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Poster
Mon 1:00 What Makes Instance Discrimination Good for Transfer Learning?
Nanxuan Zhao · Zhirong Wu · Rynson W Lau · Stephen Lin
Poster
Mon 1:00 Training with Quantization Noise for Extreme Model Compression
Pierre Stock · Angela Fan · Benjamin Graham · Edouard Grave · Rémi Gribonval · Hervé Jégou · Armand Joulin
Poster
Mon 1:00 Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability
Suraj Srinivas · François Fleuret
Poster
Mon 1:00 Batch Reinforcement Learning Through Continuation Method
Yijie Guo · Shengyu Feng · Nicolas Le Roux · Ed H. Chi · Honglak Lee · Minmin Chen
Poster
Mon 1:00 Scalable Transfer Learning with Expert Models
Joan Puigcerver i Perez · Carlos Riquelme · Basil Mustafa · Cedric Renggli · André Susano Pinto · Sylvain Gelly · Daniel Keysers · Neil Houlsby
Poster
Mon 1:00 Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets
Hayeon Lee · Eunyoung Hyung · Sung Ju Hwang
Poster
Mon 1:00 Semantic Re-tuning with Contrastive Tension
Fredrik Carlsson · Amaru C Gyllensten · Evangelia Gogoulou · Erik Y Hellqvist · Magnus Sahlgren
Poster
Mon 1:00 Towards Impartial Multi-task Learning
Liyang Liu · Yi Li · Zhanghui Kuang · Jing-Hao Xue · Yimin Chen · Wenming Yang · Qingmin Liao · Wei Zhang
Poster
Mon 1:00 Overfitting for Fun and Profit: Instance-Adaptive Data Compression
Ties van Rozendaal · Iris Huijben · Taco Cohen
Poster
Mon 1:00 Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs
Pim De Haan · Maurice Weiler · Taco Cohen · Max Welling
Oral
Mon 5:30 Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability
Suraj Srinivas · François Fleuret
Invited Talk
Mon 8:00 Moving beyond the fairness rhetoric in machine learning
Timnit Gebru