Area of research
Computer Vision and Pattern Recognition · Artificial Intelligence
Research interest
Research focused on Artificial intelligence and Hash function, with related work in Segmentation, Binary code, Softmax function. Notable publications include 'Visualizing and Understanding Convolutional Networks', 'Indoor Segmentation and Support Inference from RGBD Images', and 'Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences'.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture
User Conditional Hashtag Prediction for Images
End-to-end integration of a Convolutional Network, Deformable Parts Model and non-maximum suppression
Visualizing and Understanding Convolutional Networks
Instance Segmentation of Indoor Scenes Using a Coverage Loss
Restoring an Image Taken through a Window Covered with Dirt or Rain
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
2013cited by 432position: last
RECONNAISSANCE OF THE HR 8799 EXOSOLAR SYSTEM. I. NEAR-INFRARED SPECTROSCOPY
Indoor Segmentation and Support Inference from RGBD Images
Multidimensional Spectral Hashing
Learning Binary Hash Codes for Large-Scale Image Search
Nonparametric image parsing using adaptive neighbor sets