31 citations · 38 across the 3 of their papers we have counts for
3 papers
Compressing Many-Shots in In-Context Learning
Devvrit Khatri, Pranamya Kulkarni, Nilesh Gupta +9
Large Language Models (LLMs) have been shown to be able to learn different tasks without explicit finetuning when given many input-output examples / demonstrations through In-Conte…
SmartChoices: Hybridizing Programming and Machine Learning
Victor Carbune, Thierry Coppey, Alexander Daryin +3
We present SmartChoices, an approach to making machine learning (ML) a first class citizen in programming languages which we see as one way to lower the entrance cost to applying M…
Deep Networks With Large Output Spaces
Sudheendra Vijayanarasimhan, Jonathon Shlens, Rajat Monga +1
Deep neural networks have been extremely successful at various image, speech, video recognition tasks because of their ability to model deep structures within the data. However, th…