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05/17/2023
Provably Correct Physics-Informed Neural Networks
Recent work provides promising evidence that Physics-informed neural net...
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05/16/2023
Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right?
We revisit the common practice of evaluating adaptation of Online Contin...
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04/25/2023
Certifying Ensembles: A General Certification Theory with S-Lipschitzness
Improving and guaranteeing the robustness of deep learning models has be...
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03/23/2023
Don't FREAK Out: A Frequency-Inspired Approach to Detecting Backdoor Poisoned Samples in DNNs
In this paper we investigate the frequency sensitivity of Deep Neural Ne...
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03/20/2023
Computationally Budgeted Continual Learning: What Does Matter?
Continual Learning (CL) aims to sequentially train models on streams of ...
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02/02/2023
Real-Time Evaluation in Online Continual Learning: A New Paradigm
Current evaluations of Continual Learning (CL) methods typically assume ...
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11/29/2022
SimCS: Simulation for Online Domain-Incremental Continual Segmentation
Continual Learning is a step towards lifelong intelligence where models ...
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09/26/2022
Diversified Dynamic Routing for Vision Tasks
Deep learning models for vision tasks are trained on large datasets unde...
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07/20/2022
Illusionary Attacks on Sequential Decision Makers and Countermeasures
Autonomous intelligent agents deployed to the real-world need to be robu...
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06/16/2022
Catastrophic overfitting is a bug but also a feature
Despite clear computational advantages in building robust neural network...
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02/02/2022
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
Recently, Wong et al. showed that adversarial training with single-step ...
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07/09/2021
ANCER: Anisotropic Certification via Sample-wise Volume Maximization
Randomized smoothing has recently emerged as an effective tool that enab...
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07/02/2021
DeformRS: Certifying Input Deformations with Randomized Smoothing
Deep neural networks are vulnerable to input deformations in the form of...
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03/26/2021
Combating Adversaries with Anti-Adversaries
Deep neural networks are vulnerable to small input perturbations known a...
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12/08/2020
Data Dependent Randomized Smoothing
Randomized smoothing is a recent technique that achieves state-of-art pe...
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06/21/2020
Network Moments: Extensions and Sparse-Smooth Attacks
The impressive performance of deep neural networks (DNNs) has immensely ...
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06/13/2020
ClustTR: Clustering Training for Robustness
This paper studies how encouraging semantically-aligned features during ...
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02/20/2020
On the Decision Boundaries of Deep Neural Networks: A Tropical Geometry Perspective
This work tackles the problem of characterizing and understanding the de...
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12/11/2019
Robust Gabor Networks
This work takes a step towards investigating the benefits of merging cla...
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07/24/2019
Constrained K-means with General Pairwise and Cardinality Constraints
In this work, we study constrained clustering, where constraints are uti...
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05/28/2019
Probabilistically True and Tight Bounds for Robust Deep Neural Network Training
Training Deep Neural Networks (DNNs) that are robust to norm bounded adv...
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04/24/2019
Analytical Moment Regularizer for Gaussian Robust Networks
Despite the impressive performance of deep neural networks (DNNs) on num...
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03/28/2018
