图 书 馆开课单位--以色列理工学院
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1A Minimal Variance Estimator for the Cardinality of Big Data Set Intersection[大数据集交集基数的最小方差估计器]
Aviv Yehezkel(以色列理工学院) In recent years there has been a growing interest in developing "streaming algorithms" for efficient processing and querying of continuous d...
热度：20
Aviv Yehezkel(以色列理工学院) In recent years there has been a growing interest in developing "streaming algorithms" for efficient processing and querying of continuous d...
热度：20
2Selective Sampling with Almost Optimal Guarantees for Learning to Rank from Pairwise Preferences[具有几乎最优保证的选择性抽样用于学习从成对偏好排序]
Ron Begleiter(以色列理工学院) Selective Sampling with Almost Optimal Guarantees for Learning to Rank from Pairwise Preferences
热度：29
Ron Begleiter(以色列理工学院) Selective Sampling with Almost Optimal Guarantees for Learning to Rank from Pairwise Preferences
热度：29
3Off-policy Model-based Learning under Unknown Factored Dynamics[未知因素动态下基于非策略模型的学习]
Assaf Hallak(以色列理工学院) Off-policy learning in dynamic decision problems is essential for providing strong evidence that a new policy is better than the one in use. But how c...
热度：37
Assaf Hallak(以色列理工学院) Off-policy learning in dynamic decision problems is essential for providing strong evidence that a new policy is better than the one in use. But how c...
热度：37
4Context-Aware Saliency Detection[上下文感知显著性检测]
Lihi Zelnik-Manor(以色列理工学院) We propose a new type of saliency – context-aware saliency – which aims at detecting the image regions that represent the scene. This defi...
热度：120
Lihi Zelnik-Manor(以色列理工学院) We propose a new type of saliency – context-aware saliency – which aims at detecting the image regions that represent the scene. This defi...
热度：120
5The Sample Complexity of Dictionary Learning[字典学习的样本复杂性]
Daniel Vainsencher(以色列理工学院) A large set of signals can sometimes be described sparsely using a dictionary, that is, every element can be represented as a linear combination of fe...
热度：125
Daniel Vainsencher(以色列理工学院) A large set of signals can sometimes be described sparsely using a dictionary, that is, every element can be represented as a linear combination of fe...
热度：125
6Online Learning for Time Series Prediction[在线学习时间序列预测]
Elad Hazan(以色列理工学院) In this paper, we address the problem of predicting a time series using the ARMA (autoregressive moving average) model, under minimal assumptions on t...
热度：268
Elad Hazan(以色列理工学院) In this paper, we address the problem of predicting a time series using the ARMA (autoregressive moving average) model, under minimal assumptions on t...
热度：268
78When Abstractions Met Landmarks[当抽象遇到地标]
Carmel Domshlak(以色列理工学院) Abstractions and landmarks are two powerful mechanisms for devising admissible heuristics for classical planning. Here we aim at putting them together...
热度：65
Carmel Domshlak(以色列理工学院) Abstractions and landmarks are two powerful mechanisms for devising admissible heuristics for classical planning. Here we aim at putting them together...
热度：65
9Landmarks in Heuristic-Search Planning[启发式搜索规划中的地标]
Silvia Richter;Erez Karpas(以色列理工学院) The recent past has seen a resurge of interest in landmarks for heuristic-search planning. Landmarks are subgoals that have to become true at some poi...
热度：68
Silvia Richter;Erez Karpas(以色列理工学院) The recent past has seen a resurge of interest in landmarks for heuristic-search planning. Landmarks are subgoals that have to become true at some poi...
热度：68
10Bootstrapping Skills[自举技巧]
Daniel Mankowitz(以色列理工学院) The monolithic approach to policy representation in Markov Decision Processes (MDPs) looks for a single policy that can be represented as a function f...
热度：46
Daniel Mankowitz(以色列理工学院) The monolithic approach to policy representation in Markov Decision Processes (MDPs) looks for a single policy that can be represented as a function f...
热度：46