Kevin Swersky - RankMe

93 papers A* 25A 3Misc 2Journal 55Unranked 7

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Comparative Analysis of LLM Adaptation: SFT, LoRA, and ICL in Data-Scarce Scenarios. CoRR Bernd Bohnet, Rumen Dangovski, Kevin Swersky, Sherry Moore, Arslan Chaudhry, Kathleen Kenealy, Noah Fiedel
2025	J	`jnl`	Do generative video models learn physical principles from watching videos? CoRR Saman Motamed, Laura Culp, Kevin Swersky, Priyank Jaini, Robert Geirhos
2025	J	`jnl`	Enhancing LLM Planning Capabilities through Intrinsic Self-Critique. CoRR Bernd Bohnet, Pierre-Alexandre Kamienny, Hanie Sedghi, Dilan Görür, Pranjal Awasthi, Aaron Parisi, Kevin Swersky, Rosanne Liu, Azade Nova, Noah Fiedel
2025	J	`jnl`	SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads? CoRR Jeffrey Jian Ma, Milad Hashemi, Amir Yazdanbakhsh, Kevin Swersky, Ofir Press, Enhui Li, Vijay Janapa Reddi, Parthasarathy Ranganathan
2025	J	`jnl`	Video models are zero-shot learners and reasoners. CoRR Thaddäus Wiedemer, Yuxuan Li, Paul Vicol, Shixiang Shane Gu, Nick Matarese, Kevin Swersky, Been Kim, Priyank Jaini, Robert Geirhos
2024	J	`jnl`	Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Trans. Mach. Learn. Res. Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T. Parisi, Abhishek Kumar, Alexander A. Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel
2024	A*	`conf`	Directly Fine-Tuning Diffusion Models on Differentiable Rewards. ICLR Kevin Clark, Paul Vicol, Kevin Swersky, David J. Fleet
2024	J	`jnl`	Exploring and Benchmarking the Planning Capabilities of Large Language Models. CoRR Bernd Bohnet, Azade Nova, Aaron T. Parisi, Kevin Swersky, Katayoon Goshvadi, Hanjun Dai, Dale Schuurmans, Noah Fiedel, Hanie Sedghi
2024	J	`jnl`	Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models. CoRR Cristina Nader Vasconcelos, Abdullah Rashwan, Austin Waters, Trevor Walker, Keyang Xu, Jimmy Yan, Rui Qian, Shixin Luo, Zarana Parekh, Andrew Bunner, Hongliang Fei, Roopal Garg, Mandy Guo, Ivana Kajic, Yeqing Li, Henna Nandwani, Jordi Pont-Tuset, Yasumasa Onoe, Sarah Rosston, Su Wang, Wenlei Zhou, Kevin Swersky, David J. Fleet, Jason M. Baldridge, Oliver Wang
2024	J	`jnl`	Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models. Trans. Mach. Learn. Res. Cristina Nader Vasconcelos, Abdullah Rashwan, Austin Waters, Trevor Walker, Keyang Xu, Jimmy Yan, Rui Qian, Yeqing Li, Shixin Luo, Yasumasa Onoe, Zarana Parekh, Ivana Kajic, Mandy Guo, Wenlei Zhou, Sarah Rosston, Roopal Garg, Hongliang Fei, Jordi Pont-Tuset, Su Wang, Henna Nandwani, Andrew Bunner, Kevin Swersky, David J. Fleet, Oliver Wang, Jason M. Baldridge
2024	J	`jnl`	Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation. CoRR Bernd Bohnet, Kevin Swersky, Rosanne Liu, Pranjal Awasthi, Azade Nova, Javier Snaider, Hanie Sedghi, Aaron T. Parisi, Michael Collins, Angeliki Lazaridou, Orhan Firat, Noah Fiedel
2024	J	`jnl`	Pre-trained Gaussian Processes for Bayesian Optimization. J. Mach. Learn. Res. Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani
2024	J	`jnl`	Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. CoRR Jiri Hron, Laura Culp, Gamaleldin F. Elsayed, Rosanne Liu, Ben Adlam, Maxwell L. Bileschi, Bernd Bohnet, JD Co-Reyes, Noah Fiedel, C. Daniel Freeman, Izzeddin Gur, Kathleen Kenealy, Jaehoon Lee, Peter J. Liu, Gaurav Mishra, Igor Mordatch, Azade Nova, Roman Novak, Aaron Parisi, Jeffrey Pennington, Alex Rizkowsky, Isabelle Simpson, Hanie Sedghi, Jascha Sohl-Dickstein, Kevin Swersky, Sharad Vikram, Tris Warkentin, Lechao Xiao, Kelvin Xu, Jasper Snoek, Simon Kornblith
2023	J	`jnl`	Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel
2023	A*	`conf`	CUF: Continuous Upsampling Filters. CVPR Cristina Nader Vasconcelos, A. Cengiz Öztireli, Mark J. Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi
2023	J	`jnl`	Directly Fine-Tuning Diffusion Models on Differentiable Rewards. CoRR Kevin Clark, Paul Vicol, Kevin Swersky, David J. Fleet
2023	J	`jnl`	Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? CoRR C. Daniel Freeman, Laura Culp, Aaron Parisi, Maxwell L. Bileschi, Gamaleldin F. Elsayed, Alex Rizkowsky, Isabelle Simpson, Alex Alemi, Azade Nova, Ben Adlam, Bernd Bohnet, Gaurav Mishra, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein
2023	J	`jnl`	Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single. CoRR Paul Vicol, Zico Kolter, Kevin Swersky
2023	J	`jnl`	Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks. Trans. Mach. Learn. Res. Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao
2022	J	`jnl`	CUF: Continuous Upsampling Filters. CoRR Cristina Nader Vasconcelos, Cengiz Öztireli, Mark J. Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi
2022	A*	`conf`	Data-Driven Offline Optimization for Architecting Hardware Accelerators. ICLR Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine
2022	J	`jnl`	Learning to Improve Code Efficiency. CoRR Binghong Chen, Daniel Tarlow, Kevin Swersky, Martin Maas, Pablo Ariel Heiber, Ashish Naik, Milad Hashemi, Parthasarathy Ranganathan
2022	J	`jnl`	Pre-training helps Bayesian optimization too. CoRR Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani
2022	J	`jnl`	Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks. CoRR Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao
2022	A*	`conf`	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. ICDM Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra
2021	A*	`conf`	A hierarchical neural model of data prefetching. ASPLOS Zhan Shi, Akanksha Jain, Kevin Swersky, Milad Hashemi, Parthasarathy Ranganathan, Calvin Lin
2021	J	`jnl`	Apollo: Transferable Architecture Exploration. CoRR Amir Yazdanbakhsh, Christof Angermüller, Berkin Akin, Yanqi Zhou, Albin Jones, Milad Hashemi, Kevin Swersky, Satrajit Chatterjee, Ravi Narayanaswami, James Laudon
2021	J	`jnl`	Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers. CoRR Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani
2021	J	`jnl`	Data-Driven Offline Optimization For Architecting Hardware Accelerators. CoRR Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine
2021	A*	`conf`	No MCMC for me: Amortized sampling for fast and stable training of energy-based models. ICLR Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud
2021	A*	`conf`	Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. ICML Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison
2021	J	`jnl`	Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. CoRR Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison
2021	J	`jnl`	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. CoRR Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra
2020	A	`conf`	Amortized Bayesian Optimization over Discrete Spaces. UAI Kevin Swersky, Yulia Rubanova, David Dohan, Kevin Murphy
2020	A*	`conf`	An Imitation Learning Approach for Cache Replacement. ICML Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn
2020	J	`jnl`	An Imitation Learning Approach for Cache Replacement. CoRR Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn
2020	A*	`conf`	Big Self-Supervised Models are Strong Semi-Supervised Learners. NeurIPS Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton
2020	J	`jnl`	Big Self-Supervised Models are Strong Semi-Supervised Learners. CoRR Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton
2020	J	`jnl`	Human 3D keypoints via spatial uncertainty modeling. CoRR Francis Williams, Or Litany, Avneesh Sud, Kevin Swersky, Andrea Tagliasacchi
2020	J	`jnl`	Learned Hardware/Software Co-Design of Neural Accelerators. CoRR Zhan Shi, Chirag Sakhuja, Milad Hashemi, Kevin Swersky, Calvin Lin
2020	A*	`conf`	Learning Execution through Neural Code fusion. ICLR Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi
2020	A*	`conf`	Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. ICLR Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle
2020	A*	`conf`	Neural Execution Engines: Learning to Execute Subroutines. NeurIPS Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi
2020	J	`jnl`	Neural Execution Engines: Learning to Execute Subroutines. CoRR Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi
2020	J	`jnl`	No MCMC for me: Amortized sampling for fast and stable training of energy-based models. CoRR Will Grathwohl, Jacob Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud
2020	A*	`conf`	Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. ICML Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier
2020	J	`jnl`	Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. CoRR Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier
2020	J	`jnl`	SentenceMIM: A Latent Variable Language Model. CoRR Micha Livne, Kevin Swersky, David J. Fleet
2020	A*	`conf`	Your classifier is secretly an energy based model and you should treat it like one. ICLR Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
2019	A*	`conf`	Flexibly Fair Representation Learning by Disentanglement. ICML Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel
2019	J	`jnl`	Flexibly Fair Representation Learning by Disentanglement. CoRR Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel
2019	A*	`conf`	Graph Normalizing Flows. NeurIPS Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky
2019	J	`jnl`	Graph Normalizing Flows. CoRR Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky
2019	J	`jnl`	High Mutual Information in Representation Learning with Symmetric Variational Inference. CoRR Micha Livne, Kevin Swersky, David J. Fleet
2019	J	`jnl`	Learning Execution through Neural Code Fusion. CoRR Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi
2019	J	`jnl`	Learning Sparse Networks Using Targeted Dropout. CoRR Aidan N. Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton
2019	J	`jnl`	MIM: Mutual Information Machine. CoRR Micha Livne, Kevin Swersky, David J. Fleet
2019	J	`jnl`	Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. CoRR Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle
2019	J	`jnl`	Neural Networks for Modeling Source Code Edits. CoRR Rui Zhao, David Bieber, Kevin Swersky, Daniel Tarlow
2019	J	`jnl`	Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One. CoRR Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
2018	Misc	`conf`	Learning Hard Alignments with Variational Inference. ICASSP Dieterich Lawson, Chung-Cheng Chiu, George Tucker, Colin Raffel, Kevin Swersky, Navdeep Jaitly
2018	A*	`conf`	Learning Memory Access Patterns. ICML Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan
2018	J	`jnl`	Learning Memory Access Patterns. CoRR Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan
2018	—	`conf`	Meta-Learning for Semi-Supervised Few-Shot Classification. ICLR (Poster) Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel
2018	J	`jnl`	Meta-Learning for Semi-Supervised Few-Shot Classification. CoRR Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel
2017	J	`jnl`	An online sequence-to-sequence model for noisy speech recognition. CoRR Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo, George Tucker, Kevin Swersky, Ilya Sutskever, Navdeep Jaitly
2017	—	`—`	Improving Bayesian Optimization for Machine Learning using Expert Priors. Kevin Swersky
2017	J	`jnl`	Learning Hard Alignments with Variational Inference. CoRR Dieterich Lawson, George Tucker, Chung-Cheng Chiu, Colin Raffel, Kevin Swersky, Navdeep Jaitly
2017	—	`conf`	Prototypical Networks for Few-shot Learning. NIPS Jake Snell, Kevin Swersky, Richard S. Zemel
2017	J	`jnl`	Prototypical Networks for Few-shot Learning. CoRR Jake Snell, Kevin Swersky, Richard S. Zemel
2016	J	`jnl`	Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, Nando de Freitas
2016	A*	`conf`	The Variational Fair Autoencoder. ICLR Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard S. Zemel
2015	A*	`conf`	Generative Moment Matching Networks. ICML Yujia Li, Kevin Swersky, Richard S. Zemel
2015	J	`jnl`	Generative Moment Matching Networks. CoRR Yujia Li, Kevin Swersky, Richard S. Zemel
2015	A*	`conf`	Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions. ICCV Lei Jimmy Ba, Kevin Swersky, Sanja Fidler, Ruslan Salakhutdinov
2015	J	`jnl`	Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions. CoRR Jimmy Ba, Kevin Swersky, Sanja Fidler, Ruslan Salakhutdinov
2015	A*	`conf`	Scalable Bayesian Optimization Using Deep Neural Networks. ICML Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams
2014	J	`jnl`	Freeze-Thaw Bayesian Optimization. CoRR Kevin Swersky, Jasper Snoek, Ryan Prescott Adams
2014	A*	`conf`	Input Warping for Bayesian Optimization of Non-Stationary Functions. ICML Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams
2014	J	`jnl`	Input Warping for Bayesian Optimization of Non-stationary Functions. CoRR Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams
2014	J	`jnl`	Learning unbiased features. CoRR Yujia Li, Kevin Swersky, Richard S. Zemel
2013	—	`conf`	Learning Fair Representations. ICML (3) Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork
2013	—	`conf`	Multi-Task Bayesian Optimization. NIPS Kevin Swersky, Jasper Snoek, Ryan Prescott Adams
2013	—	`conf`	Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. ICML (3) Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel
2012	—	`conf`	Cardinality Restricted Boltzmann Machines. NIPS Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams
2012	A*	`conf`	Estimating the Hessian by Back-propagating Curvature. ICML James Martens, Ilya Sutskever, Kevin Swersky
2012	J	`jnl`	Fast Exact Inference for Recursive Cardinality Models CoRR Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey
2012	A	`conf`	Fast Exact Inference for Recursive Cardinality Models. UAI Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey
2012	A*	`conf`	Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults. AAAI Michael A. Osborne, Roman Garnett, Kevin Swersky, Nando de Freitas
2012	—	`conf`	Probabilistic n-Choose-k Models for Classification and Ranking. NIPS Kevin Swersky, Daniel Tarlow, Ryan P. Adams, Richard S. Zemel, Brendan J. Frey
2011	A*	`conf`	On Autoencoders and Score Matching for Energy Based Models. ICML Kevin Swersky, Marc'Aurelio Ranzato, David Buchman, Benjamin M. Marlin, Nando de Freitas
2010	Misc	`conf`	A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets. ITA Kevin Swersky, Bo Chen, Benjamin M. Marlin, Nando de Freitas
2010	A	`conf`	Inductive Principles for Restricted Boltzmann Machine Learning. AISTATS Benjamin M. Marlin, Kevin Swersky, Bo Chen, Nando de Freitas