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ICLR 2015: San Diego, CA, USA
- Yoshua Bengio, Yann LeCun:
3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings. 2015
Oral Presentations
- Luke Vilnis, Andrew McCallum:
Word Representations via Gaussian Embedding. - Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Alan L. Yuille:
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN). - Max Jaderberg, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman:
Deep Structured Output Learning for Unconstrained Text Recognition. - Karen Simonyan, Andrew Zisserman:
Very Deep Convolutional Networks for Large-Scale Image Recognition. - Nicolas Vasilache, Jeff Johnson, Michaël Mathieu, Soumith Chintala, Serkan Piantino, Yann LeCun:
Fast Convolutional Nets With fbfft: A GPU Performance Evaluation. - Jörg Bornschein, Yoshua Bengio:
Reweighted Wake-Sleep. - Olivier J. Hénaff, Johannes Ballé, Neil C. Rabinowitz, Eero P. Simoncelli:
The local low-dimensionality of natural images. - Jason Weston, Sumit Chopra, Antoine Bordes:
Memory Networks. - Bolei Zhou, Aditya Khosla, Àgata Lapedriza, Aude Oliva, Antonio Torralba:
Object Detectors Emerge in Deep Scene CNNs. - Ian J. Goodfellow, Oriol Vinyals:
Qualitatively characterizing neural network optimization problems. - Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio:
Neural Machine Translation by Jointly Learning to Align and Translate.
Poster Presentations
- Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, Yoshua Bengio:
FitNets: Hints for Thin Deep Nets. - Tapani Raiko, Mathias Berglund, Guillaume Alain, Laurent Dinh:
Techniques for Learning Binary Stochastic Feedforward Neural Networks. - Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille:
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. - Jimmy Ba, Volodymyr Mnih, Koray Kavukcuoglu:
Multiple Object Recognition with Visual Attention. - Guido Montúfar:
Deep Narrow Boltzmann Machines are Universal Approximators. - Taco S. Cohen, Max Welling:
Transformation Properties of Learned Visual Representations. - Joël Legrand, Ronan Collobert:
Joint RNN-Based Greedy Parsing and Word Composition. - Diederik P. Kingma, Jimmy Ba:
Adam: A Method for Stochastic Optimization. - Krzysztof J. Geras, Charles Sutton:
Scheduled denoising autoencoders. - Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng:
Embedding Entities and Relations for Learning and Inference in Knowledge Bases. - Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy:
Explaining and Harnessing Adversarial Examples. - Ozan Irsoy, Claire Cardie:
Modeling Compositionality with Multiplicative Recurrent Neural Networks. - Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba, Ivan V. Oseledets, Victor S. Lempitsky:
Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition. - Roland Memisevic, Kishore Reddy Konda, David Krueger:
Zero-bias autoencoders and the benefits of co-adapting features. - Sobhan Naderi Parizi, Andrea Vedaldi, Andrew Zisserman, Pedro F. Felzenszwalb:
Automatic Discovery and Optimization of Parts for Image Classification. - Rupesh Kumar Srivastava, Jonathan Masci, Faustino J. Gomez, Jürgen Schmidhuber:
Understanding Locally Competitive Networks. - Hubert Soyer, Pontus Stenetorp, Akiko Aizawa:
Leveraging Monolingual Data for Crosslingual Compositional Word Representations. - Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver:
Move Evaluation in Go Using Deep Convolutional Neural Networks. - Jifeng Dai, Ying Nian Wu:
Generative Modeling of Convolutional Neural Networks. - Yongxin Yang, Timothy M. Hospedales:
A Unified Perspective on Multi-Domain and Multi-Task Learning.
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