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05/23/2023
On the Size and Approximation Error of Distilled Sets
Dataset Distillation is the task of synthesizing small datasets from lar...
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03/21/2023
Infrastructure-based End-to-End Learning and Prevention of Driver Failure
Intelligent intersection managers can improve safety by detecting danger...
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02/13/2023
Dataset Distillation with Convexified Implicit Gradients
We propose a new dataset distillation algorithm using reparameterization...
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02/02/2023
Dataset Distillation Fixes Dataset Reconstruction Attacks
Modern deep learning requires large volumes of data, which could contain...
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12/21/2022
Cooperative Flight Control Using Visual-Attention – Air-Guardian
The cooperation of a human pilot with an autonomous agent during flight ...
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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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10/20/2022
Pruning by Active Attention Manipulation
Filter pruning of a CNN is typically achieved by applying discrete masks...
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10/10/2022
PyHopper – Hyperparameter optimization
Hyperparameter tuning is a fundamental aspect of machine learning resear...
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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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06/02/2022
Entangled Residual Mappings
Residual mappings have been shown to perform representation learning in ...
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04/15/2022
End-to-End Sensitivity-Based Filter Pruning
In this paper, we present a novel sensitivity-based filter pruning algor...
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10/14/2021
Interactive Analysis of CNN Robustness
While convolutional neural networks (CNNs) have found wide adoption as s...
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07/18/2021
GoTube: Scalable Stochastic Verification of Continuous-Depth Models
We introduce a new stochastic verification algorithm that formally quant...
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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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06/13/2021
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
Robustness to variations in lighting conditions is a key objective for a...
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03/15/2021
Adversarial Training is Not Ready for Robot Learning
Adversarial training is an effective method to train deep learning model...
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03/08/2021
Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars
Despite the rich theoretical foundation of model-based deep reinforcemen...
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12/16/2020
On The Verification of Neural ODEs with Stochastic Guarantees
We show that Neural ODEs, an emerging class of time-continuous neural ne...
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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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06/08/2020
