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08/01/2023
Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks
The modern pervasiveness of large-scale deep neural networks (NNs) is dr...
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10/21/2022
Efficient Dataset Distillation Using Random Feature Approximation
Dataset distillation compresses large datasets into smaller synthetic co...
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10/21/2022
Evolution of Neural Tangent Kernels under Benign and Adversarial Training
Two key challenges facing modern deep learning are mitigating deep netwo...
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10/09/2022
Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap
There is an ever-growing zoo of modern neural network models that can ef...
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09/26/2022
Liquid Structural State-Space Models
A proper parametrization of state transition matrices of linear state-sp...
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05/26/2022
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
Multi-sensor fusion is essential for an accurate and reliable autonomous...
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04/15/2022
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning
Adversarial training (i.e., training on adversarially perturbed input da...
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11/23/2021
Learning Interactive Driving Policies via Data-driven Simulation
Data-driven simulators promise high data-efficiency for driving policy l...
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11/23/2021
VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles
Simulation has the potential to transform the development of robust algo...
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06/25/2021
Closed-form Continuous-Depth Models
Continuous-depth neural models, where the derivative of the model's hidd...
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06/24/2021
Sparse Flows: Pruning Continuous-depth Models
Continuous deep learning architectures enable learning of flexible proba...
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06/15/2021
Causal Navigation by Continuous-time Neural Networks
Imitation learning enables high-fidelity, vision-based learning of polic...
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05/20/2021
Efficient and Robust LiDAR-Based End-to-End Navigation
Deep learning has been used to demonstrate end-to-end neural network lea...
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06/08/2020
Liquid Time-constant Networks
We introduce a new class of time-continuous recurrent neural network mod...
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10/07/2019
Deep Evidential Regression
Deterministic neural networks (NNs) are increasingly being deployed in s...
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11/25/2018
Variational End-to-End Navigation and Localization
Deep learning has revolutionized the ability to learn "end-to-end" auton...
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11/01/2018
Liquid Time-constant Recurrent Neural Networks as Universal Approximators
In this paper, we introduce the notion of liquid time-constant (LTC) rec...
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09/11/2018
Re-purposing Compact Neuronal Circuit Policies to Govern Reinforcement Learning Tasks
We propose an effective method for creating interpretable control agents...
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09/11/2018
Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks
In this paper, we introduce a novel method to interpret recurrent neural...
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05/13/2018
Spatial Uncertainty Sampling for End-to-End Control
End-to-end trained neural networks (NNs) are a compelling approach to au...
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12/28/2016
