33 citations · 134 across the 20 of their papers we have counts for
9 papers · 1 filter
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…
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…
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 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…