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Artificial Intelligence and Machine Learning

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Artificial Intelligence and Machine Learning

2019

Video Stitching for Linear Camera Arrays

Wei-Sheng Lai, Orazio Gallo, Jinwei Gu, Deqing Sun, Ming-Hsuan Yang, Jan Kautz

GPU-Accelerated Atari Emulation for Reinforcement Learning

Steven Dalton, Iuri Frosio, Michael Garland

ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection

Saurabh Jha, Subho S. Banerjee, Timothy Tsai, Siva Hari, Michael B. Sullivan, Zbigniew T. Kalbarczyk, Steve Keckler, Ravishankar K. Iyer

ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection

Saurabh Jha, Subho Banerjee, Timothy Tsai, Siva Hari, Michael B. Sullivan, Zbigniew T. Kalbarczyk, Steve Keckler, Ravishankar K. Iyer

Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera

Chao Liu, Jinwei Gu, Kihwan Kim, Srinivasa Narasimhan, Jan Kautz

PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image

Chen Liu, Kihwan Kim, Jinwei Gu, Yasutaka Furukawa, Jan Kautz

A Style-Based Generator Architecture for Generative Adversarial Networks

Tero Karras, Samuli Laine, Timo Aila

Pixel-Adaptive Convolutional Neural Networks

Hang Su, Varun Jampani, Deqing Sun, Orazio Gallo, Erik-Learned Miller, Jan Kautz

Semantic Image Synthesis with Spatially-Adaptive Normalization

Taesung Park, Ming-Yu Liu, Ting-Chun Wang, Jun-Yan Zhu

STEP: Spatio-Temporal Progressive Learning for Video Action Detection

Xitong Yang, Xiaodong Yang, Ming-Yu Liu, Fanyi Xiao, Larry Davis, Jan Kautz

Joint Discriminative and Generative Learning for Person Re-identification

Zhedong Zheng, Xiaodong Yang, Zhiding Yu, Liang Zheng, Yi Yang, Jan Kautz

Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

Anurag Ranjan, Varun Jampani, Lukas Balles, Kihwan Kim, Deqing Sun, Jonas Wulff, Michael J. Black

Importance Estimation for Neural Network Pruning

Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, Jan Kautz

A 0.11 pJ/Op, 0.32-128 TOPS, Scalable Multi-Chip-Module-based Deep Neural Network Accelerator with Ground-Reference Signaling in 16nm

Brian Zimmer, Rangharajan Venkatesan, Sophia Shao, Jason Clemons, Matt Fojtik, Ted Jiang, Ben Keller, Alicia Klinefelter, Nathaniel Pinckney, Priyanka Raina, Stephen Tell, Yanqing Zhang, William Dally, Joel Emer, Tom Gray, Steve Keckler, Brucek Khailany

DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement

Yibo Lin, Shounak Dhar, Wuxi Li, Haoxing (Mark) Ren, Brucek Khailany, David Z. Pan

DAC 2019 Best Paper Award

High Performance Graph Convolutional Networks with Applications in Testability Analysis

Yuzhe Ma, Haoxing (Mark) Ren, Brucek Khailany, Harbinder Sikka, Lijuan Luo, Karthikeyan Natarajan, Bei Yu

PRIMAL: Power Inference using Machine Learning

Yuan Zhou, Haoxing (Mark) Ren, Yanqing Zhang, Ben Keller, Brucek Khailany, Zhiru Zhang

Analog/Mixed-Signal Hardware Error Modeling for Deep Learning Inference

Angad S. Rekhi, Brian Zimmer, Nikola Nedovic, Nigxi Liu, Rangharajan Venkatesan, Miaorong Wang, Brucek Khailany, William Dally, Tom Gray

Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs

Esha Choukse, Michael B. Sullivan, Mike O'Connor, Mattan Erez, Jeff Pool, David Nellans, Stephen W. Keckler

DeLTA: GPU Performance Model for Deep Learning Applications with In-depth Memory System Traffic Analysis

Sankug Lym, Donghyuk Lee, Niladrish Chatterjee, Mike O'Connor, Mattan Erez

Routability-Driven Macro Placement with Embedded CNN-Based Prediction Model

Yu-Hung Huang, Zhiyao Xie, Guan-Qi Fang, Tao-Chun Yu, Haoxing (Mark) Ren, Shao-Yun Fang, Yiran Chen, Jiang Hu

Timeloop: A Systematic Approach to DNN Accelerator Evaluation

Angshuman Parashar, Priyanka Raina, Yakun Sophia Shao, Yu-Hsin Chen, Victor A. Ying, Anurag Mukkara, Rangharajan Venkatesan, Brucek Khailany, Steve Keckler, Joel Emer

Informative Object Annotations: Tell Me Something I Don't Know

Lior Bracha, Gal Chechik

Rethinking full connectivity in recurrent neural networks

Matthijs Van keirsbilck, Alex Keller, Xiaodong Yang

Metaoptimization on a Distributed System for Deep Reinforcement Learning

Greg Heinrich, Iuri Frosio

Unsupervised Stylish Image Description Generation via Domain Layer Norm

Cheng-Kuan Chen, Zhu-Feng Pan, Ming-Yu Liu, Min Sun

Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation

Deqing Sun, Xiaodong Yang, Ming-Yu Liu, Jan Kautz

A Fusion Approach for Multi-Frame Optical Flow Estimation

Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth, Jan Kautz

2018

Learning towards Minimum Hyperspherical Energy

Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song

Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction

Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson

Exascale Deep Learning for Climate Analytics

Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett Phillips, Ankur Mahesh, Michael Matheson, Jack Deslippe, Massimiliano Fatica, Prabhat, Michael Houston

RouteNet: Routability Prediction for Mixed-size Designs using Convolutional Neural Network

Zhiyao Xie, Yu-Hung Huang, Guan-Qi Fang, Haoxing (Mark) Ren, Shao-Yun Fang, Yiran Chen, Hu Jiang