101 citations · 276 across the 27 of their papers we have counts for
16 papers · 1 filter
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
Tim Seyde, Igor Gilitschenski, Wilko Schwarting +4
Reinforcement learning (RL) for continuous control typically employs distributions whose support covers the entire action space. In this work, we investigate the colloquially known…
Adversarial Training is Not Ready for Robot Learning
Mathias Lechner, Ramin Hasani, Radu Grosu +2
Adversarial training is an effective method to train deep learning models that are resilient to norm-bounded perturbations, with the cost of nominal performance drop. While adversa…
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy
Lucas Liebenwein, Cenk Baykal, Brandon Carter +2
Neural network pruning is a popular technique used to reduce the inference costs of modern, potentially overparameterized, networks. Starting from a pre-trained network, the proces…
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space
Wilko Schwarting, Tim Seyde, Igor Gilitschenski +4
Learning competitive behaviors in multi-agent settings such as racing requires long-term reasoning about potential adversarial interactions. This paper presents Deep Latent Competi…
DiffPD: Differentiable Projective Dynamics
Tao Du, Kui Wu, Pingchuan Ma +4
We present a novel, fast differentiable simulator for soft-body learning and control applications. Existing differentiable soft-body simulators can be classified into two categorie…
Deep Learning Meets Projective Clustering
Alaa Maalouf, Harry Lang, Daniela Rus +1
A common approach for compressing NLP networks is to encode the embedding layer as a matrix , compute its rank- approximation via SVD, and then…