Kristin Branson — accepted AI-conference papers

2023

MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of Behavior

Jennifer J. Sun, Markus Marks, Andrew Wesley Ulmer, Dipam Chakraborty, Brian Geuther, Edward Hayes +17

ICML 2023poster

We introduce MABe22, a large-scale, multi-agent video and trajectory benchmark to assess the quality of learned behavior representations. This dataset is collected from a variety of biology experiments, and includes triplets of interacting mice (4.7 million frames video+pose tracking data, 10 millio…

Cited by 12 Source ↗PDF ↗code ↗Scholar ↗

2018

Quantitatively Evaluating GANs With Divergences Proposed for Training

Daniel Jiwoong Im, He Ma, Graham W. Taylor, Kristin Branson

ICLR 2018poster

Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application. However, we currently l…

Cited by 84 Source ↗PDF ↗Scholar ↗

2017

Learning Recurrent Representations for Hierarchical Behavior Modeling

Eyrun Eyjolfsdottir, Kristin Branson, Yisong Yue, Pietro Perona

ICLR 2017poster

We propose a framework for detecting action patterns from motion sequences and modeling the sensory-motor relationship of animals, using a generative recurrent neural network. The network has a discriminative part (classifying actions) and a generative part (predicting motion), whose recurrent cells…

Cited by 59 Source ↗Scholar ↗

2015

Sample Complexity of Learning Mahalanobis Distance Metrics

Nakul Verma, Kristin Branson

NeurIPS 2015poster

Metric learning seeks a transformation of the feature space that enhances prediction quality for a given task. In this work we provide PAC-style sample complexity rates for supervised metric learning. We give matching lower- and upper-bounds showing that sample complexity scales with the representat…

Cited by 45 Source ↗PDF ↗Scholar ↗

2015

Understanding Classifier Errors by Examining Influential Neighbors

Mayank Kabra, Alice Robie, Kristin Branson

CVPR 2015poster

Modern supervised learning algorithms can learn very accurate and complex discriminating functions. But when these classifiers fail, this complexity can also be a drawback because there is no easy, intuitive way to diagnose why they are failing and remedy the problem. This important question has rec…

Cited by 42 Source ↗PDF ↗Scholar ↗