图 书 馆开课单位--斯坦福大学
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161Lecture 1 - The Motivation & Applications of Machine Learning[Lecture 1 -机器学习的动机与应用]
Andrew Ng(斯坦福大学) The Motivation & Applications of Machine Learning, The Logistics of the Class, The Definition of Machine Learning, The Overview of Supervised Lear...
热度：45
Andrew Ng(斯坦福大学) The Motivation & Applications of Machine Learning, The Logistics of the Class, The Definition of Machine Learning, The Overview of Supervised Lear...
热度：45
162Constrained Approximate Maximum Entropy Learning of Markov Random Fields[马尔可夫随机场的约束近似最大熵学习]
Varun Ganapathi(斯坦福大学) Parameter estimation in Markov random fields (MRFs) is a difficult task, in which inference over the network is run in the inner loop of a gradient de...
热度：59
Varun Ganapathi(斯坦福大学) Parameter estimation in Markov random fields (MRFs) is a difficult task, in which inference over the network is run in the inner loop of a gradient de...
热度：59
163Modeling real-world networks using Kronecker multiplication[使用Kronecker乘法对现实世界进行建模]
Jure Leskovec(斯坦福大学) Given a large, real graph, how can we generate a synthetic graph that matches its properties, i.e., it has similar degree distribution, similar (sma...
热度：31
Jure Leskovec(斯坦福大学) Given a large, real graph, how can we generate a synthetic graph that matches its properties, i.e., it has similar degree distribution, similar (sma...
热度：31
164Challenges in the Computational Discovery of Explanatory Scientific Models[解释性科学模型的计算发现中的挑战]
Pat Langley(斯坦福大学) The growing amount of scientific data has led to the increased use of computational discovery methods to understand and interpret them. However, most ...
热度：27
Pat Langley(斯坦福大学) The growing amount of scientific data has led to the increased use of computational discovery methods to understand and interpret them. However, most ...
热度：27
165Semantic text features from small world graphs[小世界地图的语义文本特征]
Jure Leskovec(斯坦福大学) We present a set of methods for creating a semantic representation from a collection of textual documents. Given a document collection we use a simple...
热度：83
Jure Leskovec(斯坦福大学) We present a set of methods for creating a semantic representation from a collection of textual documents. Given a document collection we use a simple...
热度：83
166Learning to Distinguish Valid Textual Entailments[学习区分有效的语篇蕴涵]
Marie-Catherine de Marneffe(斯坦福大学) This paper proposes a new architecture for textual inference in which finding a good alignment is separated from evaluating entailment. Current approa...
热度：60
Marie-Catherine de Marneffe(斯坦福大学) This paper proposes a new architecture for textual inference in which finding a good alignment is separated from evaluating entailment. Current approa...
热度：60
167Some Ideas for Formalizing Clustering[关于聚类形式化的几点思考 ]
Facundo Memoli(斯坦福大学) Despite being one of the most commonly used tools for unsupervised exploratory data analisys and despite its and extensive literature very little is k...
热度：56
Facundo Memoli(斯坦福大学) Despite being one of the most commonly used tools for unsupervised exploratory data analisys and despite its and extensive literature very little is k...
热度：56
168Understanding Gene Regulatory Networks and Their Variations[了解基因调控网络及其变异]
Daphne Koller(斯坦福大学) A key biological question is to uncover the regulatory networks in a cellular system and to understand how this network varies across individuals, cel...
热度：49
Daphne Koller(斯坦福大学) A key biological question is to uncover the regulatory networks in a cellular system and to understand how this network varies across individuals, cel...
热度：49
169Sparse Filtering[稀疏滤波]
Jiquan Ngiam(斯坦福大学) Unsupervised feature learning has been shown to be effective at learning representations that perform well on image, video and audio classification. H...
热度：141
Jiquan Ngiam(斯坦福大学) Unsupervised feature learning has been shown to be effective at learning representations that perform well on image, video and audio classification. H...
热度：141
170Active Classification based on Value of Classifier[基于分类器值的主动分类]
Tianshi Gao(斯坦福大学) Modern classification tasks usually involve many class labels and can be informed by a broad range of features. Many of these tasks are tackled by con...
热度：54
Tianshi Gao(斯坦福大学) Modern classification tasks usually involve many class labels and can be informed by a broad range of features. Many of these tasks are tackled by con...
热度：54