2 citations · 2 across the 9 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.LG2026
Interdomain Attention: Beyond Token-Level Key-Value Memory
Naoki Kiyohara, Harrison Bo Hua Zhu, Riccardo El Hassanin +4
Softmax attention-based transformers and recurrent memory-based models (e.g., state space models (SSMs)) form the foundation of modern sequence models. Hybrid architectures aim to…
cs.AI2026
Saliency-Aware Regularized Quantization Calibration for Large Language Models
Yanlong Zhao, Xiaoyuan Cheng, Huihang Liu +6
Post-training quantization (PTQ) is an effective approach for deploying large language models (LLMs) under memory and latency constraints. Most existing PTQ methods determine quant…
cs.LG2026
Probabilistic Learning and Generation in Deep Sequence Models
Wenlong Chen
Despite exceptional predictive performance of Deep sequence models (DSMs), the main concern of their deployment centers around the lack of uncertainty awareness. In contrast, proba…