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2020 – today
- 2024
[i15]Akul Goel
, Surya Narayanan Hari, Belinda Waltman, Matt Thomson:
Leveraging Open-Source Large Language Models for encoding Social Determinants of Health using an Intelligent Router. CoRR abs/2405.19631 (2024)- [i14]Aman Bhargava, Cameron Witkowski, Alexander Detkov, Matt Thomson:
Prompt Baking. CoRR abs/2409.13697 (2024) - [i13]Michael J. Zellinger, Matt Thomson:
Efficiently Deploying LLMs with Controlled Risk. CoRR abs/2410.02173 (2024) - 2023
- [i12]Surya Narayanan Hari, Matt Thomson:
Tryage: Real-time, intelligent Routing of User Prompts to Large Language Models. CoRR abs/2308.11601 (2023) - [i11]Aman Bhargava, Cameron Witkowski, Manav Shah, Matt Thomson:
What's the Magic Word? A Control Theory of LLM Prompting. CoRR abs/2310.04444 (2023) - [i10]Surya Narayanan Hari, Matt Thomson:
Herd: Using multiple, smaller LLMs to match the performances of proprietary, large LLMs via an intelligent composer. CoRR abs/2310.19902 (2023) - 2022
- [j2]Xiaoqiao Chen, Sisi Chen, Matt Thomson:
Minimal gene set discovery in single-cell mRNA-seq datasets with ActiveSVM. Nat. Comput. Sci. 2(6): 387-398 (2022) - [c2]Danil Akhtiamov, Matt Thomson:
Connectedness of loss landscapes via the lens of Morse theory. NeurReps 2022: 171-181 - [i9]Guruprasad Raghavan, Matt Thomson:
Engineering flexible machine learning systems by traversing functionally invariant paths in weight space. CoRR abs/2205.00334 (2022) - [i8]Zitong Jerry Wang, Matt Thomson:
Generating counterfactual explanations of tumor spatial proteomes to discover effective, combinatorial therapies that enhance cancer immunotherapy. CoRR abs/2211.04020 (2022) - 2021
- [i7]Dominik Schildknecht, Anastasia N. Popova, Jack Stellwagen, Matt Thomson:
Reinforcement Learning reveals fundamental limits on the mixing of active particles. CoRR abs/2105.14105 (2021) - [i6]Guruprasad Raghavan, Matt Thomson:
Solving hybrid machine learning tasks by traversing weight space geodesics. CoRR abs/2106.02793 (2021) - [i5]Xiaoqiao Chen, Sisi Chen, Matt Thomson:
Active feature selection discovers minimal gene-sets for classifying cell-types and disease states in single-cell mRNA-seq data. CoRR abs/2106.08317 (2021) - 2020
- [i4]Guruprasad Raghavan, Jiayi Li, Matt Thomson:
Geometric algorithms for predicting resilience and recovering damage in neural networks. CoRR abs/2005.11603 (2020) - [i3]Guruprasad Raghavan, Cong Lin, Matt Thomson:
Self-organization of multi-layer spiking neural networks. CoRR abs/2006.06902 (2020) - [i2]Guruprasad Raghavan, Matt Thomson:
Sparsifying networks by traversing Geodesics. CoRR abs/2012.09605 (2020)
2010 – 2019
- 2019
- [c1]Guruprasad Raghavan, Matt Thomson:
Neural networks grown and self-organized by noise. NeurIPS 2019: 1895-1905 - [i1]Guruprasad Raghavan, Matt Thomson:
Neural networks grown and self-organized by noise. CoRR abs/1906.01039 (2019) - 2014
- [j1]David A. Sivak, Matt Thomson:
Environmental Statistics and Optimal Regulation. PLoS Comput. Biol. 10(9) (2014)
Coauthor Index
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