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02/09/2023
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick
Self-predictive unsupervised learning methods such as BYOL or SimSiam ha...
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01/12/2023
SemPPL: Predicting pseudo-labels for better contrastive representations
Learning from large amounts of unsupervised data and a small amount of s...
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11/28/2022
Continuous diffusion for categorical data
Diffusion models have quickly become the go-to paradigm for generative m...
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10/26/2022
Categorical SDEs with Simplex Diffusion
Diffusion models typically operate in the standard framework of generati...
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03/15/2022
Zipfian environments for Reinforcement Learning
As humans and animals learn in the natural world, they encounter distrib...
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10/20/2020
BYOL works even without batch statistics
Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach ...
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06/13/2020
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-su...
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11/25/2019
Biologically inspired architectures for sample-efficient deep reinforcement learning
Deep reinforcement learning requires a heavy price in terms of sample ef...
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05/03/2019
Static Activation Function Normalization
Recent seminal work at the intersection of deep neural networks practice...
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02/07/2019
Combining learning rate decay and weight decay with complexity gradient descent - Part I
The role of L^2 regularization, in the specific case of deep neural netw...
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12/22/2017
A short variational proof of equivalence between policy gradients and soft Q learning
Two main families of reinforcement learning algorithms, Q-learning and p...
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12/19/2017
