223 citations · 475 across the 51 of their papers we have counts for
6 papers · 1 filter
Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control
Neehal Tumma, Mathias Lechner, Noel Loo +2
Developing autonomous agents that can interact with changing environments is an open challenge in machine learning. Robustness is particularly important in these settings as agents…
On the Size and Approximation Error of Distilled Sets
Alaa Maalouf, Murad Tukan, Noel Loo +3
Dataset Distillation is the task of synthesizing small datasets from large ones while still retaining comparable predictive accuracy to the original uncompressed dataset. Despite s…
Learning Stability Attention in Vision-based End-to-end Driving Policies
Tsun-Hsuan Wang, Wei Xiao, Makram Chahine +3
Modern end-to-end learning systems can learn to explicitly infer control from perception. However, it is difficult to guarantee stability and robustness for these systems since the…
Infrastructure-based End-to-End Learning and Prevention of Driver Failure
Noam Buckman, Shiva Sreeram, Mathias Lechner +4
Intelligent intersection managers can improve safety by detecting dangerous drivers or failure modes in autonomous vehicles, warning oncoming vehicles as they approach an intersect…
Dataset Distillation with Convexified Implicit Gradients
Noel Loo, Ramin Hasani, Mathias Lechner +1
We propose a new dataset distillation algorithm using reparameterization and convexification of implicit gradients (RCIG), that substantially improves the state-of-the-art. To this…
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation
Noel Loo, Ramin Hasani, Mathias Lechner +2
Modern deep learning requires large volumes of data, which could contain sensitive or private information that cannot be leaked. Recent work has shown for homogeneous neural networ…