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09/11/2023
Uncovering mesa-optimization algorithms in Transformers
Transformers have become the dominant model in deep learning, but the re...
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07/21/2023
On the Universality of Linear Recurrences Followed by Nonlinear Projections
In this note (work in progress towards a full-length paper) we show that...
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07/18/2023
Promoting Exploration in Memory-Augmented Adam using Critical Momenta
Adaptive gradient-based optimizers, particularly Adam, have left their m...
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07/17/2023
Latent Space Representations of Neural Algorithmic Reasoners
Neural Algorithmic Reasoning (NAR) is a research area focused on designi...
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07/11/2023
Towards Robust and Efficient Continual Language Learning
As the application space of language models continues to evolve, a natur...
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06/27/2023
Asynchronous Algorithmic Alignment with Cocycles
State-of-the-art neural algorithmic reasoners make use of message passin...
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06/26/2023
Learning to Modulate pre-trained Models in RL
Reinforcement Learning (RL) has been successful in various domains like ...
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06/14/2023
Kalman Filter for Online Classification of Non-Stationary Data
In Online Continual Learning (OCL) a learning system receives a stream o...
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05/31/2023
The Tunnel Effect: Building Data Representations in Deep Neural Networks
Deep neural networks are widely known for their remarkable effectiveness...
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04/25/2023
Towards Compute-Optimal Transfer Learning
The field of transfer learning is undergoing a significant shift with th...
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03/11/2023
Resurrecting Recurrent Neural Networks for Long Sequences
Recurrent Neural Networks (RNNs) offer fast inference on long sequences ...
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03/02/2023
Understanding plasticity in neural networks
Plasticity, the ability of a neural network to quickly change its predic...
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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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09/28/2022
Disentangling Transfer in Continual Reinforcement Learning
The ability of continual learning systems to transfer knowledge from pre...
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07/05/2022
An Empirical Study of Implicit Regularization in Deep Offline RL
Deep neural networks are the most commonly used function approximators i...
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06/20/2022
When Does Re-initialization Work?
Re-initializing a neural network during training has been observed to im...
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05/31/2022
Pre-training via Denoising for Molecular Property Prediction
Many important problems involving molecular property prediction from 3D ...
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05/31/2022
The CLRS Algorithmic Reasoning Benchmark
Learning representations of algorithms is an emerging area of machine le...
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02/01/2022
Architecture Matters in Continual Learning
A large body of research in continual learning is devoted to overcoming ...
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01/13/2022
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
Despite recent progress made by self-supervised methods in representatio...
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10/21/2021
Wide Neural Networks Forget Less Catastrophically
A growing body of research in continual learning is devoted to overcomin...
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10/01/2021
Powerpropagation: A sparsity inducing weight reparameterisation
The training of sparse neural networks is becoming an increasingly impor...
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07/27/2021
On the Role of Optimization in Double Descent: A Least Squares Study
Empirically it has been observed that the performance of deep neural net...
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06/24/2021
Task-agnostic Continual Learning with Hybrid Probabilistic Models
Learning new tasks continuously without forgetting on a constantly chang...
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06/15/2021
Predicting Unreliable Predictions by Shattering a Neural Network
Piecewise linear neural networks can be split into subfunctions, each wi...
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06/07/2021
Top-KAST: Top-K Always Sparse Training
Sparse neural networks are becoming increasingly important as the field ...
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05/31/2021
A study on the plasticity of neural networks
One aim shared by multiple settings, such as continual learning or trans...
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05/27/2021
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error
In computer vision, it is standard practice to draw a single sample from...
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05/23/2021
Continual World: A Robotic Benchmark For Continual Reinforcement Learning
Continual learning (CL) – the ability to continuously learn, building on...
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05/11/2021
Spectral Normalisation for Deep Reinforcement Learning: an Optimisation Perspective
Most of the recent deep reinforcement learning advances take an RL-centr...
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03/17/2021
Regularized Behavior Value Estimation
Offline reinforcement learning restricts the learning process to rely on...
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10/27/2020
Behavior Priors for Efficient Reinforcement Learning
As we deploy reinforcement learning agents to solve increasingly challen...
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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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10/09/2020
Linear Mode Connectivity in Multitask and Continual Learning
Continual (sequential) training and multitask (simultaneous) training ar...
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06/12/2020
Understanding the Role of Training Regimes in Continual Learning
Catastrophic forgetting affects the training of neural networks, limitin...
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06/11/2020
Pointer Graph Networks
Graph neural networks (GNNs) are typically applied to static graphs that...
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12/16/2019
A Deep Neural Network's Loss Surface Contains Every Low-dimensional Pattern
The work "Loss Landscape Sightseeing with Multi-Point Optimization" (Sko...
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10/31/2019
Continual Unsupervised Representation Learning
Continual learning aims to improve the ability of modern learning system...
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10/22/2019
Improving the Gating Mechanism of Recurrent Neural Networks
Gating mechanisms are widely used in neural network models, where they a...
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10/13/2019
Stabilizing Transformers for Reinforcement Learning
Owing to their ability to both effectively integrate information over lo...
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08/30/2019
Meta-Learning with Warped Gradient Descent
A versatile and effective approach to meta-learning is to infer a gradie...
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06/12/2019
Task Agnostic Continual Learning via Meta Learning
While neural networks are powerful function approximators, they suffer f...
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05/08/2019
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for buildi...
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05/03/2019
Information asymmetry in KL-regularized RL
Many real world tasks exhibit rich structure that is repeated across dif...
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04/25/2019
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning
Rather than proposing a new method, this paper investigates an issue pre...
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03/18/2019
A RAD approach to deep mixture models
Flow based models such as Real NVP are an extremely powerful approach to...
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03/18/2019
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL
As reinforcement learning agents are tasked with solving more challengin...
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02/06/2019
Distilling Policy Distillation
The transfer of knowledge from one policy to another is an important too...
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01/31/2019
Functional Regularisation for Continual Learning using Gaussian Processes
We introduce a novel approach for supervised continual learning based on...
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12/05/2018
