activity
20182020
most citedThe Bach Doodle: Approachable music composition with machine learning at scale

42 citations · 55 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL202013 cited

The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models

Ian Tenney, James Wexler, Jasmijn Bastings +8

We present the Language Interpretability Tool (LIT), an open-source platform for visualization and understanding of NLP models. We focus on core questions about model behavior: Why…

cs.SD201942 cited

The Bach Doodle: Approachable music composition with machine learning at scale

Cheng-Zhi Anna Huang, Curtis Hawthorne, Adam Roberts +4

To make music composition more approachable, we designed the first AI-powered Google Doodle, the Bach Doodle, where users can create their own melody and have it harmonized by a ma…

cs.LG2019

The What-If Tool: Interactive Probing of Machine Learning Models

James Wexler, Mahima Pushkarna, Tolga Bolukbasi +3

A key challenge in developing and deploying Machine Learning (ML) systems is understanding their performance across a wide range of inputs. To address this challenge, we created th…

stat.ML2019

Towards Automatic Concept-based Explanations

Amirata Ghorbani, James Wexler, James Zou +1

Interpretability has become an important topic of research as more machine learning (ML) models are deployed and widely used to make important decisions. Most of the current explan…

cs.HC2018

ClinicalVis: Supporting Clinical Task-Focused Design Evaluation

Marzyeh Ghassemi, Mahima Pushkarna, James Wexler +2

Making decisions about what clinical tasks to prepare for is multi-factored, and especially challenging in intensive care environments where resources must be balanced with patient…