250 citations · 532 across the 72 of their papers we have counts for
7 papers · 2 filters
ETAGE: Enhanced Test Time Adaptation with Integrated Entropy and Gradient Norms for Robust Model Performance
Afshar Shamsi, Rejisa Becirovic, Ahmadreza Argha +3
Test time adaptation (TTA) equips deep learning models to handle unseen test data that deviates from the training distribution, even when source data is inaccessible. While traditi…
Rethinking State Disentanglement in Causal Reinforcement Learning
Haiyao Cao, Zhen Zhang, Panpan Cai +7
One of the significant challenges in reinforcement learning (RL) when dealing with noise is estimating latent states from observations. Causality provides rigorous theoretical supp…
Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks
Bao Gia Doan, Afshar Shamsi, Xiao-Yu Guo +6
Computational complexity of Bayesian learning is impeding its adoption in practical, large-scale tasks. Despite demonstrations of significant merits such as improved robustness and…
Knowledge Composition using Task Vectors with Learned Anisotropic Scaling
Frederic Z. Zhang, Paul Albert, Cristian Rodriguez-Opazo +2
Pre-trained models produce strong generic representations that can be adapted via fine-tuning. The learned weight difference relative to the pre-trained model, known as a task vect…
BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack
Viet Quoc Vo, Ehsan Abbasnejad, Damith C. Ranasinghe
We study the unique, less-well understood problem of generating sparse adversarial samples simply by observing the score-based replies to model queries. Sparse attacks aim to disco…
Do Deep Neural Network Solutions Form a Star Domain?
Ankit Sonthalia, Alexander Rubinstein, Ehsan Abbasnejad +1
It has recently been conjectured that neural network solution sets reachable via stochastic gradient descent (SGD) are convex, considering permutation invariances (Entezari et al.,…