research
∙
07/06/2023
Touch, press and stroke: a soft capacitive sensor skin
Soft sensors that can discriminate shear and normal force could help pro...
share
research
∙
06/08/2023
Posterior Collapse in Linear Conditional and Hierarchical Variational Autoencoders
The posterior collapse phenomenon in variational autoencoders (VAEs), wh...
research
∙
01/01/2023
Neural Collapse in Deep Linear Network: From Balanced to Imbalanced Data
Modern deep neural networks have achieved superhuman performance in task...
research
∙
11/28/2022
Revisiting Over-smoothing and Over-squashing using Ollivier's Ricci Curvature
Graph Neural Networks (GNNs) had been demonstrated to be inherently susc...
research
∙
09/29/2022
Improving Generative Flow Networks with Path Regularization
Generative Flow Networks (GFlowNets) are recently proposed models for le...
research
∙
08/01/2022
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization
Transformers have achieved remarkable success in sequence modeling and b...
research
∙
06/01/2022
Transformer with Fourier Integral Attentions
Multi-head attention empowers the recent success of transformers, the st...
research
∙
03/16/2022
3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation
Medical image segmentation has been so far achieving promising results w...
research
∙
03/16/2022
Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation
Medical image analysis using deep learning has recently been prevalent, ...
research
∙
10/13/2021
How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies
We present and review an algorithmic and theoretical framework for impro...
research
∙
07/17/2020
Neural Networks with Recurrent Generative Feedback
Neural networks are vulnerable to input perturbations such as additive n...
research
∙
06/04/2020
Sample Efficient Graph-Based Optimization with Noisy Observations
We study sample complexity of optimizing "hill-climbing friendly" functi...
research
∙
10/09/2019
Learning Near-optimal Convex Combinations of Basis Models with Generalization Guarantees
The problem of learning an optimal convex combination of basis models ha...
research
∙
07/10/2019
Out-of-Distribution Detection Using Neural Rendering Generative Models
Out-of-distribution (OoD) detection is a natural downstream task for dee...
research
∙
07/10/2019
Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference
State-of-the-art convolutional neural networks (CNNs) yield record-break...
research
∙
11/01/2018
Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning
Unsupervised and semi-supervised learning are important problems that ar...
research
∙
12/06/2016
Semi-Supervised Learning with the Deep Rendering Mixture Model
Semi-supervised learning algorithms reduce the high cost of acquiring la...
research
∙
12/06/2016
A Probabilistic Framework for Deep Learning
We develop a probabilistic framework for deep learning based on the Deep...
research
∙
04/12/2016
BoxLib with Tiling: An AMR Software Framework
In this paper we introduce a block-structured adaptive mesh refinement (...
research
∙
04/02/2015
