开课单位--以色列理工学院
1
A 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
2
Selective 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:28
Ron Begleiter(以色列理工学院) Selective Sampling with Almost Optimal Guarantees for Learning to Rank from Pairwise Preferences
热度:28
3
Off-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
4
Context-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
5
The 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
6
Online 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
7
8
When 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
9
Landmarks 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
10
Bootstrapping 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