开课单位--加州大学欧文分校
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Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition[逃离鞍点——张量分解的在线随机梯度]
Furong Huang(加州大学欧文分校) We analyze stochastic gradient descent for optimizing non-convex functions. For non-convex functions often it is good to find a reasonable local minim...
热度:15
Furong Huang(加州大学欧文分校) We analyze stochastic gradient descent for optimizing non-convex functions. For non-convex functions often it is good to find a reasonable local minim...
热度:15
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Learning Overcomplete Latent Variable Models through Tensor Methods[用张量方法学习超完全潜变量模型]
Animashree Anandkumar(加州大学欧文分校) We provide guarantees for learning latent variable models emphasizing on the overcomplete regime, where the dimensionality of the latent space exceeds...
热度:20
Animashree Anandkumar(加州大学欧文分校) We provide guarantees for learning latent variable models emphasizing on the overcomplete regime, where the dimensionality of the latent space exceeds...
热度:20
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Lecture 20: Emotion, Motivation, and Volition 1[20讲:情感,动机,和意志1]
Michael Martinez(加州大学欧文分校) Emotion, Motivation, and Volition
热度:67
Michael Martinez(加州大学欧文分校) Emotion, Motivation, and Volition
热度:67
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The strength of evidence versus the power of belief: Are we all Bayesians?[证据与信仰的力量:我们都是独立的?]
Jessica Utts(加州大学欧文分校) Although statisticians have the job of making conclusions based on data, for many questions in science and society prior beliefs are strong and may ta...
热度:32
Jessica Utts(加州大学欧文分校) Although statisticians have the job of making conclusions based on data, for many questions in science and society prior beliefs are strong and may ta...
热度:32
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Communication Chains and Multitasking[通信链和多任务]
Norman Makoto Su(加州大学欧文分校) Observations revealed that information workers interact in“chains” of interactions, switching organizational contexts and communication me...
热度:38
Norman Makoto Su(加州大学欧文分校) Observations revealed that information workers interact in“chains” of interactions, switching organizational contexts and communication me...
热度:38
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Do We Need More Training Data or Better Models for Object Detection?[我们需要更多的训练数据还是更好的对象检测模型?]
Charless C. Fowlkes(加州大学欧文分校) Datasets for training object recognition systems are steadily growing in size. This paper investigates the question of whether existing detectors will...
热度:74
Charless C. Fowlkes(加州大学欧文分校) Datasets for training object recognition systems are steadily growing in size. This paper investigates the question of whether existing detectors will...
热度:74
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Analyzing Text and Social Network Data with Probabilistic Models[使用概率模型分析文本和社交网络数据]
Padhraic Smyth(加州大学欧文分校) Exploring and understanding large text and social network data sets is of increasing interest across multiple fields, in computer science, social scie...
热度:44
Padhraic Smyth(加州大学欧文分校) Exploring and understanding large text and social network data sets is of increasing interest across multiple fields, in computer science, social scie...
热度:44
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Analyzing 3D Objects in Cluttered Images[分析杂乱图像中的三维对象]
Mohsen Hejrati(加州大学欧文分校) We present an approach to detecting and analyzing the 3D configuration of objects in real-world images with heavy occlusion and clutter. We focus on t...
热度:69
Mohsen Hejrati(加州大学欧文分校) We present an approach to detecting and analyzing the 3D configuration of objects in real-world images with heavy occlusion and clutter. We focus on t...
热度:69
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