231 citations · 337 across the 12 of their papers we have counts for
8 papers · 1 filter
Mining Generalizable Activation Functions
Alex Vitvitskyi, Michael Boratko, Matej Grcic +3
The choice of activation function is an active area of research, with different proposals aimed at improving optimization, while maintaining expressivity. Additionally, the activat…
What makes a good feedforward computational graph?
Alex Vitvitskyi, João G. M. Araújo, Marc Lackenby +1
As implied by the plethora of literature on graph rewiring, the choice of computational graph employed by a neural network can make a significant impact on its downstream performan…
Amplifying human performance in combinatorial competitive programming
Petar Veličković, Alex Vitvitskyi, Larisa Markeeva +4
Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors. While steady prog…
The CLRS-Text Algorithmic Reasoning Language Benchmark
Larisa Markeeva, Sean McLeish, Borja Ibarz +7
Eliciting reasoning capabilities from language models (LMs) is a critical direction on the path towards building intelligent systems. Most recent studies dedicated to reasoning foc…
A Generalist Neural Algorithmic Learner
Borja Ibarz, Vitaly Kurin, George Papamakarios +12
The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution. While recent years have seen a…
Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning
Víctor Campos, Pablo Sprechmann, Steven Hansen +5
Designing agents that acquire knowledge autonomously and use it to solve new tasks efficiently is an important challenge in reinforcement learning. Knowledge acquired during an uns…
Agent57: Outperforming the Atari Human Benchmark
Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski +4
Atari games have been a long-standing benchmark in the reinforcement learning (RL) community for the past decade. This benchmark was proposed to test general competency of RL algor…
Never Give Up: Learning Directed Exploration Strategies
Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi +8
We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memory-based intrinsic rewa…