3 citations · 6 across the 9 of their papers we have counts for
12 papers
Admissible Diffusion for Multimodal Interventional Trajectories
Xing Han, Shravan Chaudhari, Jiarui Shao +2
Generating a plausible clinical trajectory does not establish what would happen under a different treatment. We present ADMIT, a framework combining irregular multimodal representa…
B[FM]: Brain Foundation Model via Flow Matching with SplitUNet
Jaedong Hwang, Kathleen Zhang, Wei Dai +5
EEG foundation models can learn generalizable representations from large-scale EEG corpora to enable single-backbone transfer across diverse clinical and brain-computer interface t…
Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals
Phu X. Nguyen, Konstantinos Kontras, Wei Dai +5
Electrocardiograms (ECGs) are widely used non-invasive measurements of cardiac activity and play a central role in clinical diagnosis. Recent multimodal approaches align ECG signal…
NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces
Konstantinos Kontras, Trui Osselaer, Stylianos G. Mouslech +12
Foundation models (FMs) promise to extract unified representations that generalize across downstream tasks. They have emerged across fields, including electroencephalography (EEG),…
On the Invariance and Generality of Neural Scaling Laws
Xing Han, Ziyin Liu, Liu Ziyin +2
Neural scaling laws establish a predictable relationship between model performance and data or compute, offering crucial guidance for resource allocation in new domains and tasks.…
OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization
Keane Ong, Sabri Boughorbel, Luwei Xiao +9
Socially intelligent AI systems must reason across diverse human behavioral tasks and generalize to new social contexts. However, behavioral data is inherently heterogeneous, compr…
Partial Information Decomposition via Normalizing Flows in Latent Gaussian Distributions
Wenyuan Zhao, Adithya Balachandran, Chao Tian +1
The study of multimodality has garnered significant interest in fields where the analysis of interactions among multiple information sources can enhance predictive modeling, data f…
What One Cannot, Two Can: Two-Layer Transformers Provably Represent Induction Heads on Any-Order Markov Chains
Chanakya Ekbote, Marco Bondaschi, Nived Rajaraman +4
In-context learning (ICL) is a hallmark capability of transformers, through which trained models learn to adapt to new tasks by leveraging information from the input context. Prior…
PuzzleWorld: A Benchmark for Multimodal, Open-Ended Reasoning in Puzzlehunts
Hengzhi Li, Justin Zhang, Brendon Jiang +9
Puzzlehunts are a genre of complex, multi-step puzzles lacking well-defined problem definitions. In contrast to conventional reasoning benchmarks consisting of tasks with clear ins…
QoQ-Med: Building Multimodal Clinical Foundation Models with Domain-Aware GRPO Training
Wei Dai, Peilin Chen, Chanakya Ekbote +1
Clinical decision-making routinely demands reasoning over heterogeneous data, yet existing multimodal language models (MLLMs) remain largely vision-centric and fail to generalize a…
SmellNet: A Large-scale Dataset for Real-world Smell Recognition
Dewei Feng, Wei Dai, Carol Li +3
The ability of AI to sense and identify various substances based on their smell alone can have profound impacts on allergen detection (e.g. smelling gluten or peanuts in a cake), m…
CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Models
Wei Dai, Peilin Chen, Malinda Lu +4
Recent advances in clinical AI have enabled remarkable progress across many clinical domains. However, existing benchmarks and models are primarily limited to a small set of modali…