1.2k citations · 2.1k across the 51 of their papers we have counts for
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On Pitfalls of Test-Time Adaptation
Hao Zhao, Yuejiang Liu, Alexandre Alahi +1
Test-Time Adaptation (TTA) has recently emerged as a promising approach for tackling the robustness challenge under distribution shifts. However, the lack of consistent settings an…
Predicting the long-term collective behaviour of fish pairs with deep learning
Vaios Papaspyros, Ramón Escobedo, Alexandre Alahi +3
Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collectiv…
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning
Yuejiang Liu, Alexandre Alahi, Chris Russell +4
Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions. Yet, existing efforts are largely limited t…
DriverGym: Democratising Reinforcement Learning for Autonomous Driving
Parth Kothari, Christian Perone, Luca Bergamini +2
Despite promising progress in reinforcement learning (RL), developing algorithms for autonomous driving (AD) remains challenging: one of the critical issues being the absence of an…
Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective
Yuejiang Liu, Riccardo Cadei, Jonas Schweizer +2
Learning behavioral patterns from observational data has been a de-facto approach to motion forecasting. Yet, the current paradigm suffers from two shortcomings: brittle under dist…
Social NCE: Contrastive Learning of Socially-aware Motion Representations
Yuejiang Liu, Qi Yan, Alexandre Alahi
Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite p…
Collaborative Sampling in Generative Adversarial Networks
Yuejiang Liu, Parth Kothari, Alexandre Alahi
The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the…