Papers

Selected research and references.

Recent

Thumbnail: Bootstrapping Life-Inspired Machine Intelligence: The Biological Route from Chemistry to Cognition and Creativity
Bootstrapping Life-Inspired Machine Intelligence: The Biological Route from Chemistry to Cognition and Creativity
Giovanni Pezzulo, Michael Levin · 2026
PDF alifeintelligence
Thumbnail: From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence
From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence
Marc Finzi, Shikai Qiu, Yiding Jiang, Pavel Izmailov, J. Zico Kolter, Andrew Gordon Wilson · 2026
PDF information-theoryintelligence
Thumbnail: Generative Neural Operators through Diffusion Last Layer
Generative Neural Operators through Diffusion Last Layer
Sungwon Park, Anthony Zhou, Hongjoong Kim, Amir Barati Farimani · 2026
PDF neural-operatorsdiffusion
Thumbnail: Maximum Likelihood Reinforcement Learning
Maximum Likelihood Reinforcement Learning
Yiding Jiang, Fahim Tajwar, Guanning Zeng, Yueer Zhou, Yuda Song, Daman Arora, Jeff Schneider, Ruslan Salakhutdinov, Haiwen Feng, Andrea Zanette · 2026
PDF reinforcement-learningmaximum-likelihood
Thumbnail: A Mind Cannot Be Smeared Across Time
A Mind Cannot Be Smeared Across Time
Michael Timothy Bennett · 2026
PDF consciousnessai-agency
Thumbnail: TabICLv2: A Better, Faster, Scalable, and Open Tabular Foundation Model
TabICLv2: A Better, Faster, Scalable, and Open Tabular Foundation Model
Gael Varoquaux, Jingang Qu, David Holzmuller, Marine Le Morvan · 2026
PDF tabularfoundation-models
Thumbnail: Time, Identity and Consciousness in Language Model Agents
Time, Identity and Consciousness in Language Model Agents
Michael Timothy Bennett, Elija Perrier · 2026
PDF consciousnesslanguage-models
Thumbnail: Training Language Models via Neural Cellular Automata
Training Language Models via Neural Cellular Automata
Dan Lee, Seungwook Han, Akarsh Kumar, Pulkit Agrawal · 2026
PDF language-modelscellular-automata
Thumbnail: Why Is Anything Conscious?
Why Is Anything Conscious?
Michael Timothy Bennett, Sean Welsh, Anna Ciaunica · 2026
PDF consciousnessai-agency
Thumbnail: Hypothesis Testing with E-Values
Hypothesis Testing with E-Values
Aaditya Ramdas, Ruodu Wang · 2025
PDF statisticse-values
Thumbnail: Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models
Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models
Haoyi Song, Ruihan Ji, Naichen Shi, Fan Lai, Raed Al Kontar · 2025
PDF uncertaintyprobabilistic-deep-learning
Thumbnail: TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
Gael Varoquaux, Jingang Qu, David Holzmuller, Marine Le Morvan · 2025
PDF tabularfoundation-models

Sutskever / Carmack

Thumbnail: Scaling Laws for Neural Language Models
Scaling Laws for Neural Language Models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei · 2020
PDF scalinglanguage-models
Thumbnail: GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism
GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Mia Xu Chen, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen · 2019
PDF systemsscaling
The Annotated Transformer
2018
The Annotated Transformer
Alexander Rush · 2018
Paper transformersattention
Thumbnail: Relational Recurrent Neural Networks
Relational Recurrent Neural Networks
Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap · 2018
PDF relational-reasoningsequence-models
Thumbnail: Attention Is All You Need
Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin · 2017
PDF transformerssequence-modeling
CS231n: Convolutional Neural Networks for Visual Recognition
2017
CS231n: Convolutional Neural Networks for Visual Recognition
Fei-Fei Li, Andrej Karpathy, Justin Johnson · 2017
Paper visioncnn
Thumbnail: Kolmogorov Complexity and Algorithmic Randomness
Kolmogorov Complexity and Algorithmic Randomness
A. Shen, V. A. Uspensky, N. Vereshchagin · 2017
PDF kolmogorov-complexityalgorithmic-randomness
Thumbnail: Neural Message Passing for Quantum Chemistry
Neural Message Passing for Quantum Chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl · 2017
PDF graph-neural-networksmessage-passing
Thumbnail: Pointer Networks
Pointer Networks
Oriol Vinyals, Meire Fortunato, Navdeep Jaitly · 2017
PDF sequence-modelingneural-networks
Thumbnail: A Simple Neural Network Module for Relational Reasoning
A Simple Neural Network Module for Relational Reasoning
Adam Santoro, David Raposo, David G. T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap · 2017
PDF relational-reasoningrepresentation
Thumbnail: Variational Lossy Autoencoder
Variational Lossy Autoencoder
Ilya Sutskever, Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Pieter Abbeel · 2017
PDF generative-modelsrepresentation-learning
Thumbnail: Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, Jie Chen, Jingdong Chen, Zhijie Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Ke Ding, Niandong Du, Erich Elsen, Jesse Engel, Weiwei Fang, Linxi Fan, Christopher Fougner, Liang Gao, Caixia Gong, Awni Hannun, Tony Han, Lappi Johannes, Bing Jiang, Cai Ju, Billy Jun, Patrick LeGresley, Libby Lin, Junjie Liu, Yang Liu, Weigao Li, Xiangang Li, Dongpeng Ma, Sharan Narang, Andrew Ng, Sherjil Ozair, Yiping Peng, Ryan Prenger, Sheng Qian, Zongfeng Quan, Jonathan Raiman, Vinay Rao, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Kavya Srinet, Anuroop Sriram, Haiyuan Tang, Liliang Tang, Chong Wang, Jidong Wang, Kaifu Wang, Yi Wang, Zhijian Wang, Zhiqian Wang, Shuang Wu, Likai Wei, Bo Xiao, Wen Xie, Yan Xie, Dani Yogatama, Bin Yuan, Jun Zhan, Zhenyao Zhu · 2016
PDF sequence-modelsspeech
Thumbnail: Identity Mappings in Deep Residual Networks
Identity Mappings in Deep Residual Networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun · 2016
PDF deep-learningoptimization
Thumbnail: Multi-Scale Context Aggregation by Dilated Convolutions
Multi-Scale Context Aggregation by Dilated Convolutions
Fisher Yu, Vladlen Koltun · 2016
PDF visioncnn
Thumbnail: Order Matters: Sequence to Sequence for Sets
Order Matters: Sequence to Sequence for Sets
Oriol Vinyals, Samy Bengio, Manjunath Kudlur · 2016
PDF sequence-modelssets
Thumbnail: Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun · 2015
PDF deep-learningcomputer-vision
Thumbnail: Neural Machine Translation by Jointly Learning to Align and Translate
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio · 2015
PDF attentionsequence-models
Understanding LSTM Networks
2015
Understanding LSTM Networks
Christopher Olah · 2015
Paper sequence-modelsrnn
The Unreasonable Effectiveness of Recurrent Neural Networks
2015
The Unreasonable Effectiveness of Recurrent Neural Networks
Andrej Karpathy · 2015
Paper sequence-modelsrnn
Thumbnail: Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton
Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton
Scott Aaronson, Sean M. Carroll, Lauren Ouellette · 2014
PDF complexitykolmogorov-complexity
Thumbnail: Neural Turing Machines
Neural Turing Machines
Alex Graves, Greg Wayne, Ivo Danihelka · 2014
PDF memorysequence-models
Thumbnail: Recurrent Neural Network Regularization
Recurrent Neural Network Regularization
Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals · 2014
PDF sequence-modelsrnn
Thumbnail: ImageNet Classification with Deep Convolutional Neural Networks
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton · 2012
PDF visioncnn
The First Law of Complexodynamics
2011
The First Law of Complexodynamics
Scott Aaronson · 2011
Paper complexitythermodynamics
Thumbnail: Machine Super Intelligence
Machine Super Intelligence
Shane Legg · 2008
PDF artificial-general-intelligencetheory
Thumbnail: A Tutorial Introduction to the Minimum Description Length Principle
A Tutorial Introduction to the Minimum Description Length Principle
Peter Grunwald · 2004
PDF mdlcompression
Thumbnail: Keeping Neural Networks Simple by Minimizing the Description Length of the Weights
Keeping Neural Networks Simple by Minimizing the Description Length of the Weights
Geoffrey E. Hinton, Drew van Camp · 1993
PDF mdlcompression

Core AI

Thumbnail: TabICLv2: A Better, Faster, Scalable, and Open Tabular Foundation Model
TabICLv2: A Better, Faster, Scalable, and Open Tabular Foundation Model
Gael Varoquaux, Jingang Qu, David Holzmuller, Marine Le Morvan · 2026
PDF tabularfoundation-models
Thumbnail: TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
Gael Varoquaux, Jingang Qu, David Holzmuller, Marine Le Morvan · 2025
PDF tabularfoundation-models
Thumbnail: World Modeling with Probabilistic Structure Integration
World Modeling with Probabilistic Structure Integration
Klemen Kotar, Wanhee Lee, Rahul Venkatesh, Honglin Chen, Daniel Bear, Jared Watrous, Simon Kim, Khai Loong Aw, Lilian Naing Chen, Stefan Stojanov, Kevin Feigelis, Imran Thobani, Alex Durango, Khaled Jedoui, Atlas Kazemian, Dan Yamins · 2025
PDF world-modelsprobabilistic-deep-learning
Thumbnail: TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
Noah Hollmann, Samuel Muller, Katharina Eggensperger, Frank Hutter · 2023
PDF tabularfoundation-models
Thumbnail: Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
Jiehui Xu, Haixu Wu, Jianmin Wang, Mingsheng Long · 2022
PDF time-seriesanomaly-detection
Thumbnail: AST: Audio Spectrogram Transformer
AST: Audio Spectrogram Transformer
Yuan Gong, Yu-An Chung, James Glass · 2021
PDF audiotransformers
Thumbnail: RealFormer: Transformer Likes Residual Attention
RealFormer: Transformer Likes Residual Attention
Ruining He, Anirudh Ravula, Bhargav Kanagal, Joshua Ainslie · 2021
PDF transformersattention
Thumbnail: Scaling Laws for Neural Language Models
Scaling Laws for Neural Language Models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei · 2020
PDF scalinglanguage-models
Thumbnail: GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism
GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Mia Xu Chen, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen · 2019
PDF systemsscaling
The Annotated Transformer
2018
The Annotated Transformer
Alexander Rush · 2018
Paper transformersattention
Thumbnail: Deep One-Class Classification
Deep One-Class Classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Muller, Marius Kloft · 2018
PDF anomaly-detectiondeep-svdd
Thumbnail: Relational Recurrent Neural Networks
Relational Recurrent Neural Networks
Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap · 2018
PDF relational-reasoningsequence-models
Thumbnail: Attention Is All You Need
Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin · 2017
PDF transformerssequence-modeling
Thumbnail: Pointer Networks
Pointer Networks
Oriol Vinyals, Meire Fortunato, Navdeep Jaitly · 2017
PDF sequence-modelingneural-networks
Thumbnail: A Simple Neural Network Module for Relational Reasoning
A Simple Neural Network Module for Relational Reasoning
Adam Santoro, David Raposo, David G. T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap · 2017
PDF relational-reasoningrepresentation
Thumbnail: Variational Lossy Autoencoder
Variational Lossy Autoencoder
Ilya Sutskever, Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Pieter Abbeel · 2017
PDF generative-modelsrepresentation-learning
Thumbnail: Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, Jie Chen, Jingdong Chen, Zhijie Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Ke Ding, Niandong Du, Erich Elsen, Jesse Engel, Weiwei Fang, Linxi Fan, Christopher Fougner, Liang Gao, Caixia Gong, Awni Hannun, Tony Han, Lappi Johannes, Bing Jiang, Cai Ju, Billy Jun, Patrick LeGresley, Libby Lin, Junjie Liu, Yang Liu, Weigao Li, Xiangang Li, Dongpeng Ma, Sharan Narang, Andrew Ng, Sherjil Ozair, Yiping Peng, Ryan Prenger, Sheng Qian, Zongfeng Quan, Jonathan Raiman, Vinay Rao, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Kavya Srinet, Anuroop Sriram, Haiyuan Tang, Liliang Tang, Chong Wang, Jidong Wang, Kaifu Wang, Yi Wang, Zhijian Wang, Zhiqian Wang, Shuang Wu, Likai Wei, Bo Xiao, Wen Xie, Yan Xie, Dani Yogatama, Bin Yuan, Jun Zhan, Zhenyao Zhu · 2016
PDF sequence-modelsspeech
Thumbnail: Identity Mappings in Deep Residual Networks
Identity Mappings in Deep Residual Networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun · 2016
PDF deep-learningoptimization
Thumbnail: Multi-Scale Context Aggregation by Dilated Convolutions
Multi-Scale Context Aggregation by Dilated Convolutions
Fisher Yu, Vladlen Koltun · 2016
PDF visioncnn
Thumbnail: Order Matters: Sequence to Sequence for Sets
Order Matters: Sequence to Sequence for Sets
Oriol Vinyals, Samy Bengio, Manjunath Kudlur · 2016
PDF sequence-modelssets
Thumbnail: Neural Machine Translation by Jointly Learning to Align and Translate
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio · 2015
PDF attentionsequence-models
Understanding LSTM Networks
2015
Understanding LSTM Networks
Christopher Olah · 2015
Paper sequence-modelsrnn
The Unreasonable Effectiveness of Recurrent Neural Networks
2015
The Unreasonable Effectiveness of Recurrent Neural Networks
Andrej Karpathy · 2015
Paper sequence-modelsrnn
Thumbnail: Neural Turing Machines
Neural Turing Machines
Alex Graves, Greg Wayne, Ivo Danihelka · 2014
PDF memorysequence-models
Thumbnail: Recurrent Neural Network Regularization
Recurrent Neural Network Regularization
Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals · 2014
PDF sequence-modelsrnn

Uncertainty & Evidence

Thumbnail: Generative Neural Operators through Diffusion Last Layer
Generative Neural Operators through Diffusion Last Layer
Sungwon Park, Anthony Zhou, Hongjoong Kim, Amir Barati Farimani · 2026
PDF neural-operatorsdiffusion
Thumbnail: Hypothesis Testing with E-Values
Hypothesis Testing with E-Values
Aaditya Ramdas, Ruodu Wang · 2025
PDF statisticse-values
Thumbnail: Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models
Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models
Haoyi Song, Ruihan Ji, Naichen Shi, Fan Lai, Raed Al Kontar · 2025
PDF uncertaintyprobabilistic-deep-learning
Thumbnail: Uncertainty Quantification of Large Language Models using Approximate Bayesian Computation
Uncertainty Quantification of Large Language Models using Approximate Bayesian Computation
Mridul Sharma, Adeetya Patel, Zaneta D' Souza, Samira Abbasgholizadeh Rahimi, Siva Reddy, Sreenath Madathil · 2025
PDF uncertaintylanguage-models
Thumbnail: TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
Noah Hollmann, Samuel Muller, Katharina Eggensperger, Frank Hutter · 2023
PDF tabularfoundation-models
Thumbnail: Variational Inference: A Review for Statisticians
Variational Inference: A Review for Statisticians
David M. Blei, Alp Kucukelbir, Jon D. McAuliffe · 2017
PDF variational-inferencebayesian
Thumbnail: How Random Is a Coin Toss? Bayesian Inference and the Symbolic Dynamics of Deterministic Chaos
How Random Is a Coin Toss? Bayesian Inference and the Symbolic Dynamics of Deterministic Chaos
Christopher C. Strelioff, James P. Crutchfield · 2006
PDF bayesian-inferencedynamical-systems

Agency & RL

Thumbnail: Maximum Likelihood Reinforcement Learning
Maximum Likelihood Reinforcement Learning
Yiding Jiang, Fahim Tajwar, Guanning Zeng, Yueer Zhou, Yuda Song, Daman Arora, Jeff Schneider, Ruslan Salakhutdinov, Haiwen Feng, Andrea Zanette · 2026
PDF reinforcement-learningmaximum-likelihood
Thumbnail: Time, Identity and Consciousness in Language Model Agents
Time, Identity and Consciousness in Language Model Agents
Michael Timothy Bennett, Elija Perrier · 2026
PDF consciousnesslanguage-models
Thumbnail: The Markov Blanket Trick: On the Scope of the Free Energy Principle and Active Inference
The Markov Blanket Trick: On the Scope of the Free Energy Principle and Active Inference
Vicente Raja, Dinesh Valluri, Edward Baggs, Anthony Chemero, Michael L. Anderson · 2021
Paper active-inferencesystems
Thumbnail: Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review
Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review
Sergey Levine · 2018
PDF reinforcement-learningprobabilistic-inference
Thumbnail: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine · 2018
PDF reinforcement-learningmaximum-entropy
Thumbnail: Active Inference: A Process Theory
Active Inference: A Process Theory
Karl Friston, Thomas FitzGerald, Francesco Rigoli, Philipp Schwartenbeck, Giovanni Pezzulo · 2017
PDF active-inferencefree-energy
Thumbnail: VIME: Variational Information Maximizing Exploration
VIME: Variational Information Maximizing Exploration
Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel · 2016
PDF reinforcement-learningexploration
Thumbnail: Efficient Computation of Optimal Actions
Efficient Computation of Optimal Actions
Emanuel Todorov · 2009
PDF controlreinforcement-learning

AI Consciousness

Thumbnail: A Mind Cannot Be Smeared Across Time
A Mind Cannot Be Smeared Across Time
Michael Timothy Bennett · 2026
PDF consciousnessai-agency
Thumbnail: Time, Identity and Consciousness in Language Model Agents
Time, Identity and Consciousness in Language Model Agents
Michael Timothy Bennett, Elija Perrier · 2026
PDF consciousnesslanguage-models
Thumbnail: Why Is Anything Conscious?
Why Is Anything Conscious?
Michael Timothy Bennett, Sean Welsh, Anna Ciaunica · 2026
PDF consciousnessai-agency

Learning

Thumbnail: Muon: An optimizer for hidden layers in neural networks
Muon: An optimizer for hidden layers in neural networks
Keller Jordan · 2024
Paper optimizationtraining
Thumbnail: GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism
GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Mia Xu Chen, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen · 2019
PDF systemsscaling
Thumbnail: Cyclical Learning Rates for Training Neural Networks
Cyclical Learning Rates for Training Neural Networks
Leslie N. Smith · 2017
PDF optimizationtraining
Thumbnail: Identity Mappings in Deep Residual Networks
Identity Mappings in Deep Residual Networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun · 2016
PDF deep-learningoptimization
Thumbnail: Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun · 2015
PDF deep-learningcomputer-vision

Applied Modeling

Thumbnail: TabICLv2: A Better, Faster, Scalable, and Open Tabular Foundation Model
TabICLv2: A Better, Faster, Scalable, and Open Tabular Foundation Model
Gael Varoquaux, Jingang Qu, David Holzmuller, Marine Le Morvan · 2026
PDF tabularfoundation-models
Thumbnail: TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
Gael Varoquaux, Jingang Qu, David Holzmuller, Marine Le Morvan · 2025
PDF tabularfoundation-models
Thumbnail: WTNN: Weibull-Tailored Neural Networks for Survival Analysis
WTNN: Weibull-Tailored Neural Networks for Survival Analysis
Gabrielle Rives, Olivier Lopez, Nicolas Bousquet · 2025
PDF survivalwtte
Thumbnail: TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
Noah Hollmann, Samuel Muller, Katharina Eggensperger, Frank Hutter · 2023
PDF tabularfoundation-models
Thumbnail: Towards Total Recall in Industrial Anomaly Detection
Towards Total Recall in Industrial Anomaly Detection
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Scholkopf, Thomas Brox, Peter Gehler · 2022
Paper industrial-visionanomaly-detection
Thumbnail: Deep Cox Mixtures for Survival Regression
Deep Cox Mixtures for Survival Regression
Chirag Nagpal, Steve Yadlowsky, Negar Rostamzadeh, Katherine Heller · 2021
PDF survivaltime-to-event
Thumbnail: Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks
Chirag Nagpal, Xinyu Li, Artur Dubrawski · 2021
Paper survivalcompeting-risks
Thumbnail: Tabular Data: Deep Learning Is Not All You Need
Tabular Data: Deep Learning Is Not All You Need
Ravid Shwartz-Ziv, Amitai Armon · 2021
PDF tabularbaselines
Thumbnail: Estimation of Conditional Mixture Weibull Distribution with Right-Censored Data Using Neural Network for Time-to-Event Analysis
Estimation of Conditional Mixture Weibull Distribution with Right-Censored Data Using Neural Network for Time-to-Event Analysis
Achraf Bennis, Sandrine Mouysset, Mathieu Serrurier · 2020
PDF survivalwtte
Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis With Competing Risks Based on Longitudinal Data
2020
Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis With Competing Risks Based on Longitudinal Data
Changhee Lee, Jinsung Yoon, Mihaela van der Schaar · 2020
Paper survivalcompeting-risks
Thumbnail: Underspecification Presents Challenges for Credibility in Modern Machine Learning
Underspecification Presents Challenges for Credibility in Modern Machine Learning
D. Sculley, Alex Beutel, Zachary Nado, Xuezhi Wang, Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai · 2020
PDF systemsevaluation
Thumbnail: Anomaly Detection Using One-Class Neural Networks
Anomaly Detection Using One-Class Neural Networks
Raghavendra Chalapathy, Aditya Krishna Menon, Sanjay Chawla · 2019
PDF anomaly-detectionvision
Thumbnail: Reliability-Equivalent Field Reference Usage Level When Both Field Usage and Usage to Failure Are Random
Reliability-Equivalent Field Reference Usage Level When Both Field Usage and Usage to Failure Are Random
Fengbin Sun · 2019
Paper reliabilityusage-modeling
Thumbnail: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, Dan Pei · 2019
Paper time-seriesanomaly-detection
Thumbnail: DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks
DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks
Changhee Lee, William Zame, Jinsung Yoon, Mihaela van der Schaar · 2018
PDF survivalcompeting-risks
Thumbnail: Deep Learning for Patient-Specific Kidney Graft Survival Analysis
Deep Learning for Patient-Specific Kidney Graft Survival Analysis
Margaux Luck, Tristan Sylvain, Heloise Cardinal, Andrea Lodi, Yoshua Bengio · 2017
PDF survivalmedical-modeling
Thumbnail: WTTE-RNN: Weibull Time To Event Recurrent Neural Network
WTTE-RNN: Weibull Time To Event Recurrent Neural Network
Egil Martinsson · 2017
PDF survivalwtte
Thumbnail: Random Survival Forests
Random Survival Forests
Hemant Ishwaran, Udaya B. Kogalur, Eugene H. Blackstone, Michael S. Lauer · 2008
Paper survivalreliability

Vision & Anomaly

Thumbnail: EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
Kilian Batzner, Lars Heckler, Rebecca König · 2024
PDF industrial-visionanomaly-detection
Thumbnail: Multimodal Industrial Anomaly Detection via Hybrid Fusion
Multimodal Industrial Anomaly Detection via Hybrid Fusion
Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, Chengjie Wang · 2023
Paper industrial-visionanomaly-detection
Thumbnail: WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, Onkar Dabeer · 2023
PDF industrial-visionanomaly-detection
Thumbnail: Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
Jiehui Xu, Haixu Wu, Jianmin Wang, Mingsheng Long · 2022
PDF time-seriesanomaly-detection
Thumbnail: Towards Total Recall in Industrial Anomaly Detection
Towards Total Recall in Industrial Anomaly Detection
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Scholkopf, Thomas Brox, Peter Gehler · 2022
Paper industrial-visionanomaly-detection
Thumbnail: DRAEM: A Discriminatively Trained Reconstruction Embedding for Surface Anomaly Detection
DRAEM: A Discriminatively Trained Reconstruction Embedding for Surface Anomaly Detection
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2015
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Paper sequence-modelsrnn
Thumbnail: Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton
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PDF complexitykolmogorov-complexity
Thumbnail: Neural Turing Machines
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Thumbnail: Recurrent Neural Network Regularization
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Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals · 2014
PDF sequence-modelsrnn
Thumbnail: ImageNet Classification with Deep Convolutional Neural Networks
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Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton · 2012
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The First Law of Complexodynamics
2011
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Scott Aaronson · 2011
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Thumbnail: Efficient Computation of Optimal Actions
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Emanuel Todorov · 2009
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Thumbnail: Machine Super Intelligence
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Shane Legg · 2008
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Thumbnail: Random Survival Forests
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Thumbnail: How Random Is a Coin Toss? Bayesian Inference and the Symbolic Dynamics of Deterministic Chaos
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Thumbnail: A Tutorial Introduction to the Minimum Description Length Principle
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Peter Grunwald · 2004
PDF mdlcompression
Thumbnail: Keeping Neural Networks Simple by Minimizing the Description Length of the Weights
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PDF mdlcompression