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Zeitschriftenartikel zum Thema „Generative sequence models“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

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1

Wang, Yongkang, Xuan Liu, Feng Huang, Zhankun Xiong, and Wen Zhang. "A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (2024): 3–11. http://dx.doi.org/10.1609/aaai.v38i1.27749.

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Therapeutic peptides represent a unique class of pharmaceutical agents crucial for the treatment of human diseases. Recently, deep generative models have exhibited remarkable potential for generating therapeutic peptides, but they only utilize sequence or structure information alone, which hinders the performance in generation. In this study, we propose a Multi-Modal Contrastive Diffusion model (MMCD), fusing both sequence and structure modalities in a diffusion framework to co-generate novel peptide sequences and structures. Specifically, MMCD constructs the sequence-modal and structure-modal

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Wu, Zachary, Kadina E. Johnston, Frances H. Arnold, and Kevin K. Yang. "Protein sequence design with deep generative models." Current Opinion in Chemical Biology 65 (December 2021): 18–27. http://dx.doi.org/10.1016/j.cbpa.2021.04.004.

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Akl, Hoda, Brooke Emison, Xiaochuan Zhao, Arup Mondal, Alberto Perez, and Purushottam D. Dixit. "GENERALIST: A latent space based generative model for protein sequence families." PLOS Computational Biology 19, no. 11 (2023): e1011655. http://dx.doi.org/10.1371/journal.pcbi.1011655.

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Generative models of protein sequence families are an important tool in the repertoire of protein scientists and engineers alike. However, state-of-the-art generative approaches face inference, accuracy, and overfitting- related obstacles when modeling moderately sized to large proteins and/or protein families with low sequence coverage. Here, we present a simple to learn, tunable, and accurate generative model, GENERALIST: GENERAtive nonLInear tenSor-factorizaTion for protein sequences. GENERALIST accurately captures several high order summary statistics of amino acid covariation. GENERALIST

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Feinauer, Christoph, Barthelemy Meynard-Piganeau, and Carlo Lucibello. "Interpretable pairwise distillations for generative protein sequence models." PLOS Computational Biology 18, no. 6 (2022): e1010219. http://dx.doi.org/10.1371/journal.pcbi.1010219.

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Many different types of generative models for protein sequences have been proposed in literature. Their uses include the prediction of mutational effects, protein design and the prediction of structural properties. Neural network (NN) architectures have shown great performances, commonly attributed to the capacity to extract non-trivial higher-order interactions from the data. In this work, we analyze two different NN models and assess how close they are to simple pairwise distributions, which have been used in the past for similar problems. We present an approach for extracting pairwise model

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Won, K. J., C. Saunders, and A. Prügel-Bennett. "Evolving Fisher Kernels for Biological Sequence Classification." Evolutionary Computation 21, no. 1 (2013): 83–105. http://dx.doi.org/10.1162/evco_a_00065.

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Fisher kernels have been successfully applied to many problems in bioinformatics. However, their success depends on the quality of the generative model upon which they are built. For Fisher kernel techniques to be used on novel problems, a mechanism for creating accurate generative models is required. A novel framework is presented for automatically creating domain-specific generative models that can be used to produce Fisher kernels for support vector machines (SVMs) and other kernel methods. The framework enables the capture of prior knowledge and addresses the issue of domain-specific kerne

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Liu, Yitian, and Zhouhui Lian. "DeepCalliFont: Few-Shot Chinese Calligraphy Font Synthesis by Integrating Dual-Modality Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3774–82. http://dx.doi.org/10.1609/aaai.v38i4.28168.

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Few-shot font generation, especially for Chinese calligraphy fonts, is a challenging and ongoing problem. With the help of prior knowledge that is mainly based on glyph consistency assumptions, some recently proposed methods can synthesize high-quality Chinese glyph images. However, glyphs in calligraphy font styles often do not meet these assumptions. To address this problem, we propose a novel model, DeepCalliFont, for few-shot Chinese calligraphy font synthesis by integrating dual-modality generative models. Specifically, the proposed model consists of image synthesis and sequence generatio

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Safranchik, Esteban, Shiying Luo, and Stephen Bach. "Weakly Supervised Sequence Tagging from Noisy Rules." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5570–78. http://dx.doi.org/10.1609/aaai.v34i04.6009.

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We propose a framework for training sequence tagging models with weak supervision consisting of multiple heuristic rules of unknown accuracy. In addition to supporting rules that vote on tags in the output sequence, we introduce a new type of weak supervision, called linking rules, that vote on how sequence elements should be grouped into spans with the same tag. These rules are an alternative to candidate span generators that require significantly more human effort. To estimate the accuracies of the rules and combine their conflicting outputs into training data, we introduce a new type of gen

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Polceanu, Mihai, Julie Porteous, Alan Lindsay, and Marc Cavazza. "Narrative Plan Generation with Self-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (2021): 5984–92. http://dx.doi.org/10.1609/aaai.v35i7.16747.

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Narrative Generation has attracted significant interest as a novel application of Automated Planning techniques. However, the vast amount of narrative material available opens the way to the use of Deep Learning techniques. In this paper, we explore the feasibility of narrative generation through self-supervised learning, using sequence embedding techniques or auto-encoders to produce narrative sequences. We use datasets of well-formed plots generated by a narrative planning approach, using pre-existing, published, narrative planning domains, to train generative models. Our experiments demonst

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Zhang, Zhiyuan, and Zhanshan Wang. "Multi-Objective Prediction of Integrated Energy System Using Generative Tractive Network." Mathematics 11, no. 20 (2023): 4350. http://dx.doi.org/10.3390/math11204350.

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Accurate load forecasting can bring economic benefits and scheduling optimization. The complexity and uncertainty arising from the coupling of different energy sources in integrated energy systems pose challenges for simultaneously predicting multiple target load sequences. Existing data-driven methods for load forecasting in integrated energy systems use multi-task learning to address these challenges. When determining the input data for multi-task learning, existing research primarily relies on data correlation analysis and considers the influence of external environmental factors in terms o

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Hawkins-Hooker, Alex, Florence Depardieu, Sebastien Baur, Guillaume Couairon, Arthur Chen, and David Bikard. "Generating functional protein variants with variational autoencoders." PLOS Computational Biology 17, no. 2 (2021): e1008736. http://dx.doi.org/10.1371/journal.pcbi.1008736.

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The vast expansion of protein sequence databases provides an opportunity for new protein design approaches which seek to learn the sequence-function relationship directly from natural sequence variation. Deep generative models trained on protein sequence data have been shown to learn biologically meaningful representations helpful for a variety of downstream tasks, but their potential for direct use in the design of novel proteins remains largely unexplored. Here we show that variational autoencoders trained on a dataset of almost 70000 luciferase-like oxidoreductases can be used to generate n

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Tang, Fangfang, Mengyuan Ren, Xiaofan Li, Zhanglin Lin, and Xiaofeng Yang. "Generating Novel and Soluble Class II Fructose-1,6-Bisphosphate Aldolase with ProteinGAN." Catalysts 13, no. 12 (2023): 1457. http://dx.doi.org/10.3390/catal13121457.

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Fructose-1,6-bisphosphate aldolase (FBA) is an important enzyme involved in central carbon metabolism (CCM) with promising industrial applications. Artificial intelligence models like generative adversarial networks (GANs) can design novel sequences that differ from natural ones. To expand the sequence space of FBA, we applied the generative adversarial network (ProteinGAN) model for the de novo design of FBA in this study. First, we corroborated the viability of the ProteinGAN model through replicating the generation of functional MDH variants. The model was then applied to the design of clas

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Lu, Zhengdong, Todd K. Leen, and Jeffrey Kaye. "Kernels for Longitudinal Data with Variable Sequence Length and Sampling Intervals." Neural Computation 23, no. 9 (2011): 2390–420. http://dx.doi.org/10.1162/neco_a_00164.

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We develop several kernel methods for classification of longitudinal data and apply them to detect cognitive decline in the elderly. We first develop mixed-effects models, a type of hierarchical empirical Bayes generative models, for the time series. After demonstrating their utility in likelihood ratio classifiers (and the improvement over standard regression models for such classifiers), we develop novel Fisher kernels based on mixture of mixed-effects models and use them in support vector machine classifiers. The hierarchical generative model allows us to handle variations in sequence lengt

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Philip, Philemon, and Sidra Minhas. "A Brief Survey on Natural Language Processing Based Text Generation and Evaluation Techniques." VFAST Transactions on Software Engineering 10, no. 3 (2022): 24–36. http://dx.doi.org/10.21015/vtse.v10i3.1104.

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Text Generation is a pressing topic of Natural Language Processing that involves the prediction of upcoming text. Applications like auto-complete, chatbots, auto-correct, and many others use text generation to meet certain communicative requirements. However more accurate text generation methods are needed to encapsulate all possibilities of natural language communication. In this survey, we present cutting-edge methods being adopted for text generation. These methods are divided into three broad categories i.e. 1) Sequence-to-Sequence models (Seq2Seq), 2) Generative Adversarial Networks (GAN)

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Bitard-Feildel, Tristan. "Navigating the amino acid sequence space between functional proteins using a deep learning framework." PeerJ Computer Science 7 (September 17, 2021): e684. http://dx.doi.org/10.7717/peerj-cs.684.

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Motivation Shedding light on the relationships between protein sequences and functions is a challenging task with many implications in protein evolution, diseases understanding, and protein design. The protein sequence space mapping to specific functions is however hard to comprehend due to its complexity. Generative models help to decipher complex systems thanks to their abilities to learn and recreate data specificity. Applied to proteins, they can capture the sequence patterns associated with functions and point out important relationships between sequence positions. By learning these depen

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Ramakers, Julius, Christopher Frederik Blum, Sabrina König, Stefan Harmeling, and Markus Kollmann. "De novo prediction of RNA 3D structures with deep generative models." PLOS ONE 19, no. 2 (2024): e0297105. http://dx.doi.org/10.1371/journal.pone.0297105.

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We present a Deep Learning approach to predict 3D folding structures of RNAs from their nucleic acid sequence. Our approach combines an autoregressive Deep Generative Model, Monte Carlo Tree Search, and a score model to find and rank the most likely folding structures for a given RNA sequence. We show that RNA de novo structure prediction by deep learning is possible at atom resolution, despite the low number of experimentally measured structures that can be used for training. We confirm the predictive power of our approach by achieving competitive results in a retrospective evaluation of the

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Hazra, Debapriya, Mi-Ryung Kim, and Yung-Cheol Byun. "Generative Adversarial Networks for Creating Synthetic Nucleic Acid Sequences of Cat Genome." International Journal of Molecular Sciences 23, no. 7 (2022): 3701. http://dx.doi.org/10.3390/ijms23073701.

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Nucleic acids are the basic units of deoxyribonucleic acid (DNA) sequencing. Every organism demonstrates different DNA sequences with specific nucleotides. It reveals the genetic information carried by a particular DNA segment. Nucleic acid sequencing expresses the evolutionary changes among organisms and revolutionizes disease diagnosis in animals. This paper proposes a generative adversarial networks (GAN) model to create synthetic nucleic acid sequences of the cat genome tuned to exhibit specific desired properties. We obtained the raw sequence data from Illumina next generation sequencing.

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Zhang, Zhaohui, Lijun Yang, Ligong Chen, et al. "A generative adversarial network–based method for generating negative financial samples." International Journal of Distributed Sensor Networks 16, no. 2 (2020): 155014772090705. http://dx.doi.org/10.1177/1550147720907053.

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In financial anti-fraud field, negative samples are small and sparse with serious sample imbalanced problem. Generating negative samples consistent with original data to naturally solve imbalanced problem is a serious problem. This article proposes a new method to solve this problem. We introduce a new generation model, combined Generative Adversarial Network with Long Short-Term Memory network for one-dimensional negative financial samples. The characteristic association between transaction sequences can be learned by long short-term memory layer, and the generator covers real data distributi

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Shen, Xiaojuan, Tao Zeng, Nianhang Chen, Jiabo Li, and Ruibo Wu. "NIMO: A Natural Product-Inspired Molecular Generative Model Based on Conditional Transformer." Molecules 29, no. 8 (2024): 1867. http://dx.doi.org/10.3390/molecules29081867.

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Natural products (NPs) have diverse biological activity and significant medicinal value. The structural diversity of NPs is the mainstay of drug discovery. Expanding the chemical space of NPs is an urgent need. Inspired by the concept of fragment-assembled pseudo-natural products, we developed a computational tool called NIMO, which is based on the transformer neural network model. NIMO employs two tailor-made motif extraction methods to map a molecular graph into a semantic motif sequence. All these generated motif sequences are used to train our molecular generative models. Various NIMO mode

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Xuan, Bona, Jin Li, and Yafei Song. "SFCWGAN-BiTCN with Sequential Features for Malware Detection." Applied Sciences 13, no. 4 (2023): 2079. http://dx.doi.org/10.3390/app13042079.

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In the field of adversarial attacks, the generative adversarial network (GAN) has shown better performance. There have been few studies applying it to malware sample supplementation, due to the complexity of handling discrete data. More importantly, unbalanced malware family samples interfere with the analytical power of malware detection models and mislead malware classification. To address the problem of the impact of malware family imbalance on accuracy, a selection feature conditional Wasserstein generative adversarial network (SFCWGAN) and bidirectional temporal convolutional network (BiT

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Truong, Thanh-Dat, Chi Nhan Duong, Minh-Triet Tran, Ngan Le, and Khoa Luu. "Fast Flow Reconstruction via Robust Invertible n × n Convolution." Future Internet 13, no. 7 (2021): 179. http://dx.doi.org/10.3390/fi13070179.

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Flow-based generative models have recently become one of the most efficient approaches to model data generation. Indeed, they are constructed with a sequence of invertible and tractable transformations. Glow first introduced a simple type of generative flow using an invertible 1×1 convolution. However, the 1×1 convolution suffers from limited flexibility compared to the standard convolutions. In this paper, we propose a novel invertible n×n convolution approach that overcomes the limitations of the invertible 1×1 convolution. In addition, our proposed network is not only tractable and invertib

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You, Yuyang, Xiaoyu Guo, Xuyang Zhong, and Zhihong Yang. "A Few-Shot Learning-Based EEG and Stage Transition Sequence Generator for Improving Sleep Staging Performance." Biomedicines 10, no. 12 (2022): 3006. http://dx.doi.org/10.3390/biomedicines10123006.

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In this study, generative adversarial networks named SleepGAN are proposed to expand the training set for automatic sleep stage classification tasks by generating both electroencephalogram (EEG) epochs and sequence relationships of sleep stages. In order to reach high accuracy, most existing classification methods require substantial amounts of training data, but obtaining such quantities of real EEG epochs is expensive and time-consuming. We introduce few-shot learning, which is a method of training a GAN using a very small set of training data. This paper presents progressive Wasserstein div

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Wang, Zhenyi, Ping Yu, Yang Zhao, et al. "Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12281–88. http://dx.doi.org/10.1609/aaai.v34i07.6911.

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Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN to directly model transitions in the original action space. Due to high dimensionality and potential noise, such modeling of action transitions is particularly challenging. In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality. Conditioned on a latent sequence, actions are

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Kim, Ha Young, and Dongsup Kim. "Prediction of mutation effects using a deep temporal convolutional network." Bioinformatics 36, no. 7 (2019): 2047–52. http://dx.doi.org/10.1093/bioinformatics/btz873.

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Abstract Motivation Accurate prediction of the effects of genetic variation is a major goal in biological research. Towards this goal, numerous machine learning models have been developed to learn information from evolutionary sequence data. The most effective method so far is a deep generative model based on the variational autoencoder (VAE) that models the distributions using a latent variable. In this study, we propose a deep autoregressive generative model named mutationTCN, which employs dilated causal convolutions and attention mechanism for the modeling of inter-residue correlations in

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Härkönen, Erik, Miika Aittala, Tuomas Kynkäänniemi, Samuli Laine, Timo Aila, and Jaakko Lehtinen. "Disentangling random and cyclic effects in time-lapse sequences." ACM Transactions on Graphics 41, no. 4 (2022): 1–13. http://dx.doi.org/10.1145/3528223.3530170.

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Time-lapse image sequences offer visually compelling insights into dynamic processes that are too slow to observe in real time. However, playing a long time-lapse sequence back as a video often results in distracting flicker due to random effects, such as weather, as well as cyclic effects, such as the day-night cycle. We introduce the problem of disentangling time-lapse sequences in a way that allows separate, after-the-fact control of overall trends, cyclic effects, and random effects in the images, and describe a technique based on data-driven generative models that achieves this goal. This

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Wilburn, Grey W., and Sean R. Eddy. "Remote homology search with hidden Potts models." PLOS Computational Biology 16, no. 11 (2020): e1008085. http://dx.doi.org/10.1371/journal.pcbi.1008085.

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Most methods for biological sequence homology search and alignment work with primary sequence alone, neglecting higher-order correlations. Recently, statistical physics models called Potts models have been used to infer all-by-all pairwise correlations between sites in deep multiple sequence alignments, and these pairwise couplings have improved 3D structure predictions. Here we extend the use of Potts models from structure prediction to sequence alignment and homology search by developing what we call a hidden Potts model (HPM) that merges a Potts emission process to a generative probability

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Hu, Yijia. "Performance exploration of Generative Pre-trained Transformer-2 for lyrics generation." Applied and Computational Engineering 48, no. 1 (2024): 53–60. http://dx.doi.org/10.54254/2755-2721/48/20241154.

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In recent years, the field of Natural Language Processing (NLP) has undergone a revolution, with text generation playing a key role in this transformation. This shift is not limited to technological areas but has also seamlessly penetrated creative domains, with a prime example being the generation of song lyrics. To be truly effective, generative models, like Generative Pre-trained Transformer (GPT)-2, require fine-tuning as a crucial step. This paper, utilizing the robustness of the widely-referenced Kaggle dataset titled "Song Lyrics", carefully explores the impacts of modulating three key

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Nguyen, Viet, Giang Vu, Tung Nguyen Thanh, Khoat Than, and Toan Tran. "On Inference Stability for Diffusion Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14449–56. http://dx.doi.org/10.1609/aaai.v38i13.29359.

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Denoising Probabilistic Models (DPMs) represent an emerging domain of generative models that excel in generating diverse and high-quality images. However, most current training methods for DPMs often neglect the correlation between timesteps, limiting the model's performance in generating images effectively. Notably, we theoretically point out that this issue can be caused by the cumulative estimation gap between the predicted and the actual trajectory. To minimize that gap, we propose a novel sequence-aware loss that aims to reduce the estimation gap to enhance the sampling quality. Furthermo

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Li, Shuyu, and Yunsick Sung. "Transformer-Based Seq2Seq Model for Chord Progression Generation." Mathematics 11, no. 5 (2023): 1111. http://dx.doi.org/10.3390/math11051111.

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Machine learning is widely used in various practical applications with deep learning models demonstrating advantages in handling huge data. Treating music as a special language and using deep learning models to accomplish melody recognition, music generation, and music analysis has proven feasible. In certain music-related deep learning research, recurrent neural networks have been replaced with transformers. This has achieved significant results. In traditional approaches with recurrent neural networks, input sequences are limited in length. This paper proposes a method to generate chord prog

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ZAKI, NAZAR M., SAFAAI DERIS, and ROSLI M. ILLIAS. "FEATURES EXTRACTION FOR PROTEIN HOMOLOGY DETECTION USING HIDDEN MARKOV MODELS COMBINING SCORES." International Journal of Computational Intelligence and Applications 04, no. 01 (2004): 1–12. http://dx.doi.org/10.1142/s1469026804001161.

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Few years back, Jaakkola and Haussler published a method of combining generative and discriminative approaches for detecting protein homologies. The method was a variant of support vector machines using a new kernel function called Fisher Kernel. They begin by training a generative hidden Markov model for a protein family. Then, using the model, they derive a vector of features called Fisher scores that are assigned to the sequence and then use support vector machine in conjunction with the fisher scores for protein homologies detection. In this paper, we revisit the idea of using a discrimina

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Isacchini, Giulio, Aleksandra M. Walczak, Thierry Mora, and Armita Nourmohammad. "Deep generative selection models of T and B cell receptor repertoires with soNNia." Proceedings of the National Academy of Sciences 118, no. 14 (2021): e2023141118. http://dx.doi.org/10.1073/pnas.2023141118.

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Subclasses of lymphocytes carry different functional roles to work together and produce an immune response and lasting immunity. Additionally to these functional roles, T and B cell lymphocytes rely on the diversity of their receptor chains to recognize different pathogens. The lymphocyte subclasses emerge from common ancestors generated with the same diversity of receptors during selection processes. Here, we leverage biophysical models of receptor generation with machine learning models of selection to identify specific sequence features characteristic of functional lymphocyte repertoires an

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Wang, Chuantao, Xuexin Yang, and Linkai Ding. "Imbalanced sentiment classification based on sequence generative adversarial nets." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7909–19. http://dx.doi.org/10.3233/jifs-201370.

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The purpose of sentiment classification is to solve the problem of automatic judgment of sentiment tendency. In the sentiment classification task of text data (such as online reviews), the traditional deep learning model focuses on algorithm optimization, but ignores the characteristics of the imbalanced distribution of the number of samples in each classification, which will cause the classification performance of the model to decrease in practical applications. In this paper, the experiment is divided into two stages. In the first stage, samples of minority class in the sample distribution a

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Stokes, James, and John Terilla. "Probabilistic Modeling with Matrix Product States." Entropy 21, no. 12 (2019): 1236. http://dx.doi.org/10.3390/e21121236.

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Inspired by the possibility that generative models based on quantum circuits can provide a useful inductive bias for sequence modeling tasks, we propose an efficient training algorithm for a subset of classically simulable quantum circuit models. The gradient-free algorithm, presented as a sequence of exactly solvable effective models, is a modification of the density matrix renormalization group procedure adapted for learning a probability distribution. The conclusion that circuit-based models offer a useful inductive bias for classical datasets is supported by experimental results on the par

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Wang, Xun, Changnan Gao, Peifu Han, et al. "PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning." International Journal of Molecular Sciences 24, no. 2 (2023): 1146. http://dx.doi.org/10.3390/ijms24021146.

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Recent years have seen tremendous success in the design of novel drug molecules through deep generative models. Nevertheless, existing methods only generate drug-like molecules, which require additional structural optimization to be developed into actual drugs. In this study, a deep learning method for generating target-specific ligands was proposed. This method is useful when the dataset for target-specific ligands is limited. Deep learning methods can extract and learn features (representations) in a data-driven way with little or no human participation. Generative pretraining (GPT) was used

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Wu, Shaohan, Jingfeng Xue, Yong Wang, and Zixiao Kong. "Black-Box Evasion Attack Method Based on Confidence Score of Benign Samples." Electronics 12, no. 11 (2023): 2346. http://dx.doi.org/10.3390/electronics12112346.

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Recently, malware detection models based on deep learning have gradually replaced manual analysis as the first line of defense for anti-malware systems. However, it has been shown that these models are vulnerable to a specific class of inputs called adversarial examples. It is possible to evade the detection model by adding some carefully crafted tiny perturbations to the malicious samples without changing the sample functions. Most of the adversarial example generation methods ignore the information contained in the detection results of benign samples from detection models. Our method extract

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Russ, William P., Matteo Figliuzzi, Christian Stocker, et al. "An evolution-based model for designing chorismate mutase enzymes." Science 369, no. 6502 (2020): 440–45. http://dx.doi.org/10.1126/science.aba3304.

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The rational design of enzymes is an important goal for both fundamental and practical reasons. Here, we describe a process to learn the constraints for specifying proteins purely from evolutionary sequence data, design and build libraries of synthetic genes, and test them for activity in vivo using a quantitative complementation assay. For chorismate mutase, a key enzyme in the biosynthesis of aromatic amino acids, we demonstrate the design of natural-like catalytic function with substantial sequence diversity. Further optimization focuses the generative model toward function in a specific ge

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Chen, Yunjie, Marius Staring, Olaf M. Neve, et al. "CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation." Machine Learning for Biomedical Imaging 2, Generative Models (2024): 657–85. http://dx.doi.org/10.59275/j.melba.2024-d61g.

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Multi-sequence magnetic resonance imaging (MRI) has found wide applications in both modern clinical studies and deep learning research. However, in clinical practice, it frequently occurs that one or more of the MRI sequences are missing due to different image acquisition protocols or contrast agent contraindications of patients, limiting the utilization of deep learning models trained on multi-sequence data. One promising approach is to leverage generative models to synthesize the missing sequences, which can serve as a surrogate acquisition. State-of-the-art methods tackling this problem are

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Qi, Jingyuan, Minqian Liu, Ying Shen, Zhiyang Xu, and Lifu Huang. "MULTISCRIPT: Multimodal Script Learning for Supporting Open Domain Everyday Tasks." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 18888–96. http://dx.doi.org/10.1609/aaai.v38i17.29854.

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Automatically generating scripts (i.e. sequences of key steps described in text) from video demonstrations and reasoning about the subsequent steps are crucial to the modern AI virtual assistants to guide humans to complete everyday tasks, especially unfamiliar ones. However, current methods for generative script learning rely heavily on well-structured preceding steps described in text and/or images or are limited to a certain domain, resulting in a disparity with real-world user scenarios. To address these limitations, we present a new benchmark challenge – MULTISCRIPT, with two new tasks on

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Liu, Danyang, Juntao Li, Meng-Hsuan Yu, et al. "A Character-Centric Neural Model for Automated Story Generation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 1725–32. http://dx.doi.org/10.1609/aaai.v34i02.5536.

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Automated story generation is a challenging task which aims to automatically generate convincing stories composed of successive plots correlated with consistent characters. Most recent generation models are built upon advanced neural networks, e.g., variational autoencoder, generative adversarial network, convolutional sequence to sequence model. Although these models have achieved prompting results on learning linguistic patterns, very few methods consider the attributes and prior knowledge of the story genre, especially from the perspectives of explainability and consistency. To fill this ga

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Tubiana, Jérôme, Lucia Adriana-Lifshits, Michael Nissan, et al. "Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions." PLOS Computational Biology 19, no. 2 (2023): e1010874. http://dx.doi.org/10.1371/journal.pcbi.1010874.

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Design of peptide binders is an attractive strategy for targeting “undruggable” protein-protein interfaces. Current design protocols rely on the extraction of an initial sequence from one known protein interactor of the target protein, followed by in-silico or in-vitro mutagenesis-based optimization of its binding affinity. Wet lab protocols can explore only a minor portion of the vast sequence space and cannot efficiently screen for other desirable properties such as high specificity and low toxicity, while in-silico design requires intensive computational resources and often relies on simpli

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Shen, Kevin. "Multi-world Model in Continual Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23757–59. http://dx.doi.org/10.1609/aaai.v38i21.30555.

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World Models are made of generative networks that can predict future states of a single environment which it was trained on. This research proposes a Multi-world Model, a foundational model built from World Models for the field of continual reinforcement learning that is trained on many different environments, enabling it to generalize state sequence predictions even for unseen settings.

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Vaškevičius, Mantas, Jurgita Kapočiūtė-Dzikienė, and Liudas Šlepikas. "Generative LLMs in Organic Chemistry: Transforming Esterification Reactions into Natural Language Procedures." Applied Sciences 13, no. 24 (2023): 13140. http://dx.doi.org/10.3390/app132413140.

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This paper presents a novel approach to predicting esterification procedures in organic chemistry by employing generative large language models (LLMs) to interpret and translate SMILES molecular notation into detailed procedural texts of synthesis reactions. Esterification reaction is important in producing various industrial intermediates, fragrances, and flavors. Recognizing the challenges of accurate prediction in complex chemical landscapes, we have compiled and made publicly available a curated dataset of esterification reactions to enhance research collaboration. We systematically compar

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Marocco, Paolo, and Roberto Gigliucci. "An Investigation about Entailment and Narrative by AI Techniques (Generative Models)." Communication, Society and Media 3, no. 4 (2020): p61. http://dx.doi.org/10.22158/csm.v3n4p61.

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Many storytelling generation problems concern the difficulty to model the sequence of sentences. Language models are generally able to assign high scores to well-formed text, especially in the cases of short texts, failing when they try to simulate human textual inference. Although in some cases output text automatically generated sounds as bland, incoherent, repetitive and unrelated to the context, in other cases the process reveals capability to surprise the reader, avoiding to be boring/predictable, even if the generated text satisfies entailment task requirements. The lyric tradition often

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Lee, Sangho, Hayun Lee, and Dongkun Shin. "Proxyformer: Nyström-Based Linear Transformer with Trainable Proxy Tokens." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13418–26. http://dx.doi.org/10.1609/aaai.v38i12.29244.

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Transformer-based models have demonstrated remarkable performance in various domains, including natural language processing, image processing and generative modeling. The most significant contributor to the successful performance of Transformer models is the self-attention mechanism, which allows for a comprehensive understanding of the interactions between tokens in the input sequence. However, there is a well-known scalability issue, the quadratic dependency (i.e. O(n^2)) of self-attention operations on the input sequence length n, making the handling of lengthy sequences challenging. To add

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LEE, CHAN-SU, and DIMITRIS SAMARAS. "ANALYSIS AND CONTROL OF FACIAL EXPRESSIONS USING DECOMPOSABLE NONLINEAR GENERATIVE MODELS." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 05 (2014): 1456009. http://dx.doi.org/10.1142/s0218001414560096.

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Facial expressions convey personal characteristics and subtle emotional states. This paper presents a new framework for modeling subtle facial motions of different people with different types of expressions from high-resolution facial expression tracking data to synthesize new stylized subtle facial expressions. A conceptual facial motion manifold is used for a unified representation of facial motion dynamics from three-dimensional (3D) high-resolution facial motions as well as from two-dimensional (2D) low-resolution facial motions. Variant subtle facial motions in different people with diffe

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Liu, Zuozhu, Thiparat Chotibut, Christopher Hillar, and Shaowei Lin. "Biologically Plausible Sequence Learning with Spiking Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 1316–23. http://dx.doi.org/10.1609/aaai.v34i02.5487.

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Motivated by the celebrated discrete-time model of nervous activity outlined by McCulloch and Pitts in 1943, we propose a novel continuous-time model, the McCulloch-Pitts network (MPN), for sequence learning in spiking neural networks. Our model has a local learning rule, such that the synaptic weight updates depend only on the information directly accessible by the synapse. By exploiting asymmetry in the connections between binary neurons, we show that MPN can be trained to robustly memorize multiple spatiotemporal patterns of binary vectors, generalizing the ability of the symmetric Hopfield

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Zhou, Kun, Wenyong Wang, Teng Hu, and Kai Deng. "Time Series Forecasting and Classification Models Based on Recurrent with Attention Mechanism and Generative Adversarial Networks." Sensors 20, no. 24 (2020): 7211. http://dx.doi.org/10.3390/s20247211.

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Time series classification and forecasting have long been studied with the traditional statistical methods. Recently, deep learning achieved remarkable successes in areas such as image, text, video, audio processing, etc. However, research studies conducted with deep neural networks in these fields are not abundant. Therefore, in this paper, we aim to propose and evaluate several state-of-the-art neural network models in these fields. We first review the basics of representative models, namely long short-term memory and its variants, the temporal convolutional network and the generative advers

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Wang, Yi. "Intelligent auxiliary system for music performance under edge computing and long short-term recurrent neural networks." PLOS ONE 18, no. 5 (2023): e0285496. http://dx.doi.org/10.1371/journal.pone.0285496.

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Music performance action generation can be applied in multiple real-world scenarios as a research hotspot in computer vision and cross-sequence analysis. However, the current generation methods of music performance actions have consistently ignored the connection between music and performance actions, resulting in a strong sense of separation between visual and auditory content. This paper first analyzes the attention mechanism, Recurrent Neural Network (RNN), and long and short-term RNN. The long and short-term RNN is suitable for sequence data with a strong temporal correlation. Based on thi

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Li, Longyuan, Jihai Zhang, Junchi Yan, et al. "Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 8420–28. http://dx.doi.org/10.1609/aaai.v35i10.17023.

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Time-series is ubiquitous across applications, such as transportation, finance and healthcare. Time-series is often influenced by external factors, especially in the form of asynchronous events, making forecasting difficult. However, existing models are mainly designated for either synchronous time-series or asynchronous event sequence, and can hardly provide a synthetic way to capture the relation between them. We propose Variational Synergetic Multi-Horizon Network (VSMHN), a novel deep conditional generative model. To learn complex correlations across heterogeneous sequences, a tailored enc

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Murad, Taslim, Sarwan Ali, and Murray Patterson. "Exploring the Potential of GANs in Biological Sequence Analysis." Biology 12, no. 6 (2023): 854. http://dx.doi.org/10.3390/biology12060854.

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Biological sequence analysis is an essential step toward building a deeper understanding of the underlying functions, structures, and behaviors of the sequences. It can help in identifying the characteristics of the associated organisms, such as viruses, etc., and building prevention mechanisms to eradicate their spread and impact, as viruses are known to cause epidemics that can become global pandemics. New tools for biological sequence analysis are provided by machine learning (ML) technologies to effectively analyze the functions and structures of the sequences. However, these ML-based meth

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Trinquier, Jeanne, Guido Uguzzoni, Andrea Pagnani, Francesco Zamponi, and Martin Weigt. "Efficient generative modeling of protein sequences using simple autoregressive models." Nature Communications 12, no. 1 (2021). http://dx.doi.org/10.1038/s41467-021-25756-4.

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AbstractGenerative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly accurate but computationally efficient generative sequence models. We show that they perform similarly to existing approaches based on Boltzmann machines or deep generative models, but at a substantially lower computational cost (by a factor between 102 and 103). Furthermore, the simple structu

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