图 书 馆开课单位--卡内基梅隆大学
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11Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks[使用递归神经网络对实体、关系和文本进行推理链]
Rajarshi Das(卡内基梅隆大学) Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks
热度：18
Rajarshi Das(卡内基梅隆大学) Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks
热度：18
12Robust Top-k Multiclass SVM for Visual Category Recognition[用于视觉类别识别的鲁棒Top-k多类SVM（支持向量机）]
Xiaojun Chang(卡内基梅隆大学) Classification problems with a large number of classes inevitably involve overlapping or similar classes. In such cases it seems reasonable to allow t...
热度：33
Xiaojun Chang(卡内基梅隆大学) Classification problems with a large number of classes inevitably involve overlapping or similar classes. In such cases it seems reasonable to allow t...
热度：33
13Unsupervised Visual Representation Learning by Context Prediction[基于情境预测的无监督视觉表征学习]
Carl Doersch(卡内基梅隆大学) This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation. Given on...
热度：21
Carl Doersch(卡内基梅隆大学) This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation. Given on...
热度：21
14Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)[可扩展结构化高斯过程的核插值]
Andrew Gordon Wilson(卡内基梅隆大学) We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian process...
热度：72
Andrew Gordon Wilson(卡内基梅隆大学) We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian process...
热度：72
1516Multi-task and Transfer in RL[RL中的多任务和转移]
Emma Brunskill(卡内基梅隆大学) Multi-task and Transfer in RL
热度：20
Emma Brunskill(卡内基梅隆大学) Multi-task and Transfer in RL
热度：20
17SpotLight: Detecting Anomalies in Streaming Graphs[SpotLight：检测流图中的异常]
Dhivya Eswaran(卡内基梅隆大学) How do we spot interesting events from e-mail or transportation logs? How can we detect port scan or denial of service attacks from IP-IP communicatio...
热度：20
Dhivya Eswaran(卡内基梅隆大学) How do we spot interesting events from e-mail or transportation logs? How can we detect port scan or denial of service attacks from IP-IP communicatio...
热度：20
18Activity Forecasting[活动的预测]
Kris M. Kitani(卡内基梅隆大学) We address the task of inferring the future actions of people from noisy visual input. We denote this task activity forecasting. To achieve accurate a...
热度：31
Kris M. Kitani(卡内基梅隆大学) We address the task of inferring the future actions of people from noisy visual input. We denote this task activity forecasting. To achieve accurate a...
热度：31
19Learning, Information Extraction and the Web[学习、信息提取和网络]
Tom Mitchell(卡内基梅隆大学) Learning, Information Extraction and the Web
热度：19
Tom Mitchell(卡内基梅隆大学) Learning, Information Extraction and the Web
热度：19
20Large Scale Scene Matching for Graphics and Vision[用于图形和视觉的大规模场景匹配]
James Hays(卡内基梅隆大学) Large Scale Scene Matching for Graphics and Vision
热度：24
James Hays(卡内基梅隆大学) Large Scale Scene Matching for Graphics and Vision
热度：24