223 citations · 389 across the 45 of their papers we have counts for
19 papers · 1 filter
Towards Cooperative Flight Control Using Visual-Attention
Lianhao Yin, Makram Chahine, Tsun-Hsuan Wang +5
The cooperation of a human pilot with an autonomous agent during flight control realizes parallel autonomy. We propose an air-guardian system that facilitates cooperation between a…
Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks
Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee +2
We study the problem of training and certifying adversarially robust quantized neural networks (QNNs). Quantization is a technique for making neural networks more efficient by runn…
Efficient Dataset Distillation Using Random Feature Approximation
Noel Loo, Ramin Hasani, Alexander Amini +1
Dataset distillation compresses large datasets into smaller synthetic coresets which retain performance with the aim of reducing the storage and computational burden of processing…
Evolution of Neural Tangent Kernels under Benign and Adversarial Training
Noel Loo, Ramin Hasani, Alexander Amini +1
Two key challenges facing modern deep learning are mitigating deep networks' vulnerability to adversarial attacks and understanding deep learning's generalization capabilities. Tow…
Pruning by Active Attention Manipulation
Zahra Babaiee, Lucas Liebenwein, Ramin Hasani +2
Filter pruning of a CNN is typically achieved by applying discrete masks on the CNN's filter weights or activation maps, post-training. Here, we present a new filter-importance-sco…
PyHopper -- Hyperparameter optimization
Mathias Lechner, Ramin Hasani, Philipp Neubauer +2
Hyperparameter tuning is a fundamental aspect of machine learning research. Setting up the infrastructure for systematic optimization of hyperparameters can take a significant amou…
Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap
Mathias Lechner, Ramin Hasani, Alexander Amini +3
There is an ever-growing zoo of modern neural network models that can efficiently learn end-to-end control from visual observations. These advanced deep models, ranging from convol…
Solving Continuous Control via Q-learning
Tim Seyde, Peter Werner, Wilko Schwarting +4
While there has been substantial success for solving continuous control with actor-critic methods, simpler critic-only methods such as Q-learning find limited application in the as…
Interpreting Neural Policies with Disentangled Tree Representations
Tsun-Hsuan Wang, Wei Xiao, Tim Seyde +2
The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning. Understanding how…
On the Forward Invariance of Neural ODEs
Wei Xiao, Tsun-Hsuan Wang, Ramin Hasani +4
We propose a new method to ensure neural ordinary differential equations (ODEs) satisfy output specifications by using invariance set propagation. Our approach uses a class of cont…
Liquid Structural State-Space Models
Ramin Hasani, Mathias Lechner, Tsun-Hsuan Wang +3
A proper parametrization of state transition matrices of linear state-space models (SSMs) followed by standard nonlinearities enables them to efficiently learn representations from…
Deep Learning on Home Drone: Searching for the Optimal Architecture
Alaa Maalouf, Yotam Gurfinkel, Barak Diker +3
We suggest the first system that runs real-time semantic segmentation via deep learning on a weak micro-computer such as the Raspberry Pi Zero v2 (whose price was $15) attached to…
Entangled Residual Mappings
Mathias Lechner, Ramin Hasani, Zahra Babaiee +4
Residual mappings have been shown to perform representation learning in the first layers and iterative feature refinement in higher layers. This interplay, combined with their stab…
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks
Ryan Sander, Wilko Schwarting, Tim Seyde +3
Experience replay plays a crucial role in improving the sample efficiency of deep reinforcement learning agents. Recent advances in experience replay propose using Mixup (Zhang et…
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
Zhijian Liu, Haotian Tang, Alexander Amini +4
Multi-sensor fusion is essential for an accurate and reliable autonomous driving system. Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with ca…
End-to-End Sensitivity-Based Filter Pruning
Zahra Babaiee, Lucas Liebenwein, Ramin Hasani +2
In this paper, we present a novel sensitivity-based filter pruning algorithm (SbF-Pruner) to learn the importance scores of filters of each layer end-to-end. Our method learns the…
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning
Mathias Lechner, Alexander Amini, Daniela Rus +1
Adversarial training (i.e., training on adversarially perturbed input data) is a well-studied method for making neural networks robust to potential adversarial attacks during infer…
Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving
Wei Xiao, Tsun-Hsuan Wang, Makram Chahine +3
Guaranteeing safety of perception-based learning systems is challenging due to the absence of ground-truth state information unlike in state-aware control scenarios. In this paper,…
Concept Graph Neural Networks for Surgical Video Understanding
Yutong Ban, Jennifer A. Eckhoff, Thomas M. Ward +4
We constantly integrate our knowledge and understanding of the world to enhance our interpretation of what we see. This ability is crucial in application domains which entail reaso…