“Reading Task”版本间的差异
来自cslt Wiki
| （相同用户的6个中间修订版本未显示） | |||
| 第3行： | 第3行： | ||
! Affiliation !! Paper Name !! Principal !! Materials | ! Affiliation !! Paper Name !! Principal !! Materials | ||
|- | |- | ||
| − | | align="center" | ICML 2015 || From Word Embeddings To Document Distances || - || | + | |
| + | |align="center"| ICML 2015 ||align="center"| From Word Embeddings To Document Distances ||align="center" | - ||align="center" | - | ||
|- | |- | ||
| − | | ICML 2015 || Weight Uncertainty in Neural Network || - || | + | |align="center"| ICML 2015 ||align="center"| Weight Uncertainty in Neural Network ||align="center"| - ||align="center"| - |
|- | |- | ||
| − | | ICML 2015 || | + | |align="center"| ICML 2015 ||align="center"| Long Short-Term Memory Over Recursive Structures ||align="center"| - ||align="center"| - |
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Learning Transferable Features with Deep Adaptation Networks ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Learning Word Representations with Hierarchical Sparse Coding ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| DRAW: A Recurrent Neural Network For Image Generation ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Unsupervised Learning of Video Representations using LSTMs ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| MADE: Masked Autoencoder for Distribution Estimation ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Hashing for Distributed Data ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Is Feature Selection Secure against Training Data Poisoning? ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Mind the duality gap: safer rules for the Lasso ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Generalization error bounds for learning to rank: Does the length of document lists matter? ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Classification with Low Rank and Missing Data ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Functional Subspace Clustering with Application to Time Series ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Abstraction Selection in Model-based Reinforcement Learning ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Learning Local Invariant Mahalanobis Distances ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Learning from Corrupted Binary Labels via Class-Probability Estimation ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| On the Relationship between Sum-Product Networks and Bayesian Networks ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Efficient Training of LDA on a GPU by Mean-for-Mode Estimation ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| A low variance consistent test of relative dependency ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Streaming Sparse Principal Component Analysis ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Online Learning of Eigenvectors ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Asymmetric Transfer Learning with Deep Gaussian Processes ||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"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| BilBOWA: Fast Bilingual Distributed Representations without Word Alignments ||align="center"| Chao Xing ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Strongly Adaptive Online Learning ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Cascading Bandits: Learning to Rank in the Cascade Model ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Multi-Task Learning for Subspace Segmentation ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Convex Formulation for Learning from Positive and Unlabeled Data ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Alpha-Beta Divergences Discover Micro and Macro Structures in Data ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| On Greedy Maximization of Entropy ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| The Hedge Algorithm on a Continuum ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| MRA-based Statistical Learning from Incomplete Rankings ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| A Linear Dynamical System Model for Text ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Support Matrix Machines ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Unsupervised Domain Adaptation by Backpropagation ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| The Ladder: A Reliable Leaderboard for Machine Learning Competitions ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| On Deep Multi-View Representation Learning ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| A Probabilistic Model for Dirty Multi-task Feature Selection ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICML 2015 ||align="center"| Deep Edge-Aware Filters ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| EMBEDDING ENTITIES AND RELATIONS FOR LEARNING AND INFERENCE IN KNOWLEDGE BASES.||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| TECHNIQUES FOR LEARNING BINARY STOCHASTIC FEEDFORWARD NEURAL NETWORKS.||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Joint RNN-Based Greedy Parsing and Word Composition ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Scheduled denoising autoencoders||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Adam: A Method for Stochastic Optimization||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Modeling Compositionality with Multiplicative Recurrent Neural Networks||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Explaining and Harnessing Adversarial Examples||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Deep Structured Output Learning for Unconstrained Text Recognition||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Zero-bias autoencoders and the benefits of co-adapting features||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Understanding Locally Competitive Networks||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Leveraging Monolingual Data for Crosslingual Compositional Word Representations||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Word Representations via Gaussian Embedding||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Qualitatively characterizing neural network optimization problems||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Memory Networks||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Generative Modeling of Convolutional Neural Networks||align="center"| ChaoYuan Zuo ||align="center"| - | ||
| + | |- | ||
| + | |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"| Learning Non-deterministic Representations with Energy-based Ensembles||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Diverse Embedding Neural Network Language Models||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Hot Swapping for Online Adaptation of Optimization Hyperparameters ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Representation Learning for cold-start recommendation||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| On the Stability of Deep Networks||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Stochastic Descent Analysis of Representation Learning Algorithms||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Deep metric learning using Triplet network ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Learning Longer Memory in Recurrent Neural Networks||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Inducing Semantic Representation from Text by Jointly Predicting and Factorizing Relations ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| NICE: Non-linear Independent Components Estimation ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Tailoring Word Embeddings for Bilexical Predictions: An Experimental Comparison ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| On Learning Vector Representations in Hierarchical Label Spaces||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Real-World Font Recognition Using Deep Network and Domain Adaptation||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Algorithmic Robustness for Learning via (ε,γ,τ)-Good Similarity Functions||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Score Function Features for Discriminative Learning ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Parallel training of DNNs with Natural Gradient and Parameter Averaging||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| A Generative Model for Deep Convolutional Learning ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Random Forests Can Hash||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Provable Methods for Training Neural Networks with Sparse Connectivity ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Deep learning with Elastic Averaging SGD ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Example Selection For Dictionary Learning ||align="center"| - ||align="center"| - | ||
| + | |- | ||
| + | |align="center"| ICLR 2015 ||align="center"| Unsupervised Domain Adaptation with Feature Embeddings ||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 | - | - |
