11 citations · 16 across the 4 of their papers we have counts for
4 papers
Regularization Through Reasoning: Systematic Improvements in Language Model Classification via Explanation-Enhanced Fine-Tuning
Vivswan Shah, Randy Cogill, Hanwei Yue +2
Fine-tuning LLMs for classification typically maps inputs directly to labels. We ask whether attaching brief explanations to each label during fine-tuning yields better models. We…
Remote Labor Index: Measuring AI Automation of Remote Work
Mantas Mazeika, Alice Gatti, Cristina Menghini +44
AIs have made rapid progress on research-oriented benchmarks of knowledge and reasoning, but it remains unclear how these gains translate into economic value and automation. To mea…
Leveraging Continuously Differentiable Activation Functions for Learning in Quantized Noisy Environments
Vivswan Shah, Nathan Youngblood
Real-world analog systems intrinsically suffer from noise that can impede model convergence and accuracy on a variety of deep learning models. We demonstrate that differentiable ac…
AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks
Vivswan Shah, Nathan Youngblood
AnalogVNN, a simulation framework built on PyTorch which can simulate the effects of optoelectronic noise, limited precision, and signal normalization present in photonic neural ne…