3 papers
cs.CL2025
Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding
Tian Jin, Ellie Y. Cheng, Zack Ankner +6
Decoding with autoregressive large language models (LLMs) traditionally occurs sequentially, generating one token after another. An emerging line of work explored parallel decoding…
cs.LG2023
Analyzing Populations of Neural Networks via Dynamical Model Embedding
Jordan Cotler, Kai Sheng Tai, Felipe Hernández +2
A core challenge in the interpretation of deep neural networks is identifying commonalities between the underlying algorithms implemented by distinct networks trained for the same…
cs.HC2019
Human-Machine Collaboration for Fast Land Cover Mapping
Caleb Robinson, Anthony Ortiz, Kolya Malkin +5
We propose incorporating human labelers in a model fine-tuning system that provides immediate user feedback. In our framework, human labelers can interactively query model predicti…