1k citations
- Stanford UniversityUS33 papers
- Google (United States)US32 papers
- University of Illinois Urbana-ChampaignUS32 papers
- University of California, BerkeleyUS29 papers
- Massachusetts Institute of TechnologyUS28 papers
- Carnegie Mellon UniversityUS26 papers
- University of California San DiegoUS25 papers
- University College LondonGB22 papers
- Georgia Institute of TechnologyUS21 papers
- University of Maryland, College ParkUS21 papers
- University of MichiganUS18 papers
- University of Southern CaliforniaUS16 papers
78 papers · 1 filter
A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra
Ryan Senanayake, Fredrik Kjolstad, Changwan Hong +2
We address the problem of optimizing mixed sparse and dense tensor algebra in a compiler. We show that standard loop transformations, such as strip-mining, tiling, collapsing, para…
Controllable and Progressive Image Extrapolation
Yijun Li, Lu Jiang, Ming-Hsuan Yang
Image extrapolation aims at expanding the narrow field of view of a given image patch. Existing models mainly deal with natural scene images of homogeneous regions and have no cont…
cFineGAN: Unsupervised multi-conditional fine-grained image generation
Gunjan Aggarwal, Abhishek Sinha
We propose an unsupervised multi-conditional image generation pipeline: cFineGAN, that can generate an image conditioned on two input images such that the generated image preserves…
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution
Nikaash Puri, Sukriti Verma, Piyush Gupta +4
As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by hi…
CNN-generated images are surprisingly easy to spot... for now
Sheng-Yu Wang, Oliver Wang, Richard Zhang +2
In this work we ask whether it is possible to create a "universal" detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used.…
AtomNAS: Fine-Grained End-to-End Neural Architecture Search
Jieru Mei, Yingwei Li, Xiaochen Lian +4
Search space design is very critical to neural architecture search (NAS) algorithms. We propose a fine-grained search space comprised of atomic blocks, a minimal search unit that i…