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cs.LG2026
Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches
Shirin Alanova, Kristina Kazistova, Ekaterina Galaeva +7
The demand for efficient large language model (LLM) inference has intensified the focus on sparsification techniques. While semi-structured (N:M) pruning is well-established for we…
cs.LG2026
Adversarial Samples Are Not Created Equal
Jennifer Crawford, Amol Khanna, Fred Lu +4
Over the past decade, numerous theories have been proposed to explain the widespread vulnerability of deep neural networks to adversarial evasion attacks. Among these, the theory o…