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Mark Mazumder

5 accepted papers

2026

QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture

ICML 2026poster

The field of computer architecture, which bridges high-level software abstractions and low-level hardware implementations, remains absent from current large language model (LLM) evaluations. To this end, we present QuArch (pronounced 'quark'), the first benchmark designed to facilitate the developme…

Cited by 0SourceScholar
2023

DataPerf: Benchmarks for Data-Centric AI Development

NeurIPS 2023poster

Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems. Neglecting the fundamental importance of data has given rise to inaccuracy, bias, and…

2021

Multilingual Spoken Words Corpus

NeurIPS 2021poster

Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken words in 50 languages collectively spoken by over 5 billion people, for academic research and commercial applications in keyword spotting and spoken term search, licensed under CC-BY 4.0. The dataset contains more than 3…

Cited by 66SourceScholar
2021

The People’s Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage

NeurIPS 2021poster

The People’s Speech is a free-to-download 31,400-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-BY-SA. The data is collected via searching the Internet for appropriately licensed audio data with existing transcription…

Cited by 99SourceScholar
2015

Guaranteeing Spoof-Resilient Multi-Robot Networks

RSS 2015poster

Multi-robot networks use wireless communication to provide wide-ranging services such as aerial surveillance and unmanned delivery. However, effective coordination between multiple robots requires trust, making them particularly vulnerable to cyber-attacks. Specifically, such networks can be gravely…

Cited by 124SourcePDFScholar