Lei Jiang

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Indiana University Bloomington ∙ Indiana University

EXMA: A Genomics Accelerator for Exact-Matching

Genomics is the foundation of precision medicine, global food security a...
01/13/2021 ∙ by Lei Jiang, et al. ∙ 0 ∙ share

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CryptoGRU: Low Latency Privacy-Preserving Text Analysis With GRU

Billions of text analysis requests containing private emails, personal t...
10/22/2020 ∙ by Bo Feng, et al. ∙ 18 ∙ share

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Helix: Algorithm/Architecture Co-design for Accelerating Nanopore Genome Base-calling

Nanopore genome sequencing is the key to enabling personalized medicine,...
08/04/2020 ∙ by Qian Lou, et al. ∙ 0 ∙ share

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AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference

Hybrid Privacy-Preserving Neural Network (HPPNN) implementing linear lay...
06/07/2020 ∙ by Qian Lou, et al. ∙ 0 ∙ share

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Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning

Segmentation from renal pathological images is a key step in automatic a...
02/20/2020 ∙ by Ke Mei, et al. ∙ 0 ∙ share

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Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data

Big data is one of the cornerstones to enabling and training deep neural...
11/16/2019 ∙ by Qian Lou, et al. ∙ 11 ∙ share

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Liger: Combining Interaction Paradigms for Visual Analysis

Visualization tools usually leverage a single interaction paradigm (e.g....
07/19/2019 ∙ by Bahador Saket, et al. ∙ 0 ∙ share

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SHE: A Fast and Accurate Privacy-Preserving Deep Neural Network Via Leveled TFHE and Logarithmic Data Representation

Homomorphic Encryption (HE) is one of the most promising security soluti...
06/01/2019 ∙ by Qian Lou, et al. ∙ 0 ∙ share

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A GraphBLAS Approach for Subgraph Counting

Subgraph counting aims to count the occurrences of a subgraph template T...
03/11/2019 ∙ by Langshi Chen, et al. ∙ 0 ∙ share

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AutoQB: AutoML for Network Quantization and Binarization on Mobile Devices

In this paper, we propose a hierarchical deep reinforcement learning (DR...
02/15/2019 ∙ by Qian Lou, et al. ∙ 0 ∙ share

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Pose Invariant 3D Face Reconstruction

3D face reconstruction is an important task in the field of computer vis...
11/13/2018 ∙ by Lei Jiang, et al. ∙ 16 ∙ share

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High-Performance Massive Subgraph Counting using Pipelined Adaptive-Group Communication

Subgraph counting aims to count the number of occurrences of a subgraph ...
04/25/2018 ∙ by Langshi Chen, et al. ∙ 0 ∙ share

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PT-Spike: A Precise-Time-Dependent Single Spike Neuromorphic Architecture with Efficient Supervised Learning

One of the most exciting advancements in AI over the last decade is the ...
03/14/2018 ∙ by Tao Liu, et al. ∙ 0 ∙ share

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DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework

As one of most fascinating machine learning techniques, deep neural netw...
03/14/2018 ∙ by Zihao Liu, et al. ∙ 0 ∙ share

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Lei Jiang

Featured Co-authors

Yanzhi Wang

Jun Liu

Jie Xu

Josef Kittler

Tao Liu

Bo Peng

Minje Kim

Ariful Azad

Alex Endert

WuJie Wen

Jiayu Li

EXMA: A Genomics Accelerator for Exact-Matching

CryptoGRU: Low Latency Privacy-Preserving Text Analysis With GRU

Helix: Algorithm/Architecture Co-design for Accelerating Nanopore Genome Base-calling

AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference

Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning

Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data

Liger: Combining Interaction Paradigms for Visual Analysis

SHE: A Fast and Accurate Privacy-Preserving Deep Neural Network Via Leveled TFHE and Logarithmic Data Representation

A GraphBLAS Approach for Subgraph Counting

AutoQB: AutoML for Network Quantization and Binarization on Mobile Devices

Pose Invariant 3D Face Reconstruction

High-Performance Massive Subgraph Counting using Pipelined Adaptive-Group Communication

PT-Spike: A Precise-Time-Dependent Single Spike Neuromorphic Architecture with Efficient Supervised Learning

DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework