collaborators

5 papers

cs.AI2026

CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models

Abhilash Durgam, Nyle Siddiqui, Jeffrey A. Chan-Santiago +3

Electroencephalography (EEG) is a critical, non-invasive method to monitor electrical brain activity. EEGs can span anywhere from a couple seconds to multiple hours, posing a major…

cs.CV2026

Learnability-Guided Diffusion for Dataset Distillation

Jeffrey A. Chan-Santiago, Mubarak Shah

Training machine learning models on massive datasets is expensive and time-consuming. Dataset distillation addresses this by creating a small synthetic dataset that achieves the sa…

cs.CV2026

VRR-QA: Visual Relational Reasoning in Videos Beyond Explicit Cues

Sirnam Swetha, Rohit Gupta, Parth Parag Kulkarni +5

Video Question Answering (VideoQA) has made significant strides by leveraging multimodal learning to align visual and textual modalities. However, current benchmarks overwhelmingly…

cs.CV2025

GVD: Guiding Video Diffusion Model for Scalable Video Distillation

Kunyang Li, Jeffrey A Chan Santiago, Sarinda Dhanesh Samarasinghe +2

To address the larger computation and storage requirements associated with large video datasets, video dataset distillation aims to capture spatial and temporal information in a si…

cs.CV2025

MGD: Mode-Guided Dataset Distillation using Diffusion Models

Jeffrey A. Chan-Santiago, Praveen Tirupattur, Gaurav Kumar Nayak +2

Dataset distillation has emerged as an effective strategy, significantly reducing training costs and facilitating more efficient model deployment. Recent advances have leveraged ge…