Pierre H. Richemond

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All published works

Action	Title	Year	Authors
+	SemPPL: Predicting pseudo-labels for better contrastive representations	2023	Matko Bošnjak Pierre H. Richemond Nenad Tomašev Florian Strub Jacob C. Walker Felix Hill Lars Buesing Razvan Pascanu Charles Blundell Jovana Mitrovic
+	The Edge of Orthogonality: A Simple View of What Makes BYOL Tick	2023	Pierre H. Richemond Allison Tam Yunhao Tang Florian Strub Bilal Piot Felix Hill
+	Zipfian environments for Reinforcement Learning	2022	Stephanie C. Y. Chan Andrew K. Lampinen Pierre H. Richemond Felix Hill
+	Data Distributional Properties Drive Emergent In-Context Learning in Transformers	2022	Stephanie C. Y. Chan Adam Santoro Andrew K. Lampinen Jane X. Wang Aaditya Singh Pierre H. Richemond Jay McClelland Felix Hill
+	Categorical SDEs with Simplex Diffusion	2022	Pierre H. Richemond Sander Dieleman Arnaud Doucet
+	Continuous diffusion for categorical data	2022	Sander Dieleman Laurent Sartran Arman Roshannai Nikolay Savinov Yaroslav Ganin Pierre H. Richemond Arnaud Doucet Robin Strudel Chris Dyer Conor Durkan
+	BYOL works even without batch statistics.	2020	Pierre H. Richemond Jean-Bastien Grill Florent Altché Corentin Tallec Florian Strub Andrew Brock Samuel Smith Soham De Razvan Pascanu Bilal Piot
+	Bootstrap your own latent: A new approach to self-supervised Learning	2020	Jean-Bastien Grill Florian Strub Florent Altché Corentin Tallec Pierre H. Richemond Elena Buchatskaya Carl Doersch Bernardo Ávila Pires Zhaohan Daniel Guo Mohammad Gheshlaghi Azar
+	BYOL works even without batch statistics	2020	Pierre H. Richemond Jean-Bastien Grill Florent Altché Corentin Tallec Florian Strub Andrew Brock Samuel Smith Soham De Razvan Pascanu Bilal Piot
+ PDF Chat	Biologically inspired architectures for sample-efficient deep reinforcement learning	2019	Pierre H. Richemond Arinbjörn Kolbeinsson Yike Guo
+	How many weights are enough : can tensor factorization learn efficient policies ?	2019	Pierre H. Richemond Arinbjörn Kolbeinsson Yike Guo
+	Static Activation Function Normalization.	2019	Pierre H. Richemond Yike Guo
+	Combining learning rate decay and weight decay with complexity gradient descent - Part I	2019	Pierre H. Richemond Yike Guo
+	Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control	2019	Marta Sarrico Kai Arulkumaran Andrea Agostinelli Pierre H. Richemond Anil A. Bharath
+	Biologically inspired architectures for sample-efficient deep reinforcement learning	2019	Pierre H. Richemond Arinbjörn Kolbeinsson Yike Guo
+	Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means	2019	Andrea Agostinelli Kai Arulkumaran Marta Sarrico Pierre H. Richemond Anil A. Bharath
+	Static Activation Function Normalization	2019	Pierre H. Richemond Yike Guo
+	Combining learning rate decay and weight decay with complexity gradient descent - Part I	2019	Pierre H. Richemond Yike Guo
+	Diffusing Policies : Towards Wasserstein Policy Gradient Flows	2018	Pierre H. Richemond Brendan Maginnis
+	A short variational proof of equivalence between policy gradients and soft Q learning	2017	Pierre H. Richemond Brendan Maginnis
+	Efficiently applying attention to sequential data with the Recurrent Discounted Attention unit	2017	Brendan Maginnis Pierre H. Richemond
+	On Wasserstein Reinforcement Learning and the Fokker-Planck equation	2017	Pierre H. Richemond Brendan Maginnis
+	A short variational proof of equivalence between policy gradients and soft Q learning	2017	Pierre H. Richemond Brendan Maginnis

Common Coauthors

Coauthor	Papers Together
Yike Guo	7
Florian Strub	5
Brendan Maginnis	5
Felix Hill	4
Bilal Piot	4
Florent Altché	3
Michal Vaľko	3
Arinbjörn Kolbeinsson	3
Jean-Bastien Grill	3
Razvan Pascanu	3
Arnaud Doucet	2
Marta Sarrico	2
Andrew Brock	2
Anil A. Bharath	2
Andrew K. Lampinen	2
Sander Dieleman	2
Soham De	2
Kai Arulkumaran	2
Stephanie C. Y. Chan	2
Corentin Tallec	2
Andrea Agostinelli	2
Arman Roshannai	1
Mohammad Gheshlaghi Azar	1
Elena Buchatskaya	1
Nikolay Savinov	1
Jacob C. Walker	1
Rémi Leblond	1
Lars Buesing	1
Laurent Sartran	1
Matko Bošnjak	1
Jane X. Wang	1
Nenad Tomašev	1
Jovana Mitrovic	1
Samuel Smith	1
Yaroslav Ganin	1
Conor Durkan	1
Carl Doersch	1
Curtis Hawthorne	1
Jonas Adler	1
Zhaohan Daniel Guo	1
Koray Kavukcuoglu	1
Yunhao Tang	1
Chris Dyer	1
Aaditya Singh	1
Charles Blundell	1
Rémi Munos	1
Jay McClelland	1
Samuel Smith	1
Robin Strudel	1
Adam Santoro	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Model-Free Episodic Control	2016	Charles Blundell Benigno Uría Alexander Pritzel Yazhe Li Avraham Ruderman Joel Z. Leibo Jack W. Rae Daan Wierstra Demis Hassabis	3
+	Big Self-Supervised Models are Strong Semi-Supervised Learners	2020	Ting Chen Simon Kornblith Kevin Swersky Mohammad Norouzi Geoffrey E. Hinton	2
+	Deep Relaxation: partial differential equations for optimizing deep neural networks	2017	Pratik Chaudhari Adam M. Oberman Stanley Osher Stefano Soatto Guillaume Carlier	2
+	Asynchronous Methods for Deep Reinforcement Learning	2016	Volodymyr Mnih Adrià Puigdomènech Badia Mehdi Mirza Alex Graves Timothy Lillicrap Tim Harley David Silver Koray Kavukcuoglu	2
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	2
+	Improved Baselines with Momentum Contrastive Learning	2020	Xinlei Chen Haoqi Fan Ross Girshick Kaiming He	2
+	A unified view of entropy-regularized Markov decision processes	2017	Gergely Neu Anders Jönsson Vicenç Gómez	2
+ PDF Chat	Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification	2015	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	2
+	Trust Region Policy Optimization	2015	John Schulman Sergey Levine Philipp Moritz Michael I. Jordan Pieter Abbeel	2
+	Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour	2017	Priya Goyal Piotr Dollár Ross Girshick Pieter Noordhuis Lukasz Wesolowski Aapo Kyrola Andrew Tulloch Yangqing Jia Kaiming He	2
+	Deep reinforcement learning with double Q-Learning	2016	Hado van Hasselt Arthur Guez David Silver	2
+	Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift	2015	Sergey Ioffe Christian Szegedy	2
+	Scaling SGD Batch Size to 32K for ImageNet Training.	2017	Yang You Igor Gitman Boris Ginsburg	2
+ PDF Chat	Deep Clustering for Unsupervised Learning of Visual Features	2018	Mathilde Caron Piotr Bojanowski Armand Joulin Matthijs Douze	2
+	Representation Learning with Contrastive Predictive Coding	2018	Aäron van den Oord Yazhe Li Oriol Vinyals	2
+	Large Batch Training of Convolutional Networks	2017	Yang You Igor Gitman Boris Ginsburg	2
+ PDF Chat	Building machines that learn and think like people	2016	Brenden M. Lake Tomer Ullman Joshua B. Tenenbaum Samuel J. Gershman	2
+ PDF Chat	The Arcade Learning Environment: An Evaluation Platform for General Agents	2013	Marc G. Bellemare Yavar Naddaf Joel Veness Michael Bowling	2
+ PDF Chat	Statistics of Critical Points of Gaussian Fields on Large-Dimensional Spaces	2007	Alan J. Bray David S. Dean	1
+	Continuous control with deep reinforcement learning	2015	Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel Nicolas Heess Tom Erez Yuval Tassa David Silver Daan Wierstra	1
+ PDF Chat	Group Invariant Scattering	2012	Stéphane Mallat	1
+	Asymptotic analysis of the exponential penalty trajectory in linear programming	1994	Roberto Cominetti Jaime San Martı́n	1
+	Identifying and attacking the saddle point problem in high-dimensional non-convex optimization	2014	Yann Dauphin Razvan Pascanu Çaǧlar Gülçehre Kyunghyun Cho Surya Ganguli Yoshua Bengio	1
+	SGDR: Stochastic Gradient Descent with Warm Restarts	2016	Ilya Loshchilov Frank Hutter	1
+	Discriminative Unsupervised Feature Learning with Convolutional Neural Networks	2014	Alexey Dosovitskiy Jost Tobias Springenberg Martin Riedmiller Thomas Brox	1
+	Optimizing Neural Networks with Kronecker-factored Approximate Curvature	2015	James Martens Roger Grosse	1
+	Learning with Pseudo-Ensembles	2014	Phil Bachman Ouais Alsharif Doina Precup	1
+	Stochastic Backpropagation and Approximate Inference in Deep Generative Models	2014	Danilo Jimenez Rezende Shakir Mohamed Daan Wierstra	1
+ PDF Chat	Rethinking the Inception Architecture for Computer Vision	2016	Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jon Shlens Zbigniew Wojna	1
+	On the difficulty of training Recurrent Neural Networks	2012	Razvan Pascanu Tomáš Mikolov Yoshua Bengio	1
+ PDF Chat	Statistical Applications of the Multivariate Skew Normal Distribution	1999	Adelchi Azzalini Antonella Capitanio	1
+ PDF Chat	ImageNet Large Scale Visual Recognition Challenge	2015	Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael S. Bernstein	1
+ PDF Chat	Fully convolutional networks for semantic segmentation	2015	Jonathan Long Evan Shelhamer Trevor Darrell	1
+	Semi-supervised Learning with Deep Generative Models	2014	Diederik P. Kingma Shakir Mohamed Danilo Jimenez Rezende Max Welling	1
+ PDF Chat	Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation	2014	Ross Girshick Jeff Donahue Trevor Darrell Jitendra Malik	1
+	Free Probability and Random Matrices	2017	James A. Mingo Roland Speicher	1
+	Generating Sequences With Recurrent Neural Networks	2013	Alex Graves	1
+	Optimal Transport for Applied Mathematicians : Calculus of Variations, PDEs, and Modeling	2015	Filippo Santambrogio	1
+	Wide Residual Networks	2016	Sergey Zagoruyko Nikos Komodakis	1
+	Taming the Noise in Reinforcement Learning via Soft Updates	2015	Roy Fox Ari Pakman Naftali Tishby	1
+	Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations	2016	David Krueger Tegan Maharaj János Kramár Mohammad Pezeshki Nicolas Ballas Nan Rosemary Ke Anirudh Goyal Yoshua Bengio Aaron Courville Chris Pal	1
+	Adversarially Learned Inference	2016	Vincent Dumoulin Ishmael Belghazi Ben Poole Olivier Mastropietro Alex Lamb Martín Arjovsky Aaron Courville	1
+	Adversarial Feature Learning	2016	Jeff Donahue Philipp Krähenbühl Trevor Darrell	1
+	Adaptive Computation Time for Recurrent Neural Networks	2016	Alex Graves	1
+ PDF Chat	Linear readout of object manifolds	2016	SueYeon Chung Daniel D. Lee Haim Sompolinsky	1
+	Instance Normalization: The Missing Ingredient for Fast Stylization	2016	Dmitry Ulyanov Andrea Vedaldi Victor Lempitsky	1
+	Algorithms for Minimization Without Derivatives	1974	G. Giftson Samuel Richard P. Brent	1
+	TensorLy: tensor learning in python	2019	Jean Kossaifi Yannis Panagakis Anima Anandkumar Maja Pantić	1
+	Combining policy gradient and Q-learning	2016	Brendan O’Donoghue Rémi Munos Koray Kavukcuoglu Volodymyr Mnih	1
+ PDF Chat	Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles	2016	Mehdi Noroozi Paolo Favaro	1