Neural networks grown and self-organized by noise. - Guruprasad Raghavan; Matt Thomson 001 (NeurIPS, 2019)

Geometric algorithms for predicting resilience and recovering damage in neural networks. - Guruprasad Raghavan; Jiayi Li; Matt Thomson 001 (CoRR, 2020)

Self-organization of multi-layer spiking neural networks. - Guruprasad Raghavan; Cong Lin; Matt Thomson 001 (CoRR, 2020)

Sparsifying networks by traversing Geodesics. - Guruprasad Raghavan; Matt Thomson 001 (CoRR, 2020)

Reinforcement Learning reveals fundamental limits on the mixing of active particles. - Dominik Schildknecht; Anastasia N. Popova; Jack Stellwagen; Matt Thomson 001 (CoRR, 2021)

Solving hybrid machine learning tasks by traversing weight space geodesics. - Guruprasad Raghavan; Matt Thomson 001 (CoRR, 2021)

Active feature selection discovers minimal gene-sets for classifying cell-types and disease states in single-cell mRNA-seq data. - Xiaoqiao Chen; Sisi Chen; Matt Thomson 001 (CoRR, 2021)

CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq. - Bryan He; Matthew Thomson 001; Meena Subramaniam; Richard Perez; Chun Jimmie Ye; James Zou (CoRR, 2021)

Connectedness of loss landscapes via the lens of Morse theory. - Danil Akhtiamov; Matt Thomson 001 (NeurReps, 2022)

Engineering flexible machine learning systems by traversing functionally invariant paths in weight space. - Guruprasad Raghavan; Matt Thomson 001 (CoRR, 2022)

Generating counterfactual explanations of tumor spatial proteomes to discover effective, combinatorial therapies that enhance cancer immunotherapy. - Zitong Jerry Wang; Matt Thomson 001 (CoRR, 2022)

Minimal gene set discovery in single-cell mRNA-seq datasets with ActiveSVM. - Xiaoqiao Chen; Sisi Chen; Matt Thomson 001 (Nat. Comput. Sci., 2022)

Automated mapping of virtual environments with visual predictive coding. - James Gornet; Matt Thomson 001 (CoRR, 2023)

Tryage: Real-time, intelligent Routing of User Prompts to Large Language Models. - Surya Narayanan Hari; Matt Thomson 001 (CoRR, 2023)

What's the Magic Word? A Control Theory of LLM Prompting. - Aman Bhargava; Cameron Witkowski; Manav Shah; Matt Thomson 001 (CoRR, 2023)

Herd: Using multiple, smaller LLMs to match the performances of proprietary, large LLMs via an intelligent composer. - Surya Narayanan Hari; Matt Thomson 001 (CoRR, 2023)

Leveraging Open-Source Large Language Models for encoding Social Determinants of Health using an Intelligent Router. - Akul Goel; Surya Narayanan Hari; Belinda Waltman; Matt Thomson 001 (CoRR, 2024)

Prompt Baking. - Aman Bhargava; Cameron Witkowski; Alexander Detkov; Matt Thomson 001 (CoRR, 2024)

Efficiently Deploying LLMs with Controlled Risk. - Michael J. Zellinger; Matt Thomson 001 (CoRR, 2024)

Automated construction of cognitive maps with visual predictive coding. - James Gornet; Matt Thomson 001 (Nat. Mac. Intell., 2024)

Engineering flexible machine learning systems by traversing functionally invariant paths. - Guruprasad Raghavan; Bahey Tharwat; Surya Narayanan Hari; Dhruvil Satani; Rex Liu; Matt Thomson 001 (Nat. Mac. Intell., 2024)

Rational Tuning of LLM Cascades via Probabilistic Modeling. - Michael J. Zellinger; Matt Thomson 001 (Trans. Mach. Learn. Res., 2025)

Cost-Saving LLM Cascades with Early Abstention. - Michael J. Zellinger; Rex Liu; Matt Thomson 001 (CoRR, 2025)

Economic Evaluation of LLMs. - Michael J. Zellinger; Matt Thomson 001 (CoRR, 2025)

Fail Fast, or Ask: Mitigating the Deficiencies of Reasoning LLMs with Human-in-the-Loop Systems Engineering. - Michael J. Zellinger; Matt Thomson 001 (CoRR, 2025)