| A Unified Perspective on Multi-Domain and Multi-Task Learning |
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| Adam: A Method for Stochastic Optimization |
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| Automatic Discovery and Optimization of Parts for Image Classification |
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| Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) |
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| Deep Narrow Boltzmann Machines are Universal Approximators |
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| Deep Structured Output Learning for Unconstrained Text Recognition |
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| Embedding Entities and Relations for Learning and Inference in Knowledge Bases |
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| Explaining and Harnessing Adversarial Examples |
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| Fast Convolutional Nets With fbfft: A GPU Performance Evaluation |
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| FitNets: Hints for Thin Deep Nets |
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| Generative Modeling of Convolutional Neural Networks |
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5 |
| Joint RNN-Based Greedy Parsing and Word Composition |
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4 |
| Leveraging Monolingual Data for Crosslingual Compositional Word Representations |
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3 |
| Memory Networks |
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3 |
| Modeling Compositionality with Multiplicative Recurrent Neural Networks |
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3 |
| Move Evaluation in Go Using Deep Convolutional Neural Networks |
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3 |
| Multiple Object Recognition with Visual Attention |
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3 |
| Neural Machine Translation by Jointly Learning to Align and Translate |
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5 |
| Object detectors emerge in Deep Scene CNNs |
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| Qualitatively characterizing neural network optimization problems |
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3 |
| Reweighted Wake-Sleep |
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5 |
| Scheduled denoising autoencoders |
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4 |
| Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs |
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| Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition |
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| Techniques for Learning Binary Stochastic Feedforward Neural Networks |
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| The local low-dimensionality of natural images |
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| Transformation Properties of Learned Visual Representations |
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2 |
| Understanding Locally Competitive Networks |
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| Very Deep Convolutional Networks for Large-Scale Image Recognition |
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| Word Representations via Gaussian Embedding |
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| Zero-bias autoencoders and the benefits of co-adapting features |
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4 |