Publications

Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Thomas Laurent
Anh Tuan Luu
Xavier Bresson
Benchmarking State-Merging Algorithms for Learning Regular Languages.
Adil Soubki
Jeffrey Heinz
François Coste
Faissal Ouardi
Bigger, Better, Faster: Human-level Atari with human-level efficiency
Max Schwarzer
Johan Samir Obando Ceron
Rishabh Agarwal
We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on sca… (see more)ling the neural networks used for value estimation, as well as a number of other design choices that enable this scaling in a sample-efficient manner. We conduct extensive analyses of these design choices and provide insights for future work. We end with a discussion about updating the goalposts for sample-efficient RL research on the ALE. We make our code and data publicly available at https://github.com/google-research/google-research/tree/master/bigger_better_faster.
Block-State Transformers
Mahan Fathi
Jonathan Pilault
Orhan Firat
Ross Goroshin
Bugs in the Data: How ImageNet Misrepresents Biodiversity
Alexandra Luccioni
ImageNet-1k is a dataset often used for benchmarking machine learning (ML) models and evaluating tasks such as image recognition and object … (see more)detection. Wild animals make up 27% of ImageNet-1k but, unlike classes representing people and objects, these data have not been closely scrutinized. In the current paper, we analyze the 13,450 images from 269 classes that represent wild animals in the ImageNet-1k validation set, with the participation of expert ecologists. We find that many of the classes are ill-defined or overlapping, and that 12% of the images are incorrectly labeled, with some classes having >90% of images incorrect. We also find that both the wildlife-related labels and images included in ImageNet-1k present significant geographical and cultural biases, as well as ambiguities such as artificial animals, multiple species in the same image, or the presence of humans. Our findings highlight serious issues with the extensive use of this dataset for evaluating ML systems, the use of such algorithms in wildlife-related tasks, and more broadly the ways in which ML datasets are commonly created and curated.
Cache-Efficient Dynamic Programming MDP Solver
Jaël Champagne Gareau
Guillaume Gosset
Éric Beaudry
Can Forward Gradient Match Backpropagation?
Louis Fournier
Stephane Rivaud
Michael Eickenberg
Edouard Oyallon
Forward Gradients - the idea of using directional derivatives in forward differentiation mode - have recently been shown to be utilizable fo… (see more)r neural network training while avoiding problems generally associated with backpropagation gradient computation, such as locking and memorization requirements. The cost is the requirement to guess the step direction, which is hard in high dimensions. While current solutions rely on weighted averages over isotropic guess vector distributions, we propose to strongly bias our gradient guesses in directions that are much more promising, such as feedback obtained from small, local auxiliary networks. For a standard computer vision neural network, we conduct a rigorous study systematically covering a variety of combinations of gradient targets and gradient guesses, including those previously presented in the literature. We find that using gradients obtained from a local loss as a candidate direction drastically improves on random noise in Forward Gradient methods.
Can AI Read the Minds of Corporate Executives?
Zhenzhen Fan
Ruslan Goyenko
Issam Hadj Laradji
Fred Liu
Chengyu Zhang
Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?
Shreya Chowdhary
Anna Kawakami
Jina Suh
Mary L Gray
Koustuv Saha
A circulating proteome-informed prognostic model of COVID-19 disease activity that relies on 1 routinely available clinical laboratories 2
William Ma
Antoine Soulé
Karine Tremblay
Simon Rousseau
Abstract
Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport
Alexander Tong
Nikolay Malkin
Guillaume Huguet
Yanlei Zhang
Jarrid Rector-Brooks
Kilian FATRAS
Constant Memory Attentive Neural Processes
Leo Feng
Frederick Tung
Hossein Hajimirsadeghi
Mohamed Osama Ahmed