Dilin Wang | DeepAI

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Qiang Liu
190 publications
Ke Li
159 publications
Hao Liu
150 publications
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82 publications
Muhammad Abdul-Mageed
56 publications
Mathias Unberath
55 publications
Fei Sun
51 publications
Vikas Chandra
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Russell H. Taylor
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Wei Ye
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Xiaohui Zhang
33 publications

∙ 09/05/2023

TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models

Automatic Speech Recognition (ASR) models need to be optimized for speci...

0 Yuan Shangguan, et al. ∙

∙ 06/08/2023

Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts

Weight-sharing supernet has become a vital component for performance est...

0 Ganesh Jawahar, et al. ∙

∙ 12/12/2022

PathFusion: Path-consistent Lidar-Camera Deep Feature Fusion

Fusing camera with LiDAR is a promising technique to improve the accurac...

0 Lemeng Wu, et al. ∙

∙ 12/04/2022

Fast Point Cloud Generation with Straight Flows

Diffusion models have emerged as a powerful tool for point cloud generat...

0 Lemeng Wu, et al. ∙

∙ 11/17/2021

Temporally Consistent Online Depth Estimation in Dynamic Scenes

Temporally consistent depth estimation is crucial for real-time applicat...

11 Zhaoshuo Li, et al. ∙

∙ 11/01/2021

HRViT: Multi-Scale High-Resolution Vision Transformer

Vision transformers (ViTs) have attracted much attention for their super...

0 Jiaqi Gu, et al. ∙

∙ 10/15/2021

Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet

From wearables to powerful smart devices, modern automatic speech recogn...

0 Haichuan Yang, et al. ∙

∙ 10/07/2021

Streaming Transformer Transducer Based Speech Recognition Using Non-Causal Convolution

This paper improves the streaming transformer transducer for speech reco...

0 Yangyang Shi, et al. ∙

∙ 07/09/2021

Noisy Training Improves E2E ASR for the Edge

Automatic speech recognition (ASR) has become increasingly ubiquitous on...

0 Dilin Wang, et al. ∙

∙ 04/26/2021

Improve Vision Transformers Training by Suppressing Over-smoothing

Introducing the transformer structure into computer vision tasks holds t...

4 Chengyue Gong, et al. ∙

∙ 02/16/2021

AlphaNet: Improved Training of Supernet with Alpha-Divergence

Weight-sharing neural architecture search (NAS) is an effective techniqu...

3 Dilin Wang, et al. ∙

∙ 11/23/2020

AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence

Semi-supervised learning (SSL) is a key approach toward more data-effici...

12 Chengyue Gong, et al. ∙

∙ 11/23/2020

KeepAugment: A Simple Information-Preserving Data Augmentation Approach

Data augmentation (DA) is an essential technique for training state-of-t...

22 Chengyue Gong, et al. ∙

∙ 11/18/2020

AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling

Neural architecture search (NAS) has shown great promise designing state...

0 Dilin Wang, et al. ∙

∙ 10/28/2019

Stein Variational Gradient Descent With Matrix-Valued Kernels

Stein variational gradient descent (SVGD) is a particle-based inference ...

22 Dilin Wang, et al. ∙

∙ 10/07/2019

Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent

Designing energy-efficient networks is of critical importance for enabli...

0 Dilin Wang, et al. ∙

∙ 10/06/2019

Splitting Steepest Descent for Growing Neural Architectures

We develop a progressive training approach for neural networks which ada...

0 Qiang Liu, et al. ∙

∙ 06/10/2019

Improving Neural Language Modeling via Adversarial Training

Recently, substantial progress has been made in language modeling by usi...

0 Dilin Wang, et al. ∙

∙ 10/29/2018

Variational Inference with Tail-adaptive f-Divergence

Variational inference with α-divergences has been widely used in modern ...

0 Dilin Wang, et al. ∙

∙ 10/27/2018

Stein Variational Gradient Descent as Moment Matching

Stein variational gradient descent (SVGD) is a non-parametric inference ...

0 Qiang Liu, et al. ∙

∙ 11/20/2017

Structured Stein Variational Inference for Continuous Graphical Models

We propose a novel distributed inference algorithm for continuous graphi...

0 Dilin Wang, et al. ∙

∙ 07/20/2017

Learning to Draw Samples with Amortized Stein Variational Gradient Descent

We propose a simple algorithm to train stochastic neural networks to dra...

0 Yihao Feng, et al. ∙

∙ 07/04/2017

Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE

We propose a number of new algorithms for learning deep energy models an...

0 Qiang Liu, et al. ∙

∙ 11/06/2016

Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning

We propose a simple algorithm to train stochastic neural networks to dra...

0 Dilin Wang, et al. ∙

∙ 08/16/2016

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

We propose a general purpose variational inference algorithm that forms ...

0 Qiang Liu, et al. ∙

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