Relevant bibliographies by topics / Inference training

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Inference training'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Inference training"

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Melgarejo, Teófilo Félix Valentín, Pablo Lenin La Madrid Vivar, Clodoaldo Ramos Pando, Pablo Lolo Valentín Melgarejo, and Agustín Arturo Aguirre Adauto. "Inference and reading comprehension in university students." Nurture 18, no. 4 (2024): 785–94. http://dx.doi.org/10.55951/nurture.v18i4.846.

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Purpose: The objective of this research was to determine the relationship between inference and reading comprehension. We sought to verify the relationship between inductive and deductive inferences in the comprehensive reading of Daniel Alcides Carrión National University Peru students majoring in communication and literature. Design/Methodology/Approach: The correlational-explanatory research design was used since the correlation of the study variables was sought through scientific and specifically analytical, deductive and interpretive methodology on a population of 104 and the probabilisti
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Liu, Yu, Anurag Andhare, and Kyoung-Don Kang. "Corun: Concurrent Inference and Continuous Training at the Edge for Cost-Efficient AI-Based Mobile Image Sensing." Sensors 24, no. 16 (2024): 5262. http://dx.doi.org/10.3390/s24165262.

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Intelligent mobile image sensing powered by deep learning analyzes images captured by cameras from mobile devices, such as smartphones or smartwatches. It supports numerous mobile applications, such as image classification, face recognition, and camera scene detection. Unfortunately, mobile devices often lack the resources necessary for deep learning, leading to increased inference latency and rapid battery consumption. Moreover, the inference accuracy may decline over time due to potential data drift. To address these issues, we introduce a new cost-efficient framework, called Corun, designed
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Yu, Kai, and Mark J. F. Gales. "Bayesian Adaptive Inference and Adaptive Training." IEEE Transactions on Audio, Speech and Language Processing 15, no. 6 (2007): 1932–43. http://dx.doi.org/10.1109/tasl.2007.901300.

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MIH, Viorel, and Codruța MIH. "Text-Based Inference Instruction for Elementary Grade Children with Reading Comprehension Difficulties: An Intervention Research." Studia Universitatis Babeș-Bolyai Psychologia-Paedagogia 69, no. 1 (2024): 257–72. http://dx.doi.org/10.24193/subbpsyped.2024.1.13.

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"The current study investigated the effects of inference instruction on text-based inferences by third graders who are below average in reading comprehension but average in reading fluency and cognitive abilities. Text-based inferences occur when the preceding text has an identifiable causal antecedent. Participants were randomly assigned and attended twelve 30-minute sessions of the inferences training intervention. We have included strategies for integrating information from the text to improve reading comprehension skills. We provide an overview of how specific text-based instruction influe
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Zhao, Yanchao, Jiale Chen, Jiale Zhang, et al. "User-Level Membership Inference for Federated Learning in Wireless Network Environment." Wireless Communications and Mobile Computing 2021 (October 19, 2021): 1–17. http://dx.doi.org/10.1155/2021/5534270.

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With the rise of privacy concerns in traditional centralized machine learning services, federated learning, which incorporates multiple participants to train a global model across their localized training data, has lately received significant attention in both industry and academia. Bringing federated learning into a wireless network scenario is a great move. The combination of them inspires tremendous power and spawns a number of promising applications. Recent researches reveal the inherent vulnerabilities of the various learning modes for the membership inference attacks that the adversary c
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Oyekan, Basirat. "DEVELOPING PRIVACY-PRESERVING FEDERATED LEARNING MODELS FOR COLLABORATIVE HEALTH DATA ANALYSIS ACROSS MULTIPLE INSTITUTIONS WITHOUT COMPROMISING DATA SECURITY." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 3, no. 3 (2024): 139–64. http://dx.doi.org/10.60087/jklst.vol3.n3.p139-164.

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Federated learning is an emerging distributed machine learning technique that enables collaborative training of models among devices and servers without exchanging private data. However, several privacy and security risks associated with federated learning need to be addressed for safe adoption. This review provides a comprehensive analysis of the key threats in federated learning and the mitigation strategies used to overcome these threats. Some of the major threats identified include model inversion, membership inference, data attribute inference and model extraction attacks. Model inversion
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Vishakha, Agrawal. "Demystifying Deep Learning Compiler Optimizations for Training and Inference." Journal of Advances in Developmental Research 12, no. 2 (2021): 1–9. https://doi.org/10.5281/zenodo.14551855.

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Deep learning has achieved tremendous success in recent years, powering many artificial intelligence applications. However, deep learning models are computationally intensive to train, requiring massive amounts of data and compute resources. Once trained, deep learning models need to be deployed for inference to make predictions on new data. Hardware used for training differs from hardware used for inference. Deep learning compilers have revolutionized the field of artificial intelligence by optimizing the performance of deep learning models on various hardware platforms. In the current landsc
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Suri, Anshuman, and David Evans. "Formalizing and Estimating Distribution Inference Risks." Proceedings on Privacy Enhancing Technologies 2022, no. 4 (2022): 528–51. http://dx.doi.org/10.56553/popets-2022-0121.

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Distribution inference, sometimes called property inference, infers statistical properties about a training set from access to a model trained on that data. Distribution inference attacks can pose serious risks when models are trained on private data, but are difficult to distinguish from the intrinsic purpose of statistical machine learning—namely, to produce models that capture statistical properties about a distribution. Motivated by Yeom et al.’s membership inference framework, we propose a formal definition of distribution inference attacks general enough to describe a broad class of atta
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Shafique, Muhammad Ali, Arslan Munir, and Joonho Kong. "Deep Learning Performance Characterization on GPUs for Various Quantization Frameworks." AI 4, no. 4 (2023): 926–48. http://dx.doi.org/10.3390/ai4040047.

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Deep learning is employed in many applications, such as computer vision, natural language processing, robotics, and recommender systems. Large and complex neural networks lead to high accuracy; however, they adversely affect many aspects of deep learning performance, such as training time, latency, throughput, energy consumption, and memory usage in the training and inference stages. To solve these challenges, various optimization techniques and frameworks have been developed for the efficient performance of deep learning models in the training and inference stages. Although optimization techn
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Yang, Chao-Han Huck, I.-Te Danny Hung, Yi Ouyang, and Pin-Yu Chen. "Training a Resilient Q-network against Observational Interference." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8814–22. http://dx.doi.org/10.1609/aaai.v36i8.20862.

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Deep reinforcement learning (DRL) has demonstrated impressive performance in various gaming simulators and real-world applications. In practice, however, a DRL agent may receive faulty observation by abrupt interferences such as black-out, frozen-screen, and adversarial perturbation. How to design a resilient DRL algorithm against these rare but mission-critical and safety-crucial scenarios is an essential yet challenging task. In this paper, we consider a deep q-network (DQN) framework training with an auxiliary task of observational interferences such as artificial noises. Inspired by causal
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Dissertations / Theses on the topic "Inference training"

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Thomas, Owen Matthew Truscott. "Scalable inference and private co-training for Gaussian processes." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:f7282b97-431b-466d-b7a5-1b55e05dc250.

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Two principal problems are pursued in this thesis: that of scaling inference for Gaussian process regression to very large numbers of data points, and that of differentially private co-training between multiple Gaussian processes with distinct private views of the data. The first chapter acts as an introduction to Bayesian nonparametric regression and standard techniques for performing scalable inference and differentially private communication with Gaussian Processes. The second chapter explores the use of Tucker decomposition and Kronecker structure of variational distributions in order to u
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Raut, Chandra Kant. "Discriminative adaptive training and Bayesian inference for speech recognition." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608866.

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Moldoveanu, Matei. "In-Network Learning : Distributed Training and Inference in Communication Networks." Electronic Thesis or Diss., Université Gustave Eiffel, 2023. http://www.theses.fr/2023UEFL2003.

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Nous étudions le problème de l'apprentissage statistique distribué. Plus précisément, nous considérons le cadre d'apprentissage distribué et d'inférence. Dans le scénario considéré, plusieurs nœuds doivent travailler ensemble pour prédire une certaine quantité d'intérêt/d'événement au niveau d'un nœud central. Certains nœuds observent les données liées à la quantité cible, tandis que d'autres ne le font pas ; cependant, les nœuds qui n'ont pas accès aux données forment un lien entre le nœud central et les nœuds qui observent les données. Nous considérons le cas où un graphe orienté acyclique p
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Arun, Abhishek. "Probabilistic inference for phrase-based machine translation : a sampling approach." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/4815.

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Recent advances in statistical machine translation (SMT) have used dynamic programming (DP) based beam search methods for approximate inference within probabilistic translation models. Despite their success, these methods compromise the probabilistic interpretation of the underlying model thus limiting the application of probabilistically defined decision rules during training and decoding. As an alternative, in this thesis, we propose a novel Monte Carlo sampling approach for theoretically sound approximate probabilistic inference within these models. The distribution we are interested in is
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Coy, Christopher G. "A Hybrid-Genetic Algorithm for Training a Sugeno-Type Fuzzy Inference System with a Mutable Rule Base." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1289243615.

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Scotese, Kyle C. "A Diatom Phosphorus Inference Model for 30 Freshwater Lakes in NE Ohio and NW Pennsylvania." Cleveland State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=csu1231522511.

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Mladenovic, Jelena. "Computational Modeling of User States and Skills for Optimizing BCI Training Tasks." Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0131.

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Les Interfaces Cerveaux-Ordinateur (ICO) sont des systèmes qui permettent de manipuler une machine avec sa seule activité cérébrale. Elles sont utilisées pour accomplir des objectifs variés, par exemple afin qu’un amputé puisse manipuler un bras robotique, pour une réhabilitation neuronale en cas d’accident vasculaire cérébral, dans un cadre ludique pour jouer à des jeux vidéo, etc. Une ICO comprend l’acquisition du signal cérébral (le plus souvent par électroencéphalographie, EEG), le décodage et l’interprétation de ce signal, et enfin la production d’un retour sensoriel à l’utilisateur. Ce r
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Макогон, Роман Олександрович. "Прогноз курсу криптовалюти Bitcoin на основі мереж Байєса". Master's thesis, КПІ ім. Ігоря Сікорського, 2019. https://ela.kpi.ua/handle/123456789/32006.

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У світі працюють ефективні рішення побудовані на базі Блокчейн, такі як наприклад Біткоін - інноваційна мережа платежів та цифрова валюта. З цією технологією тісно пов’язані великі дані. Одна з галузей, що зараз активно накопичує такі дані – біржі обміну криптовалют – Binance, EXMO, Bittrex, та інші. Наразі існує потреба в інструментах, які дозволяють аналізувати такі дані, щоб отримувати корисну інформацію, придатну для комерційного застосування. Мережі Байєса є ефективним інструментом для вирішення цієї задачі. Метою дослідження є створення системи підтримки прийняття рішень (СППР), що приз
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Lundstrom, Joel Thomas. "A new use of frame-of-reference training : improving reviewers' inferences from biodata information." Diss., Manhattan, Kan. : Kansas State University, 2007. http://hdl.handle.net/2097/444.

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Mardock, Michelle Anne. "Muscular Strength Training Modifies Regulation of Bone Remodeling: Inferences From Serum Biomarkers in Young Women." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/34631.

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Biochemical markers of bone turnover allow inference of the events occurring at the bone tissue level and may detect changes in bone cell activity earlier than densitometric technologies. Serum concentrations of receptor activator for nuclear factor kappa-beta ligand (RANKL), osteoprotegerin (OPG), osteocalcin, and N-telopeptide (NTx) were measured in women aged 20 + 1.5 years (mean + SD) who underwent 32 weeks of unilateral isokinetic concentric or eccentric muscular strength training. Changes in serum biomarkers were compared with changes in arm and leg flexor and extensor muscle strength.
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Books on the topic "Inference training"

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Esbensen, Kim. Multivariate Analysis in Practice: A training package. Camo AS, 1994.

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Bobyr', Maksim, Sergey Emel'yanov, Aleksandr Arhipov, Natal'ya Milostnaya, Andrey Ronzhin, and Roman Mescheryakov. Applied neuro-fuzzy computing systems and devices. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1900641.

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The monograph is devoted to the analysis and development of applied neuro-fuzzy systems and devices. The issues related to the training of neuro-fuzzy inference systems are outlined. There are many examples and algorithms that explain the essence of the functioning of the developed methods.
 It is intended for students, postgraduates, researchers, engineers engaged in the development of intelligent systems and devices for controlling mechanisms.
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Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.

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Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. 
 The textbook in the field of training "Computer Science and Computer Engineering"
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Varlamov, Oleg. Mivar databases and rules. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in th
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Multivariate Analysis in Practice: A training package. Camo AS, 1996.

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Multivariate Analysis in Practice: A training package. Camo ASA, 1998.

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Anderson, Raymond A. Credit Intelligence & Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.001.0001.

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This book, “Forest Paths” for short, started as a detailed guide for the construction of predictive models for credit and other risk assessment, for use in big-bank retail lending. It became a textbook covering credit processes (from marketing through to fraud), bureau and rating agencies, and various tools. Included are detailed histories (economics, statistics, social science}, which much referencing. It is unique in the field, with chatpers’-end questions. The primary target market is corporate and academic, but much would be of interest to a broader audience. There are eight modules: 1) an
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Väyrynen, Pekka. Doubts about Moral Perception. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786054.003.0006.

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This chapter defends doubts about the existence of genuine moral perception, understood as the claim that at least some moral properties figure in the contents of perceptual experience. The doubts are local: even if perceptual experiences generally can be cognitively penetrable and rich, standard examples of moral perception are better explained as habitual implicit inferences or transitions in thought. The chapter sketches a model on which the relevant transitions in thought can be psychologically immediate depending on how readily and reliably non-evaluative perceptual inputs, jointly with t
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Hankin, David, Michael S. Mohr, and Kenneth B. Newman. Sampling Theory. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198815792.001.0001.

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We present a rigorous but understandable introduction to the field of sampling theory for ecologists and natural resource scientists. Sampling theory concerns itself with development of procedures for random selection of a subset of units, a sample, from a larger finite population, and with how to best use sample data to make scientifically and statistically sound inferences about the population as a whole. The inferences fall into two broad categories: (a) estimation of simple descriptive population parameters, such as means, totals, or proportions, for variables of interest, and (b) estimati
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Book chapters on the topic "Inference training"

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Dua, Ishneet Kaur, and Parth Girish Patel. "Model Training and Inference Optimization." In Optimizing Generative AI Workloads for Sustainability. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0917-0_6.

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Gafni, Yotam, Ronen Gradwohl, and Moshe Tennenholtz. "Prediction-Sharing During Training and Inference." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71033-9_24.

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Wright, J. H., G. J. F. Jones, and H. Lloyd-Thomas. "Training and application of integrated grammar/bigram language models." In Grammatical Inference and Applications. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58473-0_153.

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Ayabe, Hiroaki, Emmanuel Manalo, and Noriko Hanaki. "Elucidating the Effects of Diagram Use Training for Math Word Problem Solving." In Diagrammatic Representation and Inference. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54249-8_54.

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Yan, Ran, Ruiying Du, Kun He, and Jing Chen. "Efficient Adversarial Training with Membership Inference Resistance." In Pattern Recognition and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8429-9_38.

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Suwa, Masaki. "A Training Program to be Perceptually Sensitive and Conceptually Productive through Meta-cognition: A Case Study." In Diagrammatic Representation and Inference. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25931-2_40.

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Bagos, Pantelis G., Theodore D. Liakopoulos, and Stavros J. Hamodrakas. "Faster Gradient Descent Training of Hidden Markov Models, Using Individual Learning Rate Adaptation." In Grammatical Inference: Algorithms and Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30195-0_5.

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Monreale, Anna, Francesca Naretto, and Simone Rizzo. "Agnostic Label-Only Membership Inference Attack." In Network and System Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39828-5_14.

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AbstractIn recent years we are witnessing the diffusion of AI systems based on powerful Machine Learning models which find application in many critical contexts such as medicine and financial market. In such contexts, it is important to design Trustworthy AI systems while guaranteeing privacy protection. However, some attacks on the privacy of Machine Learning models have been designed to show the threats of exposing such models. Membership Inference is one of the simplest privacy threats faced by Machine Learning models. It is based on the assumption that an adversary, observing the confidenc
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García, Pedro, José Ruiz, Antonio Cano, and Gloria Alvarez. "Inference Improvement by Enlarging the Training Set While Learning DFAs." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11578079_7.

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Rothmann, Marc, and Mario Porrmann. "STANN – Synthesis Templates for Artificial Neural Network Inference and Training." In Advances in Computational Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43085-5_31.

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Conference papers on the topic "Inference training"

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Zhu, Zeya, Enrong Zheng, and Yukai Tan. "Speculative Inference with vLLM: Optimizing Heterogeneous Computing in Training-Inference Integrated Environments." In 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT). IEEE, 2025. https://doi.org/10.1109/iscait64916.2025.11010601.

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Zhu, Zeya, Mengyu Sun, Enrong Zheng, and Mengru Cai. "Research on LLM speculative inference in training-inference integrated computing infrastructure scenarios." In International Conference on Computer Application and Information Security (ICCAIS 2024), edited by Sadiq Ali Safaa, Pandey Hari Mohan, and Boussaid Farid. SPIE, 2025. https://doi.org/10.1117/12.3061241.

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Khan, Osama, Gwanjong Park, Junyeol Yu, and Euiseong Seo. "Cloud Reamer: Enabling Inference Services in Training Clusters." In 2024 32nd International Conference on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS). IEEE, 2024. https://doi.org/10.1109/mascots64422.2024.10786549.

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Jawalkar, Neha, Kanav Gupta, Arkaprava Basu, Nishanth Chandran, Divya Gupta, and Rahul Sharma. "Orca: FSS-based Secure Training and Inference with GPUs." In 2024 IEEE Symposium on Security and Privacy (SP). IEEE, 2024. http://dx.doi.org/10.1109/sp54263.2024.00063.

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Sadat, Mobashir, and Cornelia Caragea. "Co-training for Low Resource Scientific Natural Language Inference." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.139.

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Nedelcu, Bogdan, and Adina Magda Florea. "Synthetic Dataset Generation for Edge Drone Inference and Training." In 2024 26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2024. https://doi.org/10.1109/synasc65383.2024.00052.

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Zhong, Meizhi, Lemao Liu, Kehai Chen, Mingming Yang, and Min Zhang. "Context Consistency between Training and Inference in Simultaneous Machine Translation." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.727.

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Chumachenko, Kateryna, Alexandros Iosifidis, and Moncef Gabbouj. "Uimt: A Framework for Improving Unimodal Inference via Multimodal Training." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647735.

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Qin, Meng, Chaorui Zhang, Yu Gao, et al. "Towards Faster Graph Partitioning via Pre-Training and Inductive Inference." In 2024 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2024. https://doi.org/10.1109/hpec62836.2024.10938459.

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Wang, Qifan, Shujie Cui, Lei Zhou, et al. "GTree: GPU-friendly Privacy-preserving Decision Tree Training and Inference." In 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2024. https://doi.org/10.1109/trustcom63139.2024.00118.

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Reports on the topic "Inference training"

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Schneider, Carsten. Set-Theoretic Multi-Method Research: Combining QCA and Case Studies. Instats Inc., 2025. https://doi.org/10.61700/dtj1vhg0ykv1r1939.

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This three-day seminar provides comprehensive training in Set-Theoretic Multi-Method Research (SMMR), integrating Qualitative Comparative Analysis (QCA) with detailed case studies to enhance participants' methodological capabilities for drawing descriptive and causal inference. Led by Carsten Schneider, a leading expert in the field, the workshop covers theoretical foundations, practical applications, and the use of R software for performing SMMR, equipping researchers across the social sciences with impactful skills for nuanced data analysis.
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Rosenblat, Sruly, Tim O'Reilly, and Ilan Strauss. Beyond Public Access in LLM Pre-Training Data: Non-public book content in OpenAI’s Models. AI Disclosures Project, Social Science Research Council, 2025. https://doi.org/10.35650/aidp.4111.d.2025.

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Using a legally obtained dataset of 34 copyrighted O’Reilly Media books, we apply the DE-COP membership inference attack method to investigate whether OpenAI’s large language models were trained on copyrighted content without consent. Our AUROC scores show that GPT-4o, OpenAI’s more recent and capable model, demonstrates strong recognition of paywalled O’Reilly book content (AUROC = 82%), compared to OpenAI’s earlier model GPT-3.5 Turbo. In contrast, GPT-3.5 Turbo shows greater relative recognition of publicly accessible O’Reilly book samples. GPT-4o Mini, as a much smaller model, shows no kno
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Pasupuleti, Murali Krishna. Mathematical Modeling for Machine Learning: Theory, Simulation, and Scientific Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv125.

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Abstract Mathematical modeling serves as a fundamental framework for advancing machine learning (ML) and artificial intelligence (AI) by integrating theoretical, computational, and simulation-based approaches. This research explores how numerical optimization, differential equations, variational inference, and scientific computing contribute to the development of scalable, interpretable, and efficient AI systems. Key topics include convex and non-convex optimization, physics-informed machine learning (PIML), partial differential equation (PDE)-constrained AI, and Bayesian modeling for uncertai
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Strauss, Ilan, Isobel Moure, Tim O’Reilly, and Sruly Rosenblat. The State of AI Governance Research: AI Safety and Reliability in Real World Commercial Deployment. AI Disclosures Project, Social Science Research Council, 2025. https://doi.org/10.35650/aidp.4112.d.2025.

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Drawing on 1,178 safety and reliability papers from 9,439 generative AI papers (Jan- uary 2020 - March 2025), we compare research outputs of leading AI companies (An- thropic, Google DeepMind, Meta, Microsoft, and OpenAI) and AI universities (CMU, MIT, NYU, Stanford, UC Berkeley, and University of Washington). We find that cor- porate AI research increasingly concentrates on pre-deployment areas — model align- ment and testing & evaluation — while attention to deployment-stage issues, such as model bias, has waned, as commercial imperatives and existential risks have come into focus. We fi
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Roberson, Madeleine, Kathleen Inman, Ashley Carey, Isaac Howard, and Jameson Shannon. Probabilistic neural networks that predict compressive strength of high strength concrete in mass placements using thermal history. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/44483.

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This study explored the use of artificial neural networks to predict UHPC compressive strengths given thermal history and key mix components. The model developed herein employs Bayesian variational inference using Monte Carlo dropout to convey prediction uncertainty using 735 datapoints on seven UHPC mixtures collected using a variety of techniques. Datapoints contained a measured compressive strength along with three curing inputs (specimen maturity, maximum temperature experienced during curing, time of maximum temperature) and five mixture inputs to distinguish each UHPC mixture (cement typ
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DeJaeghere, Joan, Bich-Hang Duong, and Vu Dao. Teaching Practices That Support and Promote Learning: Qualitative Evidence from High and Low Performing Classes in Vietnam. Research on Improving Systems of Education (RISE), 2021. http://dx.doi.org/10.35489/bsg-rise-ri_2021/024.

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This Insight Note contributes to the growing body of knowledge on teaching practices that foster student learning and achievement by analysing in-depth qualitative data from classroom observations and teacher interviews. Much of the research on teachers and teaching in development literature focuses on observable and quantified factors, including qualifications and training. But simply being qualified (with a university degree in education or subject areas), or trained in certain ways (e.g., coaching versus in-service) explains very little of the variation in learning outcomes (Kane and Staige
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