2 citations · 2 across the 5 of their papers we have counts for
5 papers
MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference
Simla Burcu Harma, Danila Mishin, Zhengyuan Su +7
4-bit quantization enables efficient LLM inference, but suffers from significant accuracy degradation due to outliers. Prior work addresses this problem via data rotation or mixed-…
Calibration-Reasoning Framework for Descriptive Speech Quality Assessment
Elizaveta Kostenok, Mathieu Salzmann, Milos Cernak
Explainable speech quality assessment requires moving beyond Mean Opinion Scores (MOS) to analyze underlying perceptual dimensions. To address this, we introduce a novel post-train…
Effective Interplay between Sparsity and Quantization: From Theory to Practice
Simla Burcu Harma, Ayan Chakraborty, Elizaveta Kostenok +8
The increasing size of deep neural networks (DNNs) necessitates effective model compression to reduce their computational and memory footprints. Sparsity and quantization are two p…
Machine Translation Models Stand Strong in the Face of Adversarial Attacks
Pavel Burnyshev, Elizaveta Kostenok, Alexey Zaytsev
Adversarial attacks expose vulnerabilities of deep learning models by introducing minor perturbations to the input, which lead to substantial alterations in the output. Our researc…
Uncertainty Estimation of Transformers' Predictions via Topological Analysis of the Attention Matrices
Elizaveta Kostenok, Daniil Cherniavskii, Alexey Zaytsev
Transformer-based language models have set new benchmarks across a wide range of NLP tasks, yet reliably estimating the uncertainty of their predictions remains a significant chall…