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ORCIDICLR Workshop 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, Workshop Track Proceedings. 2015 - Maruan Al-Shedivat, Emre Neftci, Gert Cauwenberghs:
Learning Non-deterministic Representations with Energy-based Ensembles. - Kartik Audhkhasi, Abhinav Sethy, Bhuvana Ramabhadran:
Diverse Embedding Neural Network Language Models. - Kevin Bache, Dennis DeCoste, Padhraic Smyth:
Hot Swapping for Online Adaptation of Optimization Hyperparameters. - Gabriella Contardo, Ludovic Denoyer, Thierry Artières:
Representation Learning for cold-start recommendation. - Sainbayar Sukhbaatar, Rob Fergus:
Learning from Noisy Labels with Deep Neural Networks. - Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, Martin A. Riedmiller:
Striving for Simplicity: The All Convolutional Net. - Dimitri Palaz, Mathew Magimai-Doss, Ronan Collobert:
Learning linearly separable features for speech recognition using convolutional neural networks. - Scott E. Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, Andrew Rabinovich:
Training Deep Neural Networks on Noisy Labels with Bootstrapping. - Raja Giryes, Guillermo Sapiro, Alexander M. Bronstein:
On the Stability of Deep Networks. - Pablo Sprechmann, Joan Bruna, Yann LeCun:
Audio Source Separation with Discriminative Scattering Networks. - Rémi Lebret, Pedro H. O. Pinheiro, Ronan Collobert:
Simple Image Description Generator via a Linear Phrase-Based Approach. - (Withdrawn) Stochastic Descent Analysis of Representation Learning Algorithms.
- Ian J. Goodfellow:
On distinguishability criteria for estimating generative models. - Felix Hill, Kyunghyun Cho, Sébastien Jean, Coline Devin, Yoshua Bengio:
Embedding Word Similarity with Neural Machine Translation. - Elad Hoffer, Nir Ailon:
Deep metric learning using Triplet network. - Daniel Jiwoong Im, Ethan Buchman, Graham W. Taylor:
Understanding Minimum Probability Flow for RBMs Under Various Kinds of Dynamics. - Arnab Paul, Suresh Venkatasubramanian:
A Group Theoretic Perspective on Unsupervised Deep Learning. - Tomás Mikolov, Armand Joulin, Sumit Chopra, Michaël Mathieu, Marc'Aurelio Ranzato:
Learning Longer Memory in Recurrent Neural Networks. - Ivan Titov, Ehsan Khoddam:
Inducing Semantic Representation from Text by Jointly Predicting and Factorizing Relations. - Laurent Dinh, David Krueger, Yoshua Bengio:
NICE: Non-linear Independent Components Estimation. - Brian Cheung, Jesse A. Livezey, Arjun K. Bansal, Bruno A. Olshausen:
Discovering Hidden Factors of Variation in Deep Networks. - Pranava Swaroop Madhyastha, Xavier Carreras, Ariadna Quattoni:
Tailoring Word Embeddings for Bilexical Predictions: An Experimental Comparison. - Jinseok Nam, Johannes Fürnkranz:
On the Importance of a Hierarchy for Learning Continuous Vector Representations of a Label Space. - Behnam Neyshabur, Ryota Tomioka, Nathan Srebro:
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning. - Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, Massih-Reza Amini:
Algorithmic Robustness for Semi-Supervised (ε, γ, τ)-Good Metric Learning. - Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jonathan Brandt, Thomas S. Huang:
Real-World Font Recognition Using Deep Network and Domain Adaptation. - Majid Janzamin, Hanie Sedghi, Anima Anandkumar:
Score Function Features for Discriminative Learning. - Daniel Povey, Xiaohui Zhang, Sanjeev Khudanpur:
Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging. - Yunchen Pu, Xin Yuan, Lawrence Carin:
A Generative Model for Deep Convolutional Learning. - Qiang Qiu, Guillermo Sapiro, Alexander M. Bronstein:
Random Forests Can Hash. - Hanie Sedghi, Anima Anandkumar:
Provable Methods for Training Neural Networks with Sparse Connectivity. - Stefano Soatto, Alessandro Chiuso:
Visual Scene Representations: Sufficiency, Minimality, Invariance and Deep Approximations. - Sixin Zhang, Anna Choromanska, Yann LeCun:
Deep learning with Elastic Averaging SGD. - Tomoki Tsuchida, Garrison W. Cottrell:
Example Selection For Dictionary Learning. - Martin Kiefel, Varun Jampani, Peter V. Gehler:
Permutohedral Lattice CNNs. - Yi Yang, Jacob Eisenstein:
Unsupervised Domain Adaptation with Feature Embeddings. - Gabriel Synnaeve, Emmanuel Dupoux:
Weakly Supervised Multi-Embeddings Learning of Acoustic Models. - Forest Agostinelli
, Matthew D. Hoffman, Peter J. Sadowski, Pierre Baldi:
Learning Activation Functions to Improve Deep Neural Networks. - Gang Chen, Sargur N. Srihari:
Restricted Boltzmann Machine for Classification with Hierarchical Correlated Prior. - Liang-Chieh Chen, Alexander G. Schwing, Alan L. Yuille, Raquel Urtasun:
Learning Deep Structured Models. - Rémi Lebret, Ronan Collobert:
N-gram-Based Low-Dimensional Representation for Document Classification. - Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David:
Low precision arithmetic for deep learning. - Weiguang Ding, Ruoyan Wang, Fei Mao, Graham W. Taylor:
Theano-based Large-Scale Visual Recognition with Multiple GPUs. - Georgiana Dinu, Marco Baroni:
Improving zero-shot learning by mitigating the hubness problem. - Daniel Fried, Kevin Duh:
Incorporating Both Distributional and Relational Semantics in Word Representations. - Otto Fabius, Joost R. van Amersfoort, Diederik P. Kingma:
Variational Recurrent Auto-Encoders. - Chelsea Finn, Lisa Anne Hendricks, Trevor Darrell:
Learning Compact Convolutional Neural Networks with Nested Dropout. - Marc Goessling, Yali Amit:
Compact Part-Based Image Representations: Extremal Competition and Overgeneralization. - Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun:
Unsupervised Feature Learning from Temporal Data. - Pitoyo Hartono, Paul Hollensen, Thomas Trappenberg:
Classifier with Hierarchical Topographical Maps as Internal Representation. - Yangfeng Ji, Jacob Eisenstein:
Entity-Augmented Distributional Semantics for Discourse Relations. - Jonghoon Jin, Aysegul Dundar, Eugenio Culurciello:
Flattened Convolutional Neural Networks for Feedforward Acceleration. - Alexander Kalmanovich, Gal Chechik:
Gradual Training Method for Denoising Auto Encoders. - Matthias Kümmerer, Lucas Theis, Matthias Bethge:
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet. - Dong-Hyun Lee, Saizheng Zhang, Antoine Biard, Yoshua Bengio:
Target Propagation. - Mingmin Zhao, Chengxu Zhuang, Yizhou Wang, Tai Sing Lee:
Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction. - Min Lin, Shuo Li, Xuan Luo, Shuicheng Yan:
Purine: A bi-graph based deep learning framework. - Jyh-Jing Hwang, Tyng-Luh Liu:
Pixel-wise Deep Learning for Contour Detection. - Grégoire Mesnil, Tomás Mikolov, Marc'Aurelio Ranzato, Yoshua Bengio:
Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews. - Paul Mineiro, Nikos Karampatziakis:
Fast Label Embeddings for Extremely Large Output Spaces. - Tom Le Paine, Pooya Khorrami, Wei Han, Thomas S. Huang:
An Analysis of Unsupervised Pre-training in Light of Recent Advances. - Deepak Pathak, Evan Shelhamer, Jonathan Long, Trevor Darrell:
Fully Convolutional Multi-Class Multiple Instance Learning. - Xingchao Peng, Baochen Sun, Karim Ali, Kate Saenko:
What Do Deep CNNs Learn About Objects? - Qiang Qiu, Andrew Thompson, A. Robert Calderbank, Guillermo Sapiro:
Representation using the Weyl Transform. - Antti Rasmus, Tapani Raiko, Harri Valpola:
Denoising autoencoder with modulated lateral connections learns invariant representations of natural images. - Shixiang Gu, Luca Rigazio:
Towards Deep Neural Network Architectures Robust to Adversarial Examples. - Levent Sagun, V. Ugur Güney, Yann LeCun:
Explorations on high dimensional landscapes. - Jan Rudy, Graham W. Taylor:
Generative Class-conditional Autoencoders. - Pierre Sermanet, Andrea Frome, Esteban Real:
Attention for Fine-Grained Categorization. - Ali Sharif Razavian, Josephine Sullivan, Atsuto Maki, Stefan Carlsson:
Visual Instance Retrieval with Deep Convolutional Networks. - Stefano Soatto, Jingming Dong, Nikolaos Karianakis:
Visual Scene Representations: Scaling and Occlusion in Convolutional Architectures. - Sudheendra Vijayanarasimhan, Jonathon Shlens, Rajat Monga, Jay Yagnik:
Deep Networks With Large Output Spaces. - Pascal Vincent:
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets. - David Warde-Farley, Andrew Rabinovich, Dragomir Anguelov:
Self-informed neural network structure learning.
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last updated on 2024-11-21 20:36 CET by the dblp team
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