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		<id>http://index.cslt.org/mediawiki/index.php?action=history&amp;feed=atom&amp;title=Asr-read-icml</id>
		<title>Asr-read-icml - 版本历史</title>
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		<updated>2026-04-14T15:28:08Z</updated>
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	<entry>
		<id>http://index.cslt.org/mediawiki/index.php?title=Asr-read-icml&amp;diff=15927&amp;oldid=prev</id>
		<title>2015年7月22日 (三) 10:57 Cslt</title>
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				<updated>2015-07-22T10:57:04Z</updated>
		
		<summary type="html">&lt;p&gt;&lt;/p&gt;
&lt;table class='diff diff-contentalign-left'&gt;
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				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;←上一版本&lt;/td&gt;
				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;2015年7月22日 (三) 10:57的版本&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;第1行：&lt;/td&gt;
&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;第1行：&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;&amp;lt;pre&amp;gt;&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;From Word Embeddings To Document Distances&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;From Word Embeddings To Document Distances&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;Weight Uncertainty in Neural Network&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;Weight Uncertainty in Neural Network&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;第48行：&lt;/td&gt;
&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;第49行：&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;A Probabilistic Model for Dirty Multi-task Feature Selection&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;A Probabilistic Model for Dirty Multi-task Feature Selection&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;Deep Edge-Aware Filters&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;Deep Edge-Aware Filters&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;&amp;lt;/pre&amp;gt;&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;/table&gt;</summary>
		<author><name>Cslt</name></author>	</entry>

	<entry>
		<id>http://index.cslt.org/mediawiki/index.php?title=Asr-read-icml&amp;diff=15926&amp;oldid=prev</id>
		<title>Cslt：以“From Word Embeddings To Document Distances Weight Uncertainty in Neural Network Long Short-Term Memory Over Recursive Structures Batch Normalization: Accelerating De...”为内容创建页面</title>
		<link rel="alternate" type="text/html" href="http://index.cslt.org/mediawiki/index.php?title=Asr-read-icml&amp;diff=15926&amp;oldid=prev"/>
				<updated>2015-07-22T10:56:49Z</updated>
		
		<summary type="html">&lt;p&gt;以“From Word Embeddings To Document Distances Weight Uncertainty in Neural Network Long Short-Term Memory Over Recursive Structures Batch Normalization: Accelerating De...”为内容创建页面&lt;/p&gt;
&lt;p&gt;&lt;b&gt;新页面&lt;/b&gt;&lt;/p&gt;&lt;div&gt;From Word Embeddings To Document Distances&lt;br /&gt;
Weight Uncertainty in Neural Network&lt;br /&gt;
Long Short-Term Memory Over Recursive Structures&lt;br /&gt;
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift&lt;br /&gt;
Learning Transferable Features with Deep Adaptation Networks&lt;br /&gt;
Learning Word Representations with Hierarchical Sparse Coding&lt;br /&gt;
DRAW: A Recurrent Neural Network For Image Generation&lt;br /&gt;
Unsupervised Learning of Video Representations using LSTMs&lt;br /&gt;
MADE: Masked Autoencoder for Distribution Estimation&lt;br /&gt;
Hashing for Distributed Data&lt;br /&gt;
Is Feature Selection Secure against Training Data Poisoning?&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Mind the duality gap: safer rules for the Lasso&lt;br /&gt;
PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data&lt;br /&gt;
Generalization error bounds for learning to rank: Does the length of document lists matter?&lt;br /&gt;
Classification with Low Rank and Missing Data&lt;br /&gt;
Functional Subspace Clustering with Application to Time Series&lt;br /&gt;
Abstraction Selection in Model-based Reinforcement Learning&lt;br /&gt;
Learning Local Invariant Mahalanobis Distances&lt;br /&gt;
A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate&lt;br /&gt;
Learning from Corrupted Binary Labels via Class-Probability Estimation&lt;br /&gt;
On the Relationship between Sum-Product Networks and Bayesian Networks&lt;br /&gt;
Efficient Training of LDA on a GPU by Mean-for-Mode Estimation&lt;br /&gt;
A low variance consistent test of relative dependency&lt;br /&gt;
Streaming Sparse Principal Component Analysis&lt;br /&gt;
How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances?&lt;br /&gt;
Online Learning of Eigenvectors&lt;br /&gt;
Asymmetric Transfer Learning with Deep Gaussian Processes&lt;br /&gt;
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network&lt;br /&gt;
BilBOWA: Fast Bilingual Distributed Representations without Word Alignments&lt;br /&gt;
Strongly Adaptive Online Learning&lt;br /&gt;
Cascading Bandits: Learning to Rank in the Cascade Model&lt;br /&gt;
Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM&lt;br /&gt;
Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models&lt;br /&gt;
Multi-Task Learning for Subspace Segmentation&lt;br /&gt;
Convex Formulation for Learning from Positive and Unlabeled Data&lt;br /&gt;
Alpha-Beta Divergences Discover Micro and Macro Structures in Data&lt;br /&gt;
On Greedy Maximization of Entropy&lt;br /&gt;
The Hedge Algorithm on a Continuum&lt;br /&gt;
MRA-based Statistical Learning from Incomplete Rankings&lt;br /&gt;
A Linear Dynamical System Model for Text&lt;br /&gt;
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades&lt;br /&gt;
Support Matrix Machines&lt;br /&gt;
Unsupervised Domain Adaptation by Backpropagation&lt;br /&gt;
The Ladder: A Reliable Leaderboard for Machine Learning Competitions&lt;br /&gt;
On Deep Multi-View Representation Learning&lt;br /&gt;
A Probabilistic Model for Dirty Multi-task Feature Selection&lt;br /&gt;
Deep Edge-Aware Filters&lt;/div&gt;</summary>
		<author><name>Cslt</name></author>	</entry>

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