carefully developed to deal with the complex and highly variant physical features of fruit and vegetables while taking advantages of both visual and non-visual features. The capability of classification techniques is tested in individual and ensemble manner to achieved the higher effectiveness. Significant results have been obtained where it can be concluded that the fruit and vegetables classification is complex task with many challenges involved. It is also observed that a larger dataset can better comprehend the complex variant features of fruit and vegetables. Complex multidimensional features can be extracted from the larger datasets to generalise on higher number of classes. However, development of a larger multiclass dataset is an expensive and time consuming process. The effectiveness of classification techniques can be significantly improved by subtracting the background occlusions and complexities. It is also worth mentioning that ensemble of simple and less complicated classification techniques can achieve effective results even if applied to less number of features for smaller number of classes. The combination of visual and nonvisual features can reduce the struggle of a classification technique to deal with higher number of classes with similar physical features. Classification of fruit and vegetables with similar physical features (i.e. colour and texture) needs careful estimation and hyper-dimensional embedding of visual features. Implementing rigorous classification penalties as loss function can achieve this goal at the cost of time and computational requirements. There is a significant need to develop larger datasets for different fruit and vegetables related computer vision applications. Considering more sophisticated loss function penalties and discriminative hyper-dimensional features embedding techniques can significantly improve the effectiveness of the classification techniques for the fruit and vegetables applications.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Variational Autoencoders (VAEs)"

1

Vishnu Shankar, S., S. R. Naffees Gowsar, and M. Manjubala. "Variational Autoencoders (VAEs)." In Information Systems Engineering and Management. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-91660-1_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Mostofi, Fatemeh, Onur Behzat Tokdemir, and Vedat Toğan. "Leveraging Variational Autoencoder for Improved Construction Progress Prediction Performance." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4355-1_51.

Full text
Abstract:
AbstractThe imbalanced construction dataset reduces the accuracy of the machine learning model. This issue that addressed by recent construction management research through different sampling approaches. Despite their advantages, the utilized sampling approaches are reducing the reliability of the prediction model, while posing the risk of artificial bias. The objective of this study is to address the challenge of imbalanced datasets in construction progress prediction models using a novel variational autoencoder (VAE) that generates synthetic data for underrepresented classes. The VAE's encoder-decoder architecture, along with its latent space components, is optimized for this task. A comparative analysis using decision tree-based ML models, including grid search optimization, substantiated the effectiveness of the VAE approach. The results indicate that the hybrid dataset benefited the ML models from the addition of the synthesized dataset, showing 2% improvements in performance metrics across most models. The synthetic data generated by VAEs contributes to the construction of more balanced datasets, which, in turn, can lead to more reliable and accurate predictive models. The enhanced accuracy of the VAE-ML model addresses the class imbalance problem and improves the reliability of construction productivity predictions and related resource allocation plans.
APA, Harvard, Vancouver, ISO, and other styles
3

Hou, Fangli, Jun Ma, Jack C. P. Cheng, and Helen H. L. Kwok. "Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.93.

Full text
Abstract:
Early failure detection and abnormal data reconstruction in sensor data provided by building ventilation control systems are critical for public health. Early detection of abnormal data can help prevent failures in crucial components of ventilation systems, which can result in a variety of issues, from energy wastage to catastrophic outcomes. However, conventional fault detection models ignore valuable features of dynamic fluctuations in indoor air quality (IAQ) measurements and early warning signals of faulty sensor data. This study introduces a hybrid framework for early failure detection and abnormal data reconstruction applying variance analysis and variational autoencoders (VAE) coupled with the long short-term memory network (VAE-LSTM). The periodicity and stable fluctuation of IAQ data are exploited by variance analysis to detect unusual variations before failure occurs. The IAQ dataset which is corrupted by introducing complete failure, bias failure and precision degradation fault is then used to verify the feasibility of the VAE-LSTM model. The results of variance analysis reveal that unusual behavior of the data can be detected as early as 12 hours before failure occurs. The reconstruction performance of the developed method is shown to be superior to other methods under different abnormal data scenarios
APA, Harvard, Vancouver, ISO, and other styles
4

Hou, Fangli, Jun Ma, Jack C. P. Cheng, and Helen H. L. Kwok. "Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.93.

Full text
Abstract:
Early failure detection and abnormal data reconstruction in sensor data provided by building ventilation control systems are critical for public health. Early detection of abnormal data can help prevent failures in crucial components of ventilation systems, which can result in a variety of issues, from energy wastage to catastrophic outcomes. However, conventional fault detection models ignore valuable features of dynamic fluctuations in indoor air quality (IAQ) measurements and early warning signals of faulty sensor data. This study introduces a hybrid framework for early failure detection and abnormal data reconstruction applying variance analysis and variational autoencoders (VAE) coupled with the long short-term memory network (VAE-LSTM). The periodicity and stable fluctuation of IAQ data are exploited by variance analysis to detect unusual variations before failure occurs. The IAQ dataset which is corrupted by introducing complete failure, bias failure and precision degradation fault is then used to verify the feasibility of the VAE-LSTM model. The results of variance analysis reveal that unusual behavior of the data can be detected as early as 12 hours before failure occurs. The reconstruction performance of the developed method is shown to be superior to other methods under different abnormal data scenarios
APA, Harvard, Vancouver, ISO, and other styles
5

Purkait, Pulak, Christopher Zach, and Ian Reid. "SG-VAE: Scene Grammar Variational Autoencoder to Generate New Indoor Scenes." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58586-0_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Gandikota, Rohit, and Deepak Mishra. "HD-VAE-GAN: Hiding Data with Variational Autoencoder Generative Adversarial Networks." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31407-0_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Ojha, Nikhil, Indrajeet Kumar Sinha, and Krishna Pratap Singh. "VAE-AD: Unsupervised Variational Autoencoder for Anomaly Detection in Hyperspectral Images." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1648-1_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Kim, Seunghwan, and Seungkyu Lee. "Beta-Sigma VAE: Separating Beta and Decoder Variance in Gaussian Variational Autoencoder." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78389-0_24.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Mukesh, K., Srisurya Ippatapu Venkata, Spandana Chereddy, E. Anbazhagan, and I. R. Oviya. "A Variational Autoencoder—General Adversarial Networks (VAE-GAN) Based Model for Ligand Designing." In International Conference on Innovative Computing and Communications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2821-5_64.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Covaci, Emanuel, Flavia Costi, and Darian M. Onchis. "ESM-VAE: Bias Reduction in EEG Models via Synthetic Data Generation with Variational Autoencoders." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-6008-7_15.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Variational Autoencoders (VAEs)"

1

Mohamed, Aezeden, Rainier Nii, Kipas Binga, Alok Kumar Pandey, J. Karpagam, and T. J. Nnadhini. "3D Object Reconstruction from 2D Images Using Variational Autoencoders (VAE)." In 2025 International Conference on Automation and Computation (AUTOCOM). IEEE, 2025. https://doi.org/10.1109/autocom64127.2025.10957632.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Pucci, Rita, and Niki Martinel. "CE-VAE: Capsule Enhanced Variational AutoEncoder for Underwater Image Enhancement." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00212.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Zhong, Cheng, Junlin Wu, Ziming Feng, Boan Chen, and Junchi Yan. "Towards Green VAE: A Light Pixel-weighting Technique to Enhance Variational AutoEncoder." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10887908.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Saha, Surojit, Sarang Joshi, and Ross Whitaker. "ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00096.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Wijanarko, Hansen, Evelyne Calista, Li-Fen Chen, and Yong-Sheng Chen. "Tri-VAE: Triplet Variational Autoencoder for Unsupervised Anomaly Detection in Brain Tumor MRI." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00397.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Ambekar, Namrata Govind, and Surmila Thokchom. "UL-VAE: An Unsupervised Learning Approach for Zero-day Malware Detection Using Variational Autoencoder." In 2024 International Conference on Computational Intelligence and Network Systems (CINS). IEEE, 2024. https://doi.org/10.1109/cins63881.2024.10864450.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

R, Naveen Kumar, Surendran R, and Sumathy K. "Improved AI-Generated Multitrack Music with Variational Autoencoders (VAE) for Harmonious Balance Compared to Recurrent Neural Network for Coherence." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911702.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Yang, Chen. "Attn-VAE-GAN:Text-Driven High-Fidelity Image Generation Model with Deep Fusion of Self-Attention and Variational Autoencoder." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651077.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Fan, Yiming, Peiyuan Zhou, David Forrester, Brian Ju, and Fotis Kopsaftopoulos. "Evaluation of Local and Global Diagnostics for the Integration of Stochastic Time Series Models and Variational Autoencoders: Experimental Assessment on a Full Scale Helicopter Blade." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1371.

Full text
Abstract:
In this work, a unified framework integrating global and local SHM methods for structural health monitoring (SHM) of rotorcraft structures is proposed. This framework integrates both "local" ultrasonic-guided wave-based and "global" vibration-based SHM schemes for tackling damage detection, identification, and quantification under uncertainty. The local SHM is completed by training a variation of variational auto-encoder (MMD-VAE) along with feed-forward neural networks (FFNN). The compressed latent space vector obtained during the training process is applied to achieve both signal reconstruction and state prediction. In terms of the global model, functionally pooled auto-regressive models with exogenous excitation (VFP-ARX) models are applied including to capture low-frequency vibrations. The complete experimental evaluation and assessment of the proposed framework are presented for an Airbus H125 helicopter blade under both low-frequency vibrations and ultrasonic guided waves for SHM.
APA, Harvard, Vancouver, ISO, and other styles
10

Jamal, Arshad, R. Kanesaraj Ramasamy, and Junaidi Abdullah. "Generative AI Respiratory and Cardiac Sound Separation Using Variational Autoencoders (VAEs)." In International Conference on Sustainable Computing and Green Technologies. MDPI, 2025. https://doi.org/10.3390/cmsf2025010009.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Variational Autoencoders (VAEs)' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography