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20242026
most citedSLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs

1 citations · 1 across the 6 of their papers we have counts for

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cs.LG2026

ROTE: Benchmarking Neural Memorization on Complexity-Controlled Symbolic Sequences

Xinye Chen, Stefan Güttel, Mohammad Mozaffari

We introduce ROTE (RollOut Testing of Exact memorization), a benchmarking protocol for evaluating symbolic memorization of neural architectures. We study memorization and the exten…

cs.AI2026

Compression Trinity: Exploring Sparsity, Quantization, and Low-Rank Approximations for LLM Compression

Mohammad Mozaffari

Prohibitive computational and environmental costs impede the scalable deployment of Large Language Models (LLMs). Traditional compression techniques (sparsity, quantization, low-ra…

cs.LG2026

LEAP: Learnable End-to-End Adaptive Pruning of Large Language Models

Mohammad Mozaffari, Younes Hourri, Mohammad Rastegari +1

Unstructured sparsity is now natively accelerated by recent GPU kernels and dataflow hardware, shifting the bottleneck from inference execution to the pruning algorithm. State-of-t…