图 书 馆开课单位--芝加哥丰田技术学院
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1Online Learning and Game Theory[在线学习与博弈论]
Adam Kalai(芝加哥丰田技术学院) We consider online learning and its relationship to game theory. In an online decision-making problem, as in Singer's lecture, one typically makes...
热度：35
Adam Kalai(芝加哥丰田技术学院) We consider online learning and its relationship to game theory. In an online decision-making problem, as in Singer's lecture, one typically makes...
热度：35
2Continuous Markov Random Fields for Robust Stereo Estimation[用于鲁棒立体声预估的马尔科夫连续随机场]
Laurent Itti;Koichiro Yamaguchi; Ramin Zabih(芝加哥丰田技术学院) In this paper we present a novel slanted-plane model which reasons jointly about occlusion boundaries as well as depth. We formulate the problem as on...
热度：48
Laurent Itti;Koichiro Yamaguchi; Ramin Zabih(芝加哥丰田技术学院) In this paper we present a novel slanted-plane model which reasons jointly about occlusion boundaries as well as depth. We formulate the problem as on...
热度：48
3Factoring Speech into Linguistic Features[语音分解成语言特点]
Karen Livescu(芝加哥丰田技术学院) Spoken language technologies, such as automatic speech recognition and synthesis, typically treat speech as a string of "phones". In contras...
热度：49
Karen Livescu(芝加哥丰田技术学院) Spoken language technologies, such as automatic speech recognition and synthesis, typically treat speech as a string of "phones". In contras...
热度：49
4Regularization Strategies and Empirical Bayesian Learning for MKL[MKL的正则化策略和贝叶斯经验学习]
Ryota Tomioka(芝加哥丰田技术学院) Multiple kernel learning (MKL) has received considerable attention recently. In this paper, we show how different MKL algorithms can be understood as ...
热度：47
Ryota Tomioka(芝加哥丰田技术学院) Multiple kernel learning (MKL) has received considerable attention recently. In this paper, we show how different MKL algorithms can be understood as ...
热度：47
5SVM Optimization: Inverse Dependence on Training Set Size[支持向量机优化：对训练集大小的逆依赖]
Nathan Srebro(芝加哥丰田技术学院) We discuss how the runtime of SVM optimization should decrease as the size of the training data increases. We present theoretical and empirical result...
热度：79
Nathan Srebro(芝加哥丰田技术学院) We discuss how the runtime of SVM optimization should decrease as the size of the training data increases. We present theoretical and empirical result...
热度：79
6Graphical Models for Speech Recognition: Articulatory and Audio-Visual Models[语音识别的图形化模型：发音和音频视觉模型]
Karen Livescu(芝加哥丰田技术学院) Since the 1980s, the main approach to automatic speech recognition has been using hidden Markov models (HMMs), in which each state corresponds to a ph...
热度：56
Karen Livescu(芝加哥丰田技术学院) Since the 1980s, the main approach to automatic speech recognition has been using hidden Markov models (HMMs), in which each state corresponds to a ph...
热度：56
7Training Structured Predictors for Novel Loss Functions[新的损失函数的训练结构的预测]
David McAllester(芝加哥丰田技术学院) As a motivation we consider the PASCAL image segmentation challenge. Given an image and a target class, such as person, the challenge is to segment th...
热度：61
David McAllester(芝加哥丰田技术学院) As a motivation we consider the PASCAL image segmentation challenge. Given an image and a target class, such as person, the challenge is to segment th...
热度：61
8On Multilabel Classification and Ranking with Partial Feedback[细粒度的分类和部分反馈的排名]
Francesco Orabona(芝加哥丰田技术学院) We present a novel multilabel/ranking algorithm working in partial information settings. The algorithm is based on 2nd-order descent methods, and reli...
热度：36
Francesco Orabona(芝加哥丰田技术学院) We present a novel multilabel/ranking algorithm working in partial information settings. The algorithm is based on 2nd-order descent methods, and reli...
热度：36
9The Projectron: a Bounded Kernel-Based Perceptron[一个有界的projectron：基于核感知器]
Francesco Orabona(芝加哥丰田技术学院) We present a discriminative online algorithm with a bounded memory growth, which is based on the kernel-based Perceptron. Generally, the required memo...
热度：84
Francesco Orabona(芝加哥丰田技术学院) We present a discriminative online algorithm with a bounded memory growth, which is based on the kernel-based Perceptron. Generally, the required memo...
热度：84
10Online-Batch Strongly Convex Multi Kernel Learning[在线批量强凸多核学习]
Francesco Orabona(芝加哥丰田技术学院) Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled approach to combine multiple cues, and to o...
热度：41
Francesco Orabona(芝加哥丰田技术学院) Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled approach to combine multiple cues, and to o...
热度：41