Publications

Auxiliary Losses for Learning Generalizable Concept-based Models
Ivaxi Sheth
Samira Ebrahimi Kahou
openreview.net
Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images
Yufei Cui
Ziquan Liu
Xiangyu Liu
Xue (Steve) Liu
Cong Wang
Tei-Wei Kuo
Chun Jason Xue
Antoni B. Chan
Multiple instance learning (MIL) is a popular weakly-supervised learning model on the whole slide image (WSI) for AI-assisted pathology diag… (voir plus)nosis. The recent advance in attention-based MIL allows the model to find its region-of-interest (ROI) for interpretation by learning the attention weights for image patches of WSI slides. However, we empirically find that the interpretability of some related methods is either untrustworthy as the principle of MIL is violated or unsatisfactory as the high-attention regions are not consistent with experts’ annotations. In this paper, we propose Bayes-MIL to address the problem from a probabilistic perspective. The induced patch-level uncertainty is proposed as a new measure of MIL interpretability, which outperforms previous methods in matching doctors annotations. We design a slide-dependent patch regularizer (SDPR) for the attention, imposing constraints derived from the MIL assumption, on the attention distribution. SDPR explicitly constrains the model to generate correct attention values. The spatial information is further encoded by an approximate convolutional conditional random field (CRF), for better interpretability. Experimental results show Bayes-MIL outperforms the related methods in patch-level and slide-level metrics and provides much better interpretable ROI on several large-scale WSI datasets.
2023-01-01
ICLR (publié)
dblp.uni-trier.de
Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images
Yufei Cui
Ziquan Liu
Xiangyu Liu
Xue (Steve) Liu
Cong Wang
Tei-Wei Kuo
Chun Jason Xue
Antoni Bert Chan
Multiple instance learning (MIL) is a popular weakly-supervised learning model on the whole slide image (WSI) for AI-assisted pathology diag… (voir plus)nosis. The recent advance in attention-based MIL allows the model to find its region-of-interest (ROI) for interpretation by learning the attention weights for image patches of WSI slides. However, we empirically find that the interpretability of some related methods is either untrustworthy as the principle of MIL is violated or unsatisfactory as the high-attention regions are not consistent with experts’ annotations. In this paper, we propose Bayes-MIL to address the problem from a probabilistic perspective. The induced patch-level uncertainty is proposed as a new measure of MIL interpretability, which outperforms previous methods in matching doctors annotations. We design a slide-dependent patch regularizer (SDPR) for the attention, imposing constraints derived from the MIL assumption, on the attention distribution. SDPR explicitly constrains the model to generate correct attention values. The spatial information is further encoded by an approximate convolutional conditional random field (CRF), for better interpretability. Experimental results show Bayes-MIL outperforms the related methods in patch-level and slide-level metrics and provides much better interpretable ROI on several large-scale WSI datasets.
2023-01-01
International Conference on Learning Representations (published)
dblp.uni-trier.de
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 (publié)
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… (voir plus)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 (publié)
doi.org
openreview.net
Block-State Transformers
Mahan Fathi
Jonathan Pilault
Pierre-Luc Bacon
Chris Pal
Orhan Firat
Ross Goroshin
2023-01-01
NeurIPS (publié)
arxiv.org
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 … (voir plus)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 (publié)
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 (publié)
doi.org
Can AI Read the Minds of Corporate Executives?
Nicolas Chapados
Zhenzhen Fan
Ruslan Goyenko
Issam Hadj Laradji
Fred Liu
Chengyu Zhang
2023-01-01
SSRN Electronic Journal (publié)
doi.org
Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?
Shreya Chowdhary
Anna Kawakami
Jina Suh
Mary L Gray
Alexandra Olteanu
Koustuv Saha
2023-01-01
FAccT (publié)
doi.org
arxiv.org
Canada's Provincial Covid-19 Pandemic Modelling Efforts: A Review of Mathematical Models and Their Impacts on the Responses
Yiqing Xia
Jorge Luis Flores Anato
Caroline Colijin
Naveed Janjua
Michael Otterstatter
Mike Irvine
Tyler Williamson
Marie B. Varughese
Michael Li
Nathaniel Osgood
David J. D. Earn
Beate Sander
Lauren E. Cipriano
Kumar Murty
Fanyu Xiu
Arnaud Godin
David Buckeridge
Amy Hurford
Sharmistha Mishra
Mathieu Maheu-Giroux
2023-01-01
SSRN Electronic Journal (publié)
doi.org