Xiaoliang Dai

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Princeton University ∙ Facebook

FP-NAS: Fast Probabilistic Neural Architecture Search

Differential Neural Architecture Search (NAS) requires all layer choices...
11/22/2020 ∙ by Zhicheng Yan, et al. ∙ 4 ∙ share

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Fully Dynamic Inference with Deep Neural Networks

Modern deep neural networks are powerful and widely applicable models th...
07/29/2020 ∙ by Wenhan Xia, et al. ∙ 0 ∙ share

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Visual Transformers: Token-based Image Representation and Processing for Computer Vision

Computer vision has achieved great success using standardized image repr...
06/05/2020 ∙ by Bichen Wu, et al. ∙ 23 ∙ share

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FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function

Neural Architecture Search (NAS) yields state-of-the-art neural networks...
06/03/2020 ∙ by Xiaoliang Dai, et al. ∙ 11 ∙ share

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FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions

Differentiable Neural Architecture Search (DNAS) has demonstrated great ...
04/12/2020 ∙ by Alvin Wan, et al. ∙ 17 ∙ share

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STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods

Neural networks (NNs) have been successfully deployed in many applicatio...
12/12/2019 ∙ by Shayan Hassantabar, et al. ∙ 0 ∙ share

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DiabDeep: Pervasive Diabetes Diagnosis based on Wearable Medical Sensors and Efficient Neural Networks

Diabetes impacts the quality of life of millions of people. However, dia...
10/11/2019 ∙ by Hongxu Yin, et al. ∙ 13 ∙ share

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Incremental Learning Using a Grow-and-Prune Paradigm with Efficient Neural Networks

Deep neural networks (DNNs) have become a widely deployed model for nume...
05/27/2019 ∙ by Xiaoliang Dai, et al. ∙ 0 ∙ share

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ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation

This paper proposes an efficient neural network (NN) architecture design...
12/21/2018 ∙ by Xiaoliang Dai, et al. ∙ 0 ∙ share

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FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

Designing accurate and efficient ConvNets for mobile devices is challeng...
12/09/2018 ∙ by Bichen Wu, et al. ∙ 0 ∙ share

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Grow and Prune Compact, Fast, and Accurate LSTMs

Long short-term memory (LSTM) has been widely used for sequential data m...
05/30/2018 ∙ by Xiaoliang Dai, et al. ∙ 0 ∙ share

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Grow and Prune Compact, Fast, and AccurateLSTMs

Long short-term memory (LSTM) has been widely used for sequential data m...
05/30/2018 ∙ by Xiaoliang Dai, et al. ∙ 0 ∙ share

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PinMe: Tracking a Smartphone User around the World

With the pervasive use of smartphones that sense, collect, and process v...
02/05/2018 ∙ by Arsalan Mosenia, et al. ∙ 0 ∙ share

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NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm

Neural networks (NNs) have begun to have a pervasive impact on various a...
11/06/2017 ∙ by Xiaoliang Dai, et al. ∙ 0 ∙ share

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Xiaoliang Dai

Featured Co-authors

Masayoshi Tomizuka

Kurt Keutzer

Joseph E. Gonzalez

Prateek Mittal

Yuandong Tian

Fei Sun

Bichen Wu

Tao Xu

Niraj K. Jha

Peter Vajda

Kan Chen

FP-NAS: Fast Probabilistic Neural Architecture Search

Fully Dynamic Inference with Deep Neural Networks

Visual Transformers: Token-based Image Representation and Processing for Computer Vision

FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function

FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions

STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods

DiabDeep: Pervasive Diabetes Diagnosis based on Wearable Medical Sensors and Efficient Neural Networks

Incremental Learning Using a Grow-and-Prune Paradigm with Efficient Neural Networks

ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

Grow and Prune Compact, Fast, and Accurate LSTMs

Grow and Prune Compact, Fast, and AccurateLSTMs

PinMe: Tracking a Smartphone User around the World

NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm