Tan Nguyen | DeepAI

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∙ 07/06/2023

Touch, press and stroke: a soft capacitive sensor skin

Soft sensors that can discriminate shear and normal force could help pro...

0 Mirza S. Sarwar, et al. ∙

∙ 06/08/2023

Posterior Collapse in Linear Conditional and Hierarchical Variational Autoencoders

The posterior collapse phenomenon in variational autoencoders (VAEs), wh...

4 Hien Dang, et al. ∙

∙ 01/01/2023

Neural Collapse in Deep Linear Network: From Balanced to Imbalanced Data

Modern deep neural networks have achieved superhuman performance in task...

8 Hien Dang, et al. ∙

∙ 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...

0 Khang Nguyen, et al. ∙

∙ 09/29/2022

Improving Generative Flow Networks with Path Regularization

Generative Flow Networks (GFlowNets) are recently proposed models for le...

12 Anh Do, et al. ∙

∙ 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...

2 Tan Nguyen, et al. ∙

∙ 06/01/2022

Transformer with Fourier Integral Attentions

Multi-head attention empowers the recent success of transformers, the st...

79 Tan Nguyen, et al. ∙

∙ 03/16/2022

3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation

Medical image segmentation has been so far achieving promising results w...

0 Tan Nguyen, et al. ∙

∙ 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, ...

0 Ngoc-Vuong Ho, et al. ∙

∙ 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...

18 Bao Wang, et al. ∙

∙ 07/17/2020

Neural Networks with Recurrent Generative Feedback

Neural networks are vulnerable to input perturbations such as additive n...

22 Yujia Huang, et al. ∙

∙ 06/04/2020

Sample Efficient Graph-Based Optimization with Noisy Observations

We study sample complexity of optimizing "hill-climbing friendly" functi...

10 Tan Nguyen, et al. ∙

∙ 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...

0 Tan Nguyen, et al. ∙

∙ 07/10/2019

Out-of-Distribution Detection Using Neural Rendering Generative Models

Out-of-distribution (OoD) detection is a natural downstream task for dee...

35 Yujia Huang, et al. ∙

∙ 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...

0 Yue Wang, et al. ∙

∙ 11/01/2018

Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning

Unsupervised and semi-supervised learning are important problems that ar...

0 Nhat Ho, et al. ∙

∙ 12/06/2016

Semi-Supervised Learning with the Deep Rendering Mixture Model

Semi-supervised learning algorithms reduce the high cost of acquiring la...

0 Tan Nguyen, et al. ∙

∙ 12/06/2016

A Probabilistic Framework for Deep Learning

We develop a probabilistic framework for deep learning based on the Deep...

0 Ankit B. Patel, et al. ∙

∙ 04/12/2016

BoxLib with Tiling: An AMR Software Framework

In this paper we introduce a block-structured adaptive mesh refinement (...

0 Weiqun Zhang, et al. ∙

∙ 04/02/2015

A Probabilistic Theory of Deep Learning

A grand challenge in machine learning is the development of computationa...

0 Ankit B. Patel, et al. ∙

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