Causal reasoning is a crucial part of science and human intelligence. In order to discover
causal relationships from data, we need structure discovery methods. We provide a review …

STABLE-BASELINES3 provides open-source implementations of deep reinforcement
learning (RL) algorithms in Python. The implementations have been benchmarked against …

While reinforcement learning (RL) achieves tremendous success in sequential decision-
making problems of many domains, it still faces key challenges of data inefficiency and the …

Today's scene graph generation (SGG) task is still far from practical, mainly due to the
severe training bias, eg, collapsing diverse" human walk on/sit on/lay on beach" into" human …

We present a novel unsupervised feature representation learning method, Visual
Commonsense Region-based Convolutional Neural Network (VC R-CNN), to serve as an …

As a pivotal component to attaining generalizable solutions in human intelligence,
reasoning provides great potential for reinforcement learning (RL) agents' generalization …

Forecasting human trajectories in complex dynamic environments plays a critical role in
autonomous vehicles and intelligent robots. Most existing methods learn to predict future …

This paper unravels the design tricks adopted by us, the champion team MReaL-BDAI, for
Visual Dialog Challenge 2019: two causal principles for improving Visual Dialog (VisDial) …

What can be learned about causality and experimentation from passive data? This question
is salient given recent successes of passively-trained language models in interactive …

This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning
method under appropriate supervised information. Unlike existing disentanglement methods …