24 citations · 159 across the 20 of their papers we have counts for
8 papers · 1 filter
Representing Partial Programs with Blended Abstract Semantics
Maxwell Nye, Yewen Pu, Matthew Bowers +3
Synthesizing programs from examples requires searching over a vast, combinatorial space of possible programs. In this search process, a key challenge is representing the behavior o…
Task-Oriented Dialogue as Dataflow Synthesis
Semantic Machines, Jacob Andreas, John Bufe +43
We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph. A dialogue agent maps each user utterance to a program that extends th…
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
Geeticka Chauhan, Ruizhi Liao, William Wells +6
We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiog…
Are Visual Explanations Useful? A Case Study in Model-in-the-Loop Prediction
Eric Chu, Deb Roy, Jacob Andreas
We present a randomized controlled trial for a model-in-the-loop regression task, with the goal of measuring the extent to which (1) good explanations of model predictions increase…
Compositional Explanations of Neurons
Jesse Mu, Jacob Andreas
We describe a procedure for explaining neurons in deep representations by identifying compositional logical concepts that closely approximate neuron behavior. Compared to prior wor…
Unnatural Language Processing: Bridging the Gap Between Synthetic and Natural Language Data
Alana Marzoev, Samuel Madden, M. Frans Kaashoek +2
Large, human-annotated datasets are central to the development of natural language processing models. Collecting these datasets can be the most challenging part of the development…