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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...
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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...
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12/12/2022
PathFusion: Path-consistent Lidar-Camera Deep Feature Fusion
Fusing camera with LiDAR is a promising technique to improve the accurac...
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12/04/2022
Fast Point Cloud Generation with Straight Flows
Diffusion models have emerged as a powerful tool for point cloud generat...
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11/17/2021
Temporally Consistent Online Depth Estimation in Dynamic Scenes
Temporally consistent depth estimation is crucial for real-time applicat...
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11/01/2021
HRViT: Multi-Scale High-Resolution Vision Transformer
Vision transformers (ViTs) have attracted much attention for their super...
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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...
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10/07/2021
Streaming Transformer Transducer Based Speech Recognition Using Non-Causal Convolution
This paper improves the streaming transformer transducer for speech reco...
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07/09/2021
Noisy Training Improves E2E ASR for the Edge
Automatic speech recognition (ASR) has become increasingly ubiquitous on...
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04/26/2021
Improve Vision Transformers Training by Suppressing Over-smoothing
Introducing the transformer structure into computer vision tasks holds t...
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02/16/2021
AlphaNet: Improved Training of Supernet with Alpha-Divergence
Weight-sharing neural architecture search (NAS) is an effective techniqu...
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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...
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11/23/2020
KeepAugment: A Simple Information-Preserving Data Augmentation Approach
Data augmentation (DA) is an essential technique for training state-of-t...
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11/18/2020
AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling
Neural architecture search (NAS) has shown great promise designing state...
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10/28/2019
Stein Variational Gradient Descent With Matrix-Valued Kernels
Stein variational gradient descent (SVGD) is a particle-based inference ...
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10/07/2019
Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent
Designing energy-efficient networks is of critical importance for enabli...
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10/06/2019
Splitting Steepest Descent for Growing Neural Architectures
We develop a progressive training approach for neural networks which ada...
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06/10/2019
Improving Neural Language Modeling via Adversarial Training
Recently, substantial progress has been made in language modeling by usi...
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10/29/2018
Variational Inference with Tail-adaptive f-Divergence
Variational inference with α-divergences has been widely used in modern ...
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10/27/2018
Stein Variational Gradient Descent as Moment Matching
Stein variational gradient descent (SVGD) is a non-parametric inference ...
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11/20/2017
Structured Stein Variational Inference for Continuous Graphical Models
We propose a novel distributed inference algorithm for continuous graphi...
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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...
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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...
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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...
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08/16/2016