开课单位--波恩大学
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GestaltMatcher: rare disease matching using facial phenotypic descriptors[GestaltMatcher:使用面部表型描述符进行罕见疾病匹配]
Peter Krawitz(波恩大学) GestaltMatcher: rare disease matching using facial phenotypic descriptors
热度:21
Peter Krawitz(波恩大学) GestaltMatcher: rare disease matching using facial phenotypic descriptors
热度:21
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Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning[通过多任务学习将联合嵌入到以目标为导向的对话中]
Firas Kassawat(波恩大学) Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning
热度:23
Firas Kassawat(波恩大学) Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning
热度:23
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Pretrained Transformers for Simple Question Answering[预培训变压器,用于简单的问题解答]
Denis Lukovnikov(波恩大学) Pretrained Transformers for Simple Question Answering
热度:20
Denis Lukovnikov(波恩大学) Pretrained Transformers for Simple Question Answering
热度:20
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Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs[学习在知识图上对复杂问题回答的查询图排序]
Denis Lukovnikov(波恩大学) Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs
热度:25
Denis Lukovnikov(波恩大学) Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs
热度:25
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Semantic Data Integration for the SMT Manufacturing Process using SANSA Stack[基于SANSA堆栈的表面贴装制造过程语义数据集成]
Mohamed Nadjib Mami(波恩大学) Semantic Data Integration for the SMT Manufacturing Process using SANSA Stack
热度:32
Mohamed Nadjib Mami(波恩大学) Semantic Data Integration for the SMT Manufacturing Process using SANSA Stack
热度:32
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Incorporating Literals into Knowledge Graph Embeddings[将文字纳入知识图嵌入]
Denis Lukovnikov(波恩大学) Incorporating Literals into Knowledge Graph Embeddings
热度:27
Denis Lukovnikov(波恩大学) Incorporating Literals into Knowledge Graph Embeddings
热度:27
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The prediction error in functional regression[在功能回归预测误差]
Alois Kneip(波恩大学) The talk considers functional linear regression, where scalar responses Y are modeled in dependence of random functions. We propose a smoothing spline...
热度:174
Alois Kneip(波恩大学) The talk considers functional linear regression, where scalar responses Y are modeled in dependence of random functions. We propose a smoothing spline...
热度:174
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Boosting statistical network inference by incorporating prior knowledge from multiple sources[做好网络统计建设]
Paurush Praveen(波恩大学) Statistical learning methods, such as Bayesian Networks, have gained a high popularity to infer cellular networks from high throughput experiments. Ho...
热度:55
Paurush Praveen(波恩大学) Statistical learning methods, such as Bayesian Networks, have gained a high popularity to infer cellular networks from high throughput experiments. Ho...
热度:55
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Efficient Closed Pattern Mining in Strongly Accessible Set Systems[强可访问集系统中的高效闭模式挖掘]
Mario Boley(波恩大学) 强可访问集系统中的高效闭模式挖掘
热度:30
Mario Boley(波恩大学) 强可访问集系统中的高效闭模式挖掘
热度:30
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