“Reading Task”版本间的差异

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|align="center"| ICML 2015 ||align="center"| Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network ||align="center"| - ||align="center"| -  
 
|align="center"| ICML 2015 ||align="center"| Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network ||align="center"| - ||align="center"| -  
 
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|align="center"| ICML 2015 ||align="center"| BilBOWA: Fast Bilingual Distributed Representations without Word Alignments ||align="center"| - ||align="center"| -  
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|align="center"| ICML 2015 ||align="center"| BilBOWA: Fast Bilingual Distributed Representations without Word Alignments ||align="center"| Chao Xing ||align="center"| -  
 
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|align="center"| ICML 2015 ||align="center"| Strongly Adaptive Online Learning ||align="center"| - ||align="center"| -  
 
|align="center"| ICML 2015 ||align="center"| Strongly Adaptive Online Learning ||align="center"| - ||align="center"| -  
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|align="center"| ICLR 2015 ||align="center"| Memory Networks||align="center"| - ||align="center"| -  
 
|align="center"| ICLR 2015 ||align="center"| Memory Networks||align="center"| - ||align="center"| -  
 
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|align="center"| ICLR 2015 ||align="center"| Generative Modeling of Convolutional Neural Networks||align="center"| - ||align="center"| -  
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|align="center"| ICLR 2015 ||align="center"| Generative Modeling of Convolutional Neural Networks||align="center"| ChaoYuan Zuo ||align="center"| -  
 
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|align="center"| ICLR 2015 ||align="center"| A Unified Perspective on Multi-Domain and Multi-Task Learning||align="center"| - ||align="center"| -  
 
|align="center"| ICLR 2015 ||align="center"| A Unified Perspective on Multi-Domain and Multi-Task Learning||align="center"| - ||align="center"| -  

2015年7月27日 (一) 14:17的最后版本

Affiliation Paper Name Principal Materials
ICML 2015 From Word Embeddings To Document Distances - -
ICML 2015 Weight Uncertainty in Neural Network - -
ICML 2015 Long Short-Term Memory Over Recursive Structures - -
ICML 2015 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift - -
ICML 2015 Learning Transferable Features with Deep Adaptation Networks - -
ICML 2015 Learning Word Representations with Hierarchical Sparse Coding - -
ICML 2015 DRAW: A Recurrent Neural Network For Image Generation - -
ICML 2015 Unsupervised Learning of Video Representations using LSTMs - -
ICML 2015 MADE: Masked Autoencoder for Distribution Estimation - -
ICML 2015 Hashing for Distributed Data - -
ICML 2015 Is Feature Selection Secure against Training Data Poisoning? - -
ICML 2015 Mind the duality gap: safer rules for the Lasso - -
ICML 2015 PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data - -
ICML 2015 Generalization error bounds for learning to rank: Does the length of document lists matter? - -
ICML 2015 Classification with Low Rank and Missing Data - -
ICML 2015 Functional Subspace Clustering with Application to Time Series - -
ICML 2015 Abstraction Selection in Model-based Reinforcement Learning - -
ICML 2015 Learning Local Invariant Mahalanobis Distances - -
ICML 2015 A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate - -
ICML 2015 Learning from Corrupted Binary Labels via Class-Probability Estimation - -
ICML 2015 On the Relationship between Sum-Product Networks and Bayesian Networks - -
ICML 2015 Efficient Training of LDA on a GPU by Mean-for-Mode Estimation - -
ICML 2015 A low variance consistent test of relative dependency - -
ICML 2015 Streaming Sparse Principal Component Analysis - -
ICML 2015 How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? - -
ICML 2015 Online Learning of Eigenvectors - -
ICML 2015 Asymmetric Transfer Learning with Deep Gaussian Processes - -
ICML 2015 Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network - -
ICML 2015 BilBOWA: Fast Bilingual Distributed Representations without Word Alignments Chao Xing -
ICML 2015 Strongly Adaptive Online Learning - -
ICML 2015 Cascading Bandits: Learning to Rank in the Cascade Model - -
ICML 2015 Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM - -
ICML 2015 Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models - -
ICML 2015 Multi-Task Learning for Subspace Segmentation - -
ICML 2015 Convex Formulation for Learning from Positive and Unlabeled Data - -
ICML 2015 Alpha-Beta Divergences Discover Micro and Macro Structures in Data - -
ICML 2015 On Greedy Maximization of Entropy - -
ICML 2015 The Hedge Algorithm on a Continuum - -
ICML 2015 MRA-based Statistical Learning from Incomplete Rankings - -
ICML 2015 A Linear Dynamical System Model for Text - -
ICML 2015 HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades - -
ICML 2015 Support Matrix Machines - -
ICML 2015 Unsupervised Domain Adaptation by Backpropagation - -
ICML 2015 The Ladder: A Reliable Leaderboard for Machine Learning Competitions - -
ICML 2015 On Deep Multi-View Representation Learning - -
ICML 2015 A Probabilistic Model for Dirty Multi-task Feature Selection - -
ICML 2015 Deep Edge-Aware Filters - -
ICLR 2015 EMBEDDING ENTITIES AND RELATIONS FOR LEARNING AND INFERENCE IN KNOWLEDGE BASES. - -
ICLR 2015 TECHNIQUES FOR LEARNING BINARY STOCHASTIC FEEDFORWARD NEURAL NETWORKS. - -
ICLR 2015 Joint RNN-Based Greedy Parsing and Word Composition - -
ICLR 2015 Scheduled denoising autoencoders - -
ICLR 2015 Adam: A Method for Stochastic Optimization - -
ICLR 2015 Modeling Compositionality with Multiplicative Recurrent Neural Networks - -
ICLR 2015 Explaining and Harnessing Adversarial Examples - -
ICLR 2015 Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition - -
ICLR 2015 Deep Structured Output Learning for Unconstrained Text Recognition - -
ICLR 2015 Zero-bias autoencoders and the benefits of co-adapting features - -
ICLR 2015 Understanding Locally Competitive Networks - -
ICLR 2015 Leveraging Monolingual Data for Crosslingual Compositional Word Representations - -
ICLR 2015 Word Representations via Gaussian Embedding - -
ICLR 2015 Qualitatively characterizing neural network optimization problems - -
ICLR 2015 Memory Networks - -
ICLR 2015 Generative Modeling of Convolutional Neural Networks ChaoYuan Zuo -
ICLR 2015 A Unified Perspective on Multi-Domain and Multi-Task Learning - -
ICLR 2015 Learning Non-deterministic Representations with Energy-based Ensembles - -
ICLR 2015 Diverse Embedding Neural Network Language Models - -
ICLR 2015 Hot Swapping for Online Adaptation of Optimization Hyperparameters - -
ICLR 2015 Representation Learning for cold-start recommendation - -
ICLR 2015 On the Stability of Deep Networks - -
ICLR 2015 Stochastic Descent Analysis of Representation Learning Algorithms - -
ICLR 2015 Deep metric learning using Triplet network - -
ICLR 2015 Learning Longer Memory in Recurrent Neural Networks - -
ICLR 2015 Inducing Semantic Representation from Text by Jointly Predicting and Factorizing Relations - -
ICLR 2015 NICE: Non-linear Independent Components Estimation - -
ICLR 2015 Tailoring Word Embeddings for Bilexical Predictions: An Experimental Comparison - -
ICLR 2015 On Learning Vector Representations in Hierarchical Label Spaces - -
ICLR 2015 Real-World Font Recognition Using Deep Network and Domain Adaptation - -
ICLR 2015 Algorithmic Robustness for Learning via (ε,γ,τ)-Good Similarity Functions - -
ICLR 2015 Score Function Features for Discriminative Learning - -
ICLR 2015 Parallel training of DNNs with Natural Gradient and Parameter Averaging - -
ICLR 2015 A Generative Model for Deep Convolutional Learning - -
ICLR 2015 Random Forests Can Hash - -
ICLR 2015 Provable Methods for Training Neural Networks with Sparse Connectivity - -
ICLR 2015 Deep learning with Elastic Averaging SGD - -
ICLR 2015 Example Selection For Dictionary Learning - -
ICLR 2015 Unsupervised Domain Adaptation with Feature Embeddings - -