33 citations · 134 across the 20 of their papers we have counts for
21 papers
Extracting Algorithms in Pre-trained LLMs: A Case on Hidden Markov Models
Yijia Dai, Zhaolin Gao, Yahya Sattar +2
Large language models (LLMs) display a striking ability to predict next observations from Hidden Markov Models (HMMs) via in-context learning (ICL), but the algorithm underlying th…
How many classes do we need to see for novel class discovery?
Akanksha Sarkar, Been Kim, Jennifer J. Sun
Novel class discovery is essential for ML models to adapt to evolving real-world data, with applications ranging from scientific discovery to robotics. However, these datasets cont…
Pre-trained Large Language Models Learn Hidden Markov Models In-context
Yijia Dai, Zhaolin Gao, Yahya Sattar +2
Hidden Markov Models (HMMs) are foundational tools for modeling sequential data with latent Markovian structure, yet fitting them to real-world data remains computationally challen…
Learning Keypoints for Multi-Agent Behavior Analysis using Self-Supervision
Daniel Khalil, Christina Liu, Pietro Perona +2
The study of social interactions and collective behaviors through multi-agent video analysis is crucial in biology. While self-supervised keypoint discovery has emerged as a promis…
BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos
Jennifer J. Sun, Lili Karashchuk, Amil Dravid +9
Quantifying motion in 3D is important for studying the behavior of humans and other animals, but manual pose annotations are expensive and time-consuming to obtain. Self-supervised…
Neurosymbolic Programming for Science
Jennifer J. Sun, Megan Tjandrasuwita, Atharva Sehgal +4
Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery. These models combine neural and symbolic components to learn complex patterns and r…
MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of Behavior
Jennifer J. Sun, Markus Marks, Andrew Ulmer +20
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 o…
Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings
Sabera Talukder, Jennifer J. Sun, Matthew Leonard +2
Neuroscientists and neuroengineers have long relied on multielectrode neural recordings to study the brain. However, in a typical experiment, many factors corrupt neural recordings…
Open-Source Tools for Behavioral Video Analysis: Setup, Methods, and Development
Kevin Luxem, Jennifer J. Sun, Sean P. Bradley +5
Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, sca…
Self-Supervised Keypoint Discovery in Behavioral Videos
Jennifer J. Sun, Serim Ryou, Roni Goldshmid +6
We propose a method for learning the posture and structure of agents from unlabelled behavioral videos. Starting from the observation that behaving agents are generally the main so…
Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis
Albert Tseng, Jennifer J. Sun, Yisong Yue
Obtaining annotations for large training sets is expensive, especially in settings where domain knowledge is required, such as behavior analysis. Weak supervision has been studied…
Unsupervised Learning of Neurosymbolic Encoders
Eric Zhan, Jennifer J. Sun, Ann Kennedy +2
We present a framework for the unsupervised learning of neurosymbolic encoders, which are encoders obtained by composing neural networks with symbolic programs from a domain-specif…
Interpreting Expert Annotation Differences in Animal Behavior
Megan Tjandrasuwita, Jennifer J. Sun, Ann Kennedy +2
Hand-annotated data can vary due to factors such as subjective differences, intra-rater variability, and differing annotator expertise. We study annotations from different experts…
The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions
Jennifer J. Sun, Tomomi Karigo, Dipam Chakraborty +8
Multi-agent behavior modeling aims to understand the interactions that occur between agents. We present a multi-agent dataset from behavioral neuroscience, the Caltech Mouse Social…
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization
Long Zhao, Yuxiao Wang, Jiaping Zhao +7
We introduce a novel representation learning method to disentangle pose-dependent as well as view-dependent factors from 2D human poses. The method trains a network using cross-vie…
Task Programming: Learning Data Efficient Behavior Representations
Jennifer J. Sun, Ann Kennedy, Eric Zhan +3
Specialized domain knowledge is often necessary to accurately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire from domain experts.…
View-Invariant, Occlusion-Robust Probabilistic Embedding for Human Pose
Ting Liu, Jennifer J. Sun, Long Zhao +6
Recognition of human poses and actions is crucial for autonomous systems to interact smoothly with people. However, cameras generally capture human poses in 2D as images and videos…
Learning Differentiable Programs with Admissible Neural Heuristics
Ameesh Shah, Eric Zhan, Jennifer J. Sun +3
We study the problem of learning differentiable functions expressed as programs in a domain-specific language. Such programmatic models can offer benefits such as composability and…
EEV: A Large-Scale Dataset for Studying Evoked Expressions from Video
Jennifer J. Sun, Ting Liu, Alan S. Cowen +3
Videos can evoke a range of affective responses in viewers. The ability to predict evoked affect from a video, before viewers watch the video, can help in content creation and vide…
View-Invariant Probabilistic Embedding for Human Pose
Jennifer J. Sun, Jiaping Zhao, Liang-Chieh Chen +3
Depictions of similar human body configurations can vary with changing viewpoints. Using only 2D information, we would like to enable vision algorithms to recognize similarity in h…
GLA in MediaEval 2018 Emotional Impact of Movies Task
Jennifer J. Sun, Ting Liu, Gautam Prasad
The visual and audio information from movies can evoke a variety of emotions in viewers. Towards a better understanding of viewer impact, we present our methods for the MediaEval 2…