4 papers
T-MLA: A targeted multiscale log-exponential attack framework for neural image compression
Nikolay I. Kalmykov, Razan Dibo, Kaiyu Shen +4
Neural image compression (NIC) has become the state-of-the-art for rate-distortion performance, yet its security vulnerabilities remain significantly less understood than those of…
Global Optimization of Atomic Clusters via Physically-Constrained Tensor Train Decomposition
Konstantin Sozykin, Nikita Rybin, Andrei Chertkov +5
The global optimization of atomic clusters represents a fundamental challenge in computational chemistry and materials science due to the exponential growth of local minima with sy…
GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling
Ashish Jha, Anh huy Phan, Razan Dibo +1
Training modern neural networks on large datasets is computationally and environmentally costly. We introduce GRAFT, a scalable in-training subset selection method that (i) extract…
High-dimensional Optimization with Low Rank Tensor Sampling and Local Search
Konstantin Sozykin, Andrei Chertkov, Anh-Huy Phan +2
We present a novel method called TESALOCS (TEnsor SAmpling and LOCal Search) for multidimensional optimization, combining the strengths of gradient-free discrete methods and gradie…