research
∙
04/17/2023
BenchMD: A Benchmark for Modality-Agnostic Learning on Medical Images and Sensors
Medical data poses a daunting challenge for AI algorithms: it exists in ...
share
research
∙
02/24/2023
Hiding Data Helps: On the Benefits of Masking for Sparse Coding
Sparse coding refers to modeling a signal as sparse linear combinations ...
research
∙
10/24/2022
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup
Mixup is a data augmentation technique that relies on training using ran...
research
∙
10/14/2021
Towards Understanding the Data Dependency of Mixup-style Training
In the Mixup training paradigm, a model is trained using convex combinat...
research
∙
10/22/2020
Beyond Lazy Training for Over-parameterized Tensor Decomposition
Over-parametrization is an important technique in training neural networ...
research
∙
10/08/2020
Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks
Hessian captures important properties of the deep neural network loss la...
research
∙
09/24/2020
Secure Data Sharing With Flow Model
In the classical multi-party computation setting, multiple parties joint...
research
∙
06/30/2020
