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Denis Mazur

4 accepted papers

2025

Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models

ICML 2025poster

Efficient real-world deployments of large language models (LLMs) rely on Key-Value (KV) caching for processing and generating long outputs, reducing the need for repetitive computation. For large contexts, Key-Value caches can take up tens of gigabytes of device memory, as they store vector represen…

2024

PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression

NeurIPS 2024oral

There has been significant interest in "extreme" compression of large language models (LLMs), i.e. to 1-2 bits per parameter, which allows such models to be executed efficiently on resource-constrained devices. Existing work focused on improved one-shot quantization techniques and weight represent…

2021

Distributed Deep Learning In Open Collaborations

NeurIPS 2021poster

Modern deep learning applications require increasingly more compute to train state-of-the-art models. To address this demand, large corporations and institutions use dedicated High-Performance Computing clusters, whose construction and maintenance are both environmentally costly and well beyond the…

Cited by 62SourcePDFScholar
2019

Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs

NeurIPS 2019poster

Learning useful representations is a key ingredient to the success of modern machine learning. Currently, representation learning mostly relies on embedding data into Euclidean space. However, recent work has shown that data in some domains is better modeled by non-euclidean metric spaces, and inapp…