5 papers
Top-P Sensor Selection for Target Localization
Kaan Buyukkalayci, Kyle Pak, Merve Karakas +2
We study set-valued decision rules in which performance is defined by the inclusion of the top- hypotheses, rather than only the single best or true hypothesis. This criterion i…
ScaleBITS: Scalable Bitwidth Search for Hardware-Aligned Mixed-Precision LLMs
Xinlin Li, Timothy Chou, Josh Fromm +3
Post-training weight quantization is crucial for reducing the memory and inference cost of large language models (LLMs), yet pushing the average precision below 4 bits remains chal…
ICQuant: Index Coding enables Low-bit LLM Quantization
Xinlin Li, Osama Hanna, Christina Fragouli +1
The rapid deployment of Large Language Models (LLMs) highlights the need for efficient low-bit post-training quantization (PTQ), due to their high memory costs. A key challenge in…
Enhancing Binary Search via Overlapping Partitions
Kaan Buyukkalayci, Merve Karakas, Xinlin Li +1
This paper considers the task of performing binary search under noisy decisions, focusing on the application of target area localization. In the presence of noise, the classical pa…
InfoMAE: Pair-Efficient Cross-Modal Alignment for Multimodal Time-Series Sensing Signals
Tomoyoshi Kimura, Xinlin Li, Osama Hanna +10
Standard multimodal self-supervised learning (SSL) algorithms regard cross-modal synchronization as implicit supervisory labels during pretraining, thus posing high requirements on…