林元庆

林元庆拥有

Exploit All the Layers: Fast and Accurate CNN Object Detector With Scale Dependent Pooling and Cascaded Rejection ClAssifiers

Fan Yang, Wongun Choi and Yuanqing Lin in CVPR 2016

Fine-Grained Image ClAssification by Exploring Bipartite-Graph Labels

Feng Zhou and Yuanqing Lin in CVPR 2016

Fine-Grained Categorization and Dataset Bootstrapping Using Deep Metric Learning With Humans in the Loop

Yin Cui, Feng Zhou, Yuanqing Lin and Serge Belongie in CVPR 2016

Data-Driven 3D Voxel Patterns for Object Category Recognition

Yu Xiang, Wongun Choi, Yuanqing Lin and Silvio Savarese in CVPR 2015；oral

Hyper-clAss Augmented and Regularized Deep Learning for Fine-grained Image ClAssification

Saining Xie, Tianbao Yang, Xiaoyu Wang and Yuanqing Lin in CVPR 2015

Fine-Grained Visual Categorization via Multi-stage Metric Learning

Qi Qian, Rong Jin, Shenghuo Zhu and Yuanqing Lin in CVPR 2015

Regionlets for Generic Object Detection

Xiaoyu Wang, Ming Yang, Shenghuo Zhu and Yuanqing Lin in ICCV 2013;oral

Dense Object Reconstruction with Semantic Priors

Sid Yingze Bao, Manmohan Chandraker, Yuanqing Lin and Silvio Savarese in CVPR 2013; oral

Object-centric Spatial Pooling for Image ClAssification

Olga Russakovsky, Yuanqing Lin, Kai Yu and Fei-Fei Li in ECCV 2012

Multi-Component Models for Object Detection

Chunhui Gu, Pablo Arbelaez, Yuanqing Lin, Kai Yu and Jitendra Malik in ECCV 2012

Learning Image Representations from the Pixel Level via Hierarchical Sparse Coding

Kai Yu, Yuanqing Lin and John Lafferty in CVPR 2011

Large-scale Image ClAssification: Fast Feature Extraction and SVM Training

Yuanqing Lin, Liangliang Cao, Fengjun Lv, Shenghuo Zhu, Ming Yang, Timothee Cour, Kai Yu and Thomas Huang in CVPR 2011

Deep Coding Network

Yuanqing Lin, Tong Zhang, Shenghuo Zhu and Kai Yu in NIPS 2010

Learning sparse Markov network structure via ensemble-of-trees models

Yuanqing Lin, Shenghuo Zhu, Daniel D. Lee and Ben Taskar in AISTATS 2009

Blind channel identification for speech dereverberation using l1-norm sparse learning

Yuanqing Lin, Jingdong Chen, Youngmoo Kim, and Daniel D. Lee in NIPS 2007 ; oral

Blind sparse-nonnegative （BSN） channel identification for acoustic time-difference-of-arrival estimation

Yuanqing Lin, Jingdong Chen, Youngmoo Kim, and Daniel D. Lee in WASPAA 2007; oral

Multiplicative updates for nonnegative quadratic programming

Fei Sha, Yuanqing Lin, Lawrence K. Saul, and Daniel D. Lee in Neural Computation, 19（8）： 2004-2031 （2007）

Bayesian Regularization And Nonnegative Deconvolution （BRAND） for room impulse response estimation

Yuanqing Lin, Daniel D. Lee in IEEE Trans. Signal Processing, 54（3）： 839-847 （2006）

Bayesian Regularization And Nonnegative Deconvolution （BRAND） for acoustic echo cancellation

Yuanqing Lin, Daniel D. Lee in IEEE Trans. Signal Processing, 54（3）： 839-847 （2006）

Bayesian Regularization And Nonnegative Deconvolution （BRAND） for acoustic echo cancellation

Yuanqing Lin, Daniel D. Lee in WASPAA 2005

Bayesian regularization and nonnegative deconvolution for time delay estimation

Yuanqing Lin, Daniel D. Lee in NIPS 2005

“IDL希望将人工智能核心技术能做到统治级别，通过深度学习技术，不仅要做好图像识别基本技术（图像搜索、OCR、人脸识别），还要实现细粒度图像识别（如菜品识别）、视频分析、AR、医学图像分析等方面的突破。他认为，很多关键技术的决战期将是接下来的1-3年。”

——来源：2016中国人工智能大会

“人工智能这个行业，我认为是要大家一起往前发展，开放非常重要。比如我们的OCR（光学字符识别）技术，不说是世界最好的，但在国内肯定是最好的。开放后，大家就可以不用再去做了，直接可以在这个基础上继续建设。”

——来源：2016年8月，林元庆接受澎湃新闻采访