Search Results for author: JinJun Xiong

Found 74 papers, 25 papers with code

xER: An Explainable Model for Entity Resolution using an Efficient Solution for the Clique Partitioning Problem

no code implementations NAACL (TrustNLP) 2021 Samhita Vadrevu, Rakesh Nagi, JinJun Xiong, Wen-mei Hwu

In this paper, we use Clique Partition- ing Problem (CPP), which is an Integer Pro- gram (IP) to formulate ER as a graph partition- ing problem and then highlight the explainable nature of this method.

Entity Resolution graph partitioning

HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computation

no code implementations28 Dec 2021 Xinheng Liu, Yao Chen, Prakhar Ganesh, Junhao Pan, JinJun Xiong, Deming Chen

Quantization for Convolutional Neural Network (CNN) has shown significant progress with the intention of reducing the cost of computation and storage with low-bitwidth data inputs.

Quantization

Graph Neural Network Training with Data Tiering

no code implementations10 Nov 2021 Seung Won Min, Kun Wu, Mert Hidayetoğlu, JinJun Xiong, Xiang Song, Wen-mei Hwu

With our data tiering method, we additionally provide a new data placement and access strategy to further minimize the CPU-GPU communication overhead.

Fraud Detection

MLHarness: A Scalable Benchmarking System for MLCommons

no code implementations9 Nov 2021 Yen-Hsiang Chang, Jianhao Pu, Wen-mei Hwu, JinJun Xiong

With the society's growing adoption of machine learning (ML) and deep learning (DL) for various intelligent solutions, it becomes increasingly imperative to standardize a common set of measures for ML/DL models with large scale open datasets under common development practices and resources so that people can benchmark and compare models quality and performance on a common ground.

Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks

no code implementations12 Oct 2021 Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, JinJun Xiong

Moreover, when the algorithm for training a pruned neural network is specified as an (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned weights in the hidden layer.

Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow

no code implementations8 Sep 2021 Zhiding Liang, Zhepeng Wang, Junhuan Yang, Lei Yang, JinJun Xiong, Yiyu Shi, Weiwen Jiang

Specifically, this paper targets quantum neural network (QNN), and proposes to learn the errors in the training phase, so that the identified QNN model can be resilient to noise.

Open Relation Modeling: Learning to Define Relations between Entities

no code implementations20 Aug 2021 Jie Huang, Kevin Chen-Chuan Chang, JinJun Xiong, Wen-mei Hwu

In this paper, we introduce the Open Relation Modeling task - given two entities, generate a coherent sentence describing the relation between them.

Generic Neural Architecture Search via Regression

1 code implementation NeurIPS 2021 Yuhong Li, Cong Hao, Pan Li, JinJun Xiong, Deming Chen

Such a self-supervised regression task can effectively evaluate the intrinsic power of an architecture to capture and transform the input signal patterns, and allow more sufficient usage of training samples.

Image Classification Neural Architecture Search

Global Rhythm Style Transfer Without Text Transcriptions

no code implementations16 Jun 2021 Kaizhi Qian, Yang Zhang, Shiyu Chang, JinJun Xiong, Chuang Gan, David Cox, Mark Hasegawa-Johnson

In this paper, we propose AutoPST, which can disentangle global prosody style from speech without relying on any text transcriptions.

Representation Learning Style Transfer

Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach

1 code implementation ACL 2021 Jie Huang, Kevin Chen-Chuan Chang, JinJun Xiong, Wen-mei Hwu

To support a fine-grained domain without relying on a matching corpus for supervision, we develop hierarchical core-fringe learning, which learns core and fringe terms jointly in a semi-supervised manner contextualized in the hierarchy of the domain.

Heterogeneous Contrastive Learning

no code implementations19 May 2021 Lecheng Zheng, Yada Zhu, Jingrui He, JinJun Xiong

With the advent of big data across multiple high-impact applications, we are often facing the challenge of complex heterogeneity.

Contrastive Learning

Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection

1 code implementation29 Apr 2021 Jiachen Li, Bowen Cheng, Rogerio Feris, JinJun Xiong, Thomas S. Huang, Wen-mei Hwu, Humphrey Shi

Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based models that are supported by well-designed assignment methods based on the Intersection-over-Union~(IoU) metric.

Object Detection

Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design

no code implementations25 Mar 2021 Cong Hao, Jordan Dotzel, JinJun Xiong, Luca Benini, Zhiru Zhang, Deming Chen

Artificial intelligence (AI) technologies have dramatically advanced in recent years, resulting in revolutionary changes in people's lives.

Edge-computing

Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture

1 code implementation4 Mar 2021 Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayetoğlu, JinJun Xiong, Eiman Ebrahimi, Deming Chen, Wen-mei Hwu

In this work, we propose a novel GPU-oriented data communication approach for GCN training, where GPU threads directly access sparse features in host memory through zero-copy accesses without much CPU help.

Graph Convolutional Network Recommendation Systems

PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses

1 code implementation20 Jan 2021 Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayetoğlu, JinJun Xiong, Eiman Ebrahimi, Deming Chen, Wen-mei Hwu

While this process accounts for a significant portion of the training time, we find existing GNN implementations using popular deep neural network (DNN) libraries such as PyTorch are limited to a CPU-centric approach for the entire data preparation step.

Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks

no code implementations NeurIPS 2021 Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, JinJun Xiong

Moreover, as the algorithm for training a sparse neural network is specified as (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned model weights in the hidden layer.

Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search Space

1 code implementation1 Jan 2021 Yuhong Li, Cong Hao, Xiaofan Zhang, JinJun Xiong, Wen-mei Hwu, Deming Chen

This raises the question of whether we can find an effective proxy search space (PS) that is only a small subset of GS to dramatically improve RandomNAS’s search efficiency while at the same time keeping a good correlation for the top-performing architectures.

Image Classification Neural Architecture Search

TEMPI: An Interposed MPI Library with a Canonical Representation of CUDA-aware Datatypes

1 code implementation28 Dec 2020 Carl Pearson, Kun Wu, I-Hsin Chung, JinJun Xiong, Wen-mei Hwu

MPI derived datatypes are an abstraction that simplifies handling of non-contiguous data in MPI applications.

Distributed, Parallel, and Cluster Computing

When Machine Learning Meets Quantum Computers: A Case Study

3 code implementations18 Dec 2020 Weiwen Jiang, JinJun Xiong, Yiyu Shi

It is imminent to know how to design the quantum circuit for accelerating neural networks.

Image Classification

Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices

no code implementations14 Oct 2020 Cong Hao, Yao Chen, Xiaofan Zhang, Yuhong Li, JinJun Xiong, Wen-mei Hwu, Deming Chen

High quality AI solutions require joint optimization of AI algorithms, such as deep neural networks (DNNs), and their hardware accelerators.

Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases

1 code implementation ECCV 2020 Ren Wang, Gaoyuan Zhang, Sijia Liu, Pin-Yu Chen, JinJun Xiong, Meng Wang

When the training data are maliciously tampered, the predictions of the acquired deep neural network (DNN) can be manipulated by an adversary known as the Trojan attack (or poisoning backdoor attack).

At-Scale Sparse Deep Neural Network Inference with Efficient GPU Implementation

1 code implementation28 Jul 2020 Mert Hidayetoglu, Carl Pearson, Vikram Sharma Mailthody, Eiman Ebrahimi, JinJun Xiong, Rakesh Nagi, Wen-mei Hwu

This paper presents GPU performance optimization and scaling results for inference models of the Sparse Deep Neural Network Challenge 2020.

ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation

no code implementations18 Jul 2020 Tianchen Wang, Xiaowei Xu, JinJun Xiong, Qianjun Jia, Haiyun Yuan, Meiping Huang, Jian Zhuang, Yiyu Shi

Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions.

MRI segmentation

A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage

3 code implementations26 Jun 2020 Weiwen Jiang, JinJun Xiong, Yiyu Shi

We discover that, in order to make full use of the strength of quantum representation, it is best to represent data in a neural network as either random variables or numbers in unitary matrices, such that they can be directly operated by the basic quantum logical gates.

Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case

no code implementations ICML 2020 Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, JinJun Xiong

In this paper, we provide a theoretically-grounded generalizability analysis of GNNs with one hidden layer for both regression and binary classification problems.

General Classification

EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions

no code implementations6 May 2020 Yuhong Li, Cong Hao, Xiaofan Zhang, Xinheng Liu, Yao Chen, JinJun Xiong, Wen-mei Hwu, Deming Chen

We formulate the co-search problem by fusing DNN search variables and hardware implementation variables into one solution space, and maximize both algorithm accuracy and hardware implementation quality.

Neural Architecture Search

A Multi-Perspective Architecture for Semantic Code Search

no code implementations ACL 2020 Rajarshi Haldar, Lingfei Wu, JinJun Xiong, Julia Hockenmaier

The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories.

Code Search Text Matching

Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation

no code implementations2 Apr 2020 Zhonghao Wang, Yunchao Wei, Rogerior Feris, JinJun Xiong, Wen-mei Hwu, Thomas S. Huang, Humphrey Shi

A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i. e. reducing domain shift.

Domain Adaptation Semantic Segmentation

Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation

1 code implementation CVPR 2020 Zhonghao Wang, Mo Yu, Yunchao Wei, Rogerio Feris, JinJun Xiong, Wen-mei Hwu, Thomas S. Huang, Humphrey Shi

We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work.

Semantic Segmentation Unsupervised Domain Adaptation

Multi-Cycle-Consistent Adversarial Networks for CT Image Denoising

no code implementations27 Feb 2020 Jinglan Liu, Yukun Ding, JinJun Xiong, Qianjun Jia, Meiping Huang, Jian Zhuang, Bike Xie, Chun-Chen Liu, Yiyu Shi

For example, if the noise is large leading to significant difference between domain $X$ and domain $Y$, can we bridge $X$ and $Y$ with an intermediate domain $Z$ such that both the denoising process between $X$ and $Z$ and that between $Z$ and $Y$ are easier to learn?

Image Denoising Image-to-Image Translation +1

DLSpec: A Deep Learning Task Exchange Specification

no code implementations26 Feb 2020 Abdul Dakkak, Cheng Li, JinJun Xiong, Wen-mei Hwu

Deep Learning (DL) innovations are being introduced at a rapid pace.

MLModelScope: A Distributed Platform for Model Evaluation and Benchmarking at Scale

no code implementations19 Feb 2020 Abdul Dakkak, Cheng Li, JinJun Xiong, Wen-mei Hwu

Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that researchers are hard-pressed to analyze and study them.

Uncertainty-Aware Training of Neural Networks for Selective Medical Image Segmentation

no code implementations MIDL 2019 Yukun Ding, Jinglan Liu, Xiaowei Xu, Meiping Huang, Jian Zhuang, JinJun Xiong, Yiyu Shi

Existing selective segmentation methods, however, ignore this unique property of selective segmentation and train their DNN models by optimizing accuracy on the entire dataset.

Medical Image Segmentation

On Interpretability of Artificial Neural Networks: A Survey

1 code implementation8 Jan 2020 Fenglei Fan, JinJun Xiong, Mengzhou Li, Ge Wang

Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success in many important areas that deal with text, images, videos, graphs, and so on.

Medical Diagnosis

Tensor Recovery from Noisy and Multi-Level Quantized Measurements

no code implementations5 Dec 2019 Ren Wang, Meng Wang, JinJun Xiong

Existing works on tensor recovery have focused on data losses and random noises.

Quantization

Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration

no code implementations3 Dec 2019 Zirui Xu, Zhao Yang, JinJun Xiong, Jianlei Yang, Xiang Chen

In this paper, we propose Helios, a heterogeneity-aware FL framework to tackle the straggler issue.

Distributed, Parallel, and Cluster Computing

The Design and Implementation of a Scalable DL Benchmarking Platform

no code implementations19 Nov 2019 Cheng Li, Abdul Dakkak, JinJun Xiong, Wen-mei Hwu

MLModelScope defines abstractions for frameworks and supports board range of DL models and evaluation scenarios.

DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs (Extended)

no code implementations18 Nov 2019 Cheng Li, Abdul Dakkak, JinJun Xiong, Wen-mei Hwu

We show that DLBricks provides an accurate performance estimate for the DL models and reduces the benchmarking time across systems (e. g. within $95\%$ accuracy and up to $4. 4\times$ benchmarking time speedup on Amazon EC2 c5. xlarge).

Image Classification Machine Translation +1

NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving

no code implementations18 Nov 2019 Cong Hao, Yao Chen, Xinheng Liu, Atif Sarwari, Daryl Sew, Ashutosh Dhar, Bryan Wu, Dongdong Fu, JinJun Xiong, Wen-mei Hwu, Junli Gu, Deming Chen

The rapidly growing demands for powerful AI algorithms in many application domains have motivated massive investment in both high-quality deep neural network (DNN) models and high-efficiency implementations.

Autonomous Driving

Benanza: Automatic $μ$Benchmark Generation to Compute "Lower-bound" Latency and Inform Optimizations of Deep Learning Models on GPUs

no code implementations16 Nov 2019 Cheng Li, Abdul Dakkak, JinJun Xiong, Wen-mei Hwu

An important venue for such improvement is to profile the execution of these models and characterize their performance to identify possible optimization opportunities.

Faceted Hierarchy: A New Graph Type to Organize Scientific Concepts and a Construction Method

no code implementations WS 2019 Qingkai Zeng, Mengxia Yu, Wenhao Yu, JinJun Xiong, Yiyu Shi, Meng Jiang

On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts.

Face Recognition

MLModelScope: A Distributed Platform for ML Model Evaluation and Benchmarking at Scale

no code implementations25 Sep 2019 Cheng Li, Abdul Dakkak, JinJun Xiong, Wen-mei Hwu

Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that researchers are hard-pressed to analyze and study them.

CNAS: Channel-Level Neural Architecture Search

no code implementations25 Sep 2019 Heechul Lim, Min-Soo Kim, JinJun Xiong

There is growing interest in automating designing good neural network architectures.

Neural Architecture Search

PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space

no code implementations IJCNLP 2019 Omer Anjum, Hongyu Gong, Suma Bhat, Wen-mei Hwu, JinJun Xiong

Finding the right reviewers to assess the quality of conference submissions is a time consuming process for conference organizers.

Topic Models

SPGNet: Semantic Prediction Guidance for Scene Parsing

no code implementations ICCV 2019 Bowen Cheng, Liang-Chieh Chen, Yunchao Wei, Yukun Zhu, Zilong Huang, JinJun Xiong, Thomas Huang, Wen-mei Hwu, Honghui Shi

The multi-scale context module refers to the operations to aggregate feature responses from a large spatial extent, while the single-stage encoder-decoder structure encodes the high-level semantic information in the encoder path and recovers the boundary information in the decoder path.

Pose Estimation Scene Parsing +1

XSP: Across-Stack Profiling and Analysis of Machine Learning Models on GPUs

no code implementations19 Aug 2019 Cheng Li, Abdul Dakkak, JinJun Xiong, Wei Wei, Lingjie Xu, Wen-mei Hwu

Such an endeavor is challenging as the characteristics of an ML model depend on the interplay between the model, framework, system libraries, and the hardware (or the HW/SW stack).

Equipping Educational Applications with Domain Knowledge

no code implementations WS 2019 Tarek Sakakini, Hongyu Gong, Jong Yoon Lee, Robert Schloss, JinJun Xiong, Suma Bhat

One of the challenges of building natural language processing (NLP) applications for education is finding a large domain-specific corpus for the subject of interest (e. g., history or science).

Distractor Generation Language Modelling +1

SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection

1 code implementation25 Jun 2019 Xiaofan Zhang, Cong Hao, Haoming Lu, Jiachen Li, Yuhong Li, Yuchen Fan, Kyle Rupnow, JinJun Xiong, Thomas Huang, Honghui Shi, Wen-mei Hwu, Deming Chen

Developing artificial intelligence (AI) at the edge is always challenging, since edge devices have limited computation capability and memory resources but need to meet demanding requirements, such as real-time processing, high throughput performance, and high inference accuracy.

Object Detection

A Retrospective Recount of Computer Architecture Research with a Data-Driven Study of Over Four Decades of ISCA Publications

no code implementations22 Jun 2019 Omer Anjum, Wen-mei Hwu, JinJun Xiong

Recently we decided to conduct a more thorough study based on all past papers of International Symposium on Computer Architecture (ISCA) from 1973 to 2018, which resulted this article.

Natural Language Understanding

A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices

2 code implementations20 May 2019 Xiaofan Zhang, Cong Hao, Yuhong Li, Yao Chen, JinJun Xiong, Wen-mei Hwu, Deming Chen

Developing deep learning models for resource-constrained Internet-of-Things (IoT) devices is challenging, as it is difficult to achieve both good quality of results (QoR), such as DNN model inference accuracy, and quality of service (QoS), such as inference latency, throughput, and power consumption.

Object Detection

Challenges and Pitfalls of Machine Learning Evaluation and Benchmarking

no code implementations29 Apr 2019 Cheng Li, Abdul Dakkak, JinJun Xiong, Wen-mei Hwu

An increasingly complex and diverse collection of Machine Learning (ML) models as well as hardware/software stacks, collectively referred to as "ML artifacts", are being proposed - leading to a diverse landscape of ML.

FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge

2 code implementations9 Apr 2019 Cong Hao, Xiaofan Zhang, Yuhong Li, Sitao Huang, JinJun Xiong, Kyle Rupnow, Wen-mei Hwu, Deming Chen

While embedded FPGAs are attractive platforms for DNN acceleration on edge-devices due to their low latency and high energy efficiency, the scarcity of resources of edge-scale FPGA devices also makes it challenging for DNN deployment.

Object Detection

Document Similarity for Texts of Varying Lengths via Hidden Topics

1 code implementation ACL 2018 Hongyu Gong, Tarek Sakakini, Suma Bhat, JinJun Xiong

This is because of the lexical, contextual and the abstraction gaps between a long document of rich details and its concise summary of abstract information.

Text Matching

SCNN: A General Distribution based Statistical Convolutional Neural Network with Application to Video Object Detection

no code implementations15 Mar 2019 Tianchen Wang, JinJun Xiong, Xiaowei Xu, Yiyu Shi

By introducing a parameterized canonical model to model correlated data and defining corresponding operations as required for CNN training and inference, we show that SCNN can process multiple frames of correlated images effectively, hence achieving significant speedup over existing CNN models.

Video Object Detection

Revisiting the Evaluation of Uncertainty Estimation and Its Application to Explore Model Complexity-Uncertainty Trade-Off

1 code implementation5 Mar 2019 Yukun Ding, Jinglan Liu, JinJun Xiong, Yiyu Shi

Accurately estimating uncertainties in neural network predictions is of great importance in building trusted DNNs-based models, and there is an increasing interest in providing accurate uncertainty estimation on many tasks, such as security cameras and autonomous driving vehicles.

Autonomous Driving

Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era

no code implementations1 Feb 2019 Gabrielle Allen, Igor Andreoni, Etienne Bachelet, G. Bruce Berriman, Federica B. Bianco, Rahul Biswas, Matias Carrasco Kind, Kyle Chard, Minsik Cho, Philip S. Cowperthwaite, Zachariah B. Etienne, Daniel George, Tom Gibbs, Matthew Graham, William Gropp, Anushri Gupta, Roland Haas, E. A. Huerta, Elise Jennings, Daniel S. Katz, Asad Khan, Volodymyr Kindratenko, William T. C. Kramer, Xin Liu, Ashish Mahabal, Kenton McHenry, J. M. Miller, M. S. Neubauer, Steve Oberlin, Alexander R. Olivas Jr, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Bernard Schutz, Alex Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Brigitta M. Sipőcz, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei, Jack Wells, Timothy J. Williams, JinJun Xiong, Zhizhen Zhao

We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.

TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments

no code implementations24 Nov 2018 Abdul Dakkak, Cheng Li, Simon Garcia de Gonzalo, JinJun Xiong, Wen-mei Hwu

Deep neural networks (DNNs) have become core computation components within low latency Function as a Service (FaaS) prediction pipelines: including image recognition, object detection, natural language processing, speech synthesis, and personalized recommendation pipelines.

Distributed, Parallel, and Cluster Computing

Frustrated with Replicating Claims of a Shared Model? A Solution

no code implementations24 Nov 2018 Abdul Dakkak, Cheng Li, JinJun Xiong, Wen-mei Hwu

Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that model owners and evaluators are hard-pressed analyzing and studying them.

A Simple Non-i.i.d. Sampling Approach for Efficient Training and Better Generalization

no code implementations23 Nov 2018 Bowen Cheng, Yunchao Wei, Jiahui Yu, Shiyu Chang, JinJun Xiong, Wen-mei Hwu, Thomas S. Huang, Humphrey Shi

While training on samples drawn from independent and identical distribution has been a de facto paradigm for optimizing image classification networks, humans learn new concepts in an easy-to-hard manner and on the selected examples progressively.

General Classification Image Classification +5

SCOPE: C3SR Systems Characterization and Benchmarking Framework

2 code implementations18 Sep 2018 Carl Pearson, Abdul Dakkak, Cheng Li, Sarah Hashash, JinJun Xiong, Wen-mei Hwu

This report presents the design of the Scope infrastructure for extensible and portable benchmarking.

Performance

Universal Approximation with Quadratic Deep Networks

no code implementations31 Jul 2018 Fenglei Fan, JinJun Xiong, Ge Wang

(4) To approximate the same class of functions with the same error bound, is a quantized quadratic network able to enjoy a lower number of weights than a quantized conventional network?

Speech Recognition

TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection

no code implementations ECCV 2018 Yunchao Wei, Zhiqiang Shen, Bowen Cheng, Honghui Shi, JinJun Xiong, Jiashi Feng, Thomas Huang

This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box mining with Surrounding Segmentation Context (TS2C).

Multiple Instance Learning Weakly Supervised Object Detection +1

Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs

no code implementations23 Mar 2018 Chuanhao Zhuge, Xinheng Liu, Xiaofan Zhang, Sudeep Gummadi, JinJun Xiong, Deming Chen

Deep Convolutional Neural Networks have become a Swiss knife in solving critical artificial intelligence tasks.

Face Recognition

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

3 code implementations ECCV 2018 Bowen Cheng, Yunchao Wei, Honghui Shi, Rogerio Feris, JinJun Xiong, Thomas Huang

Recent region-based object detectors are usually built with separate classification and localization branches on top of shared feature extraction networks.

General Classification Multi-Task Learning

On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks

no code implementations ICLR 2019 Yukun Ding, Jinglan Liu, JinJun Xiong, Yiyu Shi

To the best of our knowledge, this is the first in-depth study on the complexity bounds of quantized neural networks.

Quantization

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