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Proceedings of the 58th Annual ACM Symposium on Theory of Computing

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By aditya bhaskara, Artur Czumaj
| ACM - Association for Computing Machinery
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[2502.11439] An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning

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By aditya bhaskara
| arxiv.org
[Submitted on 17 Feb 2025 ( v1 ), last revised 24 Jul 2025 (this version, v2)] Title:An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning View a PDF of the paper titled An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning, by Cen-Jhih Li and Aditya Bhaskara View PDF HTML (experimental) Abstract:Fine-tuning is an important step in adapting foundation models such as large language models to downstream tasks.
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Factorization-based Imputation of Expression in Single-cell Transcriptomic Analysis (FIESTA) recovers Gene-Cell-State Relationships

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By Elnaz Mirzaei Mehrabad, aditya bhaskara, Benjamin T. Spike
| biorxiv.org
Abstract Single cell RNA sequencing (scRNA-seq) is a gene expression profiling technique that is presently revolutionizing the study of complex cellular systems in the biological sciences. Existing scRNA-seq methods suffer from sub-optimal target recovery leading to inaccurate measurements including many false negatives. The resulting 'zero-inflated' data may confound data interpretation and visualization.
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