Publications

Benchmarking Graph Neural Networks

Vijay Prakash Dwivedi

Chaitanya K. Joshi

Thomas Laurent

Anh Tuan Luu

Yoshua Bengio

Xavier Bresson

arxiv.org

Benchmarking State-Merging Algorithms for Learning Regular Languages.

Adil Soubki

Jeffrey Heinz

François Coste

Faissal Ouardi

Guillaume Rabusseau

2023-01-01

International Conference on Graphics and Interaction (published)

dblp.uni-trier.de

Bigger, Better, Faster: Human-level Atari with human-level efficiency

Max Schwarzer

Johan Samir Obando Ceron

Aaron Courville

Marc Gendron-Bellemare

Rishabh Agarwal

Pablo Samuel Castro

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.

2023-01-01

ICML (published)

doi.org

openreview.net

Block-State Transformers

Mahan Fathi

Jonathan Pilault

Pierre-Luc Bacon

Chris Pal

Orhan Firat

Ross Goroshin

2023-01-01

NeurIPS (published)

arxiv.org

Bugs in the Data: How ImageNet Misrepresents Biodiversity

Alexandra Luccioni

David Rolnick

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.

2023-01-01

AAAI (published)

doi.org

arxiv.org

Cache-Efficient Dynamic Programming MDP Solver

Jaël Champagne Gareau

Guillaume Gosset

Éric Beaudry

Vladimir Makarenkov

2023-01-01

European Conference on Artificial Intelligence (published)

doi.org