82 citations · 198 across the 13 of their papers we have counts for
14 papers
Neural Wasserstein Splines: Representation-Invariant Second-Order Priors for Trajectory Inference
Amartya Banerjee, Harlin Lee, Caroline Moosmüller
Population dynamics are often observed only as unpaired snapshots, which leave the evolution between measurements underdetermined. Second-order regularization can constrain it, but…
Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddings
Scott Ye, Harlin Lee
While generative models have shown promise in pediatric sleep analysis, the latent structure of their multimodal embeddings remains poorly understood. This work investigates sessio…
Fused Gromov-Wasserstein Distance with Feature Selection
Harlin Lee, Ying Yu, Mingxin Li +1
Fused Gromov-Wasserstein (FGW) distances provide a principled framework for comparing objects by jointly aligning structure and node features. However, existing FGW formulations tr…
PedSleepMAE: Generative Model for Multimodal Pediatric Sleep Signals
Saurav R. Pandey, Aaqib Saeed, Harlin Lee
Pediatric sleep is an important but often overlooked area in health informatics. We present PedSleepMAE, a generative model that fully leverages multimodal pediatric sleep signals…
Explainable Enrichment-Driven GrAph Reasoner (EDGAR) for Large Knowledge Graphs with Applications in Drug Repurposing
Olawumi Olasunkanmi, Evan Morris, Yaphet Kebede +4
Knowledge graphs (KGs) represent connections and relationships between real-world entities. We propose a link prediction framework for KGs named Enrichment-Driven GrAph Reasoner (E…
Efficient Trajectory Inference in Wasserstein Space Using Consecutive Averaging
Amartya Banerjee, Harlin Lee, Nir Sharon +1
Capturing data from dynamic processes through cross-sectional measurements is seen in many fields, such as computational biology. Trajectory inference deals with the challenge of r…
A Blob Method for Mean Field Control With Terminal Constraints
Katy Craig, Karthik Elamvazhuthi, Harlin Lee
In the present work, we develop a novel particle method for a general class of mean field control problems, with source and terminal constraints. Specific examples of the problems…
-Equivariant Dimensionality Reduction on Stiefel Manifolds
Andrew Lee, Harlin Lee, Jose A. Perea +2
Many real-world datasets live on high-dimensional Stiefel and Grassmannian manifolds, and respectively, and benefit from projection onto l…
Active Learning of Non-semantic Speech Tasks with Pretrained Models
Harlin Lee, Aaqib Saeed, Andrea L. Bertozzi
Pretraining neural networks with massive unlabeled datasets has become popular as it equips the deep models with a better prior to solve downstream tasks. However, this approach ge…
Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
Mario Krenn, Lorenzo Buffoni, Bruno Coutinho +13
A tool that could suggest new personalized research directions and ideas by taking insights from the scientific literature could significantly accelerate the progress of science. A…
Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEG
Harlin Lee, Aaqib Saeed
Sleep is particularly important to the health of infants, children, and adolescents, and sleep scoring is the first step to accurate diagnosis and treatment of potentially life-thr…
Distilled Non-Semantic Speech Embeddings with Binary Neural Networks for Low-Resource Devices
Harlin Lee, Aaqib Saeed
This work introduces BRILLsson, a novel binary neural network-based representation learning model for a broad range of non-semantic speech tasks. We train the model with knowledge…
A Large Collection of Real-world Pediatric Sleep Studies
Harlin Lee, Boyue Li, Shelly DeForte +4
Despite being crucial to health and quality of life, sleep -- especially pediatric sleep -- is not yet well understood. This is exacerbated by lack of access to sufficient pediatri…
Vector-Valued Graph Trend Filtering with Non-Convex Penalties
Rohan Varma, Harlin Lee, Jelena Kovačević +1
This work studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vector-valued. W…