114 citations · 130 across the 6 of their papers we have counts for
7 papers
Offline Reinforcement Learning at Multiple Frequencies
Kaylee Burns, Tianhe Yu, Chelsea Finn +1
Leveraging many sources of offline robot data requires grappling with the heterogeneity of such data. In this paper, we focus on one particular aspect of heterogeneity: learning fr…
Implicit Kinematic Policies: Unifying Joint and Cartesian Action Spaces in End-to-End Robot Learning
Aditya Ganapathi, Pete Florence, Jake Varley +3
Action representation is an important yet often overlooked aspect in end-to-end robot learning with deep networks. Choosing one action space over another (e.g. target joint positio…
SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection
Eric Tzeng, Kaylee Burns, Kate Saenko +1
Domain adaptation of visual detectors is a critical challenge, yet existing methods have overlooked pixel appearance transformations, focusing instead on bootstrapping and/or domai…
Object Hallucination in Image Captioning
Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns +2
Despite continuously improving performance, contemporary image captioning models are prone to "hallucinating" objects that are not actually in a scene. One problem is that standard…
Evaluating Theory of Mind in Question Answering
Aida Nematzadeh, Kaylee Burns, Erin Grant +2
We propose a new dataset for evaluating question answering models with respect to their capacity to reason about beliefs. Our tasks are inspired by theory-of-mind experiments that…
Women also Snowboard: Overcoming Bias in Captioning Models (Extended Abstract)
Lisa Anne Hendricks, Kaylee Burns, Kate Saenko +2
Most machine learning methods are known to capture and exploit biases of the training data. While some biases are beneficial for learning, others are harmful. Specifically, image c…
Women also Snowboard: Overcoming Bias in Captioning Models
Kaylee Burns, Lisa Anne Hendricks, Kate Saenko +2
Most machine learning methods are known to capture and exploit biases of the training data. While some biases are beneficial for learning, others are harmful. Specifically, image c…