168 citations · 815 across the 54 of their papers we have counts for
4 papers · 2 filters
TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations
Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju +1
Machine Learning (ML) models are increasingly used to make critical decisions in real-world applications, yet they have become more complex, making them harder to understand. To th…
Learning to Query Internet Text for Informing Reinforcement Learning Agents
Kolby Nottingham, Alekhya Pyla, Sameer Singh +1
Generalization to out of distribution tasks in reinforcement learning is a challenging problem. One successful approach improves generalization by conditioning policies on task or…
Rethinking Explainability as a Dialogue: A Practitioner's Perspective
Himabindu Lakkaraju, Dylan Slack, Yuxin Chen +2
As practitioners increasingly deploy machine learning models in critical domains such as health care, finance, and policy, it becomes vital to ensure that domain experts function e…
BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing
Yoshitomo Matsubara, Davide Callegaro, Sameer Singh +2
Although mission-critical applications require the use of deep neural networks (DNNs), their continuous execution at mobile devices results in a significant increase in energy cons…