Publications

Our publications provide insight into some of our leading-edge research.

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2018

SPLATNet: Sparse Lattice Networks for Point Cloud Processing
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz
Making Convolutional Networks Recurrent for Visual Sequence Learning
Xiaodong Yang, Pavlo Molchanov, Jan Kautz
Deep Semantic Face Deblurring
Ziyi Shen, Wei-Sheng Lai, Tingfa Xu, Jan Kautz, Ming-Hsuan Yang
UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition
Kartik Hegde, Jiyong Yu, Rohit Agrawal, Mengjia Yan, Michael Pellauer, Christopher W. Fletcher
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training
Maohua Zhu, Jason Clemons, Jeff Pool, Minsoo Rhu, Steve Keckler, Yuan Xie
Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning
Yuval Atzmon, Gal Chechik
Reblur2Deblur: Deblurring Videos via Self-Supervised Learning
Huaijin Chen, Jinwei Gu, Orazio Gallo, Ming-Yu Liu, Ashok Veeraraghavan, Jan Kautz
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen
IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification
Sam Leroux, Pavlo Molchanov, Pieter Simoens, Bart Dhoedt, Thomas Breuel, Jan Kautz
Synchronous Multi-GPU Deep Learning with Low-Precision Communication: An Experimental Study
Demjan Grubic, Leo Tam, Dan Alistarh, Ce Zhang
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks
Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee, Jeff Pool, Youngeun Kwon, Steve Keckler
Stitch-X: An Accelerator Architecture for Exploiting Unstructured Sparsity in Deep Neural Networks
Ching-En Lee, Yakun Sophia Shao, Jie-Fang Zhang, Angshuman Parashar, Joel Emer, Steve Keckler, Zhengya Zhang
Learning Binary Residual Representations for Domain-specific Video Streaming
Yi-Hsuan Tsai, Ming-Yu Liu, Deqing Sun, Ming-Hsuan Yang, Jan Kautz
Learning Adaptive Parameter Tuning for Image Processing
Jingming Dong, Iuri Frosio, Jan Kautz

2017

Parallel Complexity of Forward and Backward Propagation
Maxim Naumov
Integral Equations and Machine Learning
Alex Keller, Ken Dahm
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks
Aditya Devarakonda, Maxim Naumov, Michael Garland
Unsupervised Image-to-Image Translation Networks
Ming-Yu Liu, Thomas Breuel, Jan Kautz
Learning Affinity via Spatial Propagation Networks
Sifei Liu, Shalini De Mello, Jinwei Gu, Guangyu Zhong, Ming-Hsuan Yang, Jan Kautz
Understanding Error Propagation in Deep Learning Neural Network (DNN) Accelerators and Applications
Guanpeng Li, Siva Hari, Michael B. Sullivan, Timothy Tsai, Karthik Pattabiraman, Joel Emer, Steve Keckler
Parallel Jaccard and Related Graph Clustering Techniques
Alexandre Fender, Nahid Emad, Serge Petiton, Joe Eaton, Maxim Naumov
Learning to Super-Resolve Blurry Face and Text Images
Xiangyu Xu, Deqing Sun, Jinshan Pan, Yujin Zhang, Hanspeter Pfister, Ming-Hsuan Yang
A Lightweight Approach for On-the-Fly Reflectance Estimation
Kihwan Kim, Jinwei Gu, Stephen Tyree, Pavlo Molchanov, Matthias Nießner, Jan Kautz
Group online adaptive learning
Alon Zweig, Gal Chechik
Feedforward and Recurrent Neural Networks Backward Propagation and Hessian in Matrix Form
Maxim Naumov
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents
Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao , Meng-Li Shi, Ming-Yu Liu, Min Sun
Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion
Tero Karras, Timo Aila, Samuli Laine, Antti Herva, Jaakko Lehtinen
Parallel Modularity Clustering
Alexandre Fender, Nahid Emad, Serge Petiton, Maxim Naumov
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks
Angshuman Parashar, Minsoo Rhu, Anurag Mukkara, Antonio Puglielli, Rangharajan Venkatesan, Brucek Khailany, Joel Emer, Steve Keckler, William Dally
Pruning Convolutional Neural Networks for Resource Efficient Inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, Jan Kautz
Context-aware Captions from Context-agnostic Supervision
Ramakrishna Vedantam, Samy Bengio, Kevin Murphy, Devi Parikh, Gal Chechik
Learning From Noisy Large-Scale Datasets With Minimal Supervision
Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, Serge Belongie