ICLR 2017

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

iclr2015:accepted-main [2017/10/08 15:29]
rnogueira
iclr2015:accepted-main [2017/10/08 15:40] (current)
rnogueira
Line 1: Line 1:
-==== Main Conference Oral Presentations ====+==== Main Conference ​Oral Presentations ====
   - [[http://​arxiv.org/​abs/​1412.6623|Word Representations via Gaussian Embedding]],​ Luke Vilnis and Andrew McCallum   - [[http://​arxiv.org/​abs/​1412.6623|Word Representations via Gaussian Embedding]],​ Luke Vilnis and Andrew McCallum
   - [[http://​arxiv.org/​abs/​1412.6632|Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)]], Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, and Alan Yuille   - [[http://​arxiv.org/​abs/​1412.6632|Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)]], Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, and Alan Yuille
Line 13: Line 13:
  
  
-==== Main Conference Poster ​Session ​====+====Main Conference ​Poster ​Presentations====
  
   - [[http://​arxiv.org/​abs/​1412.6550|FitNets:​ Hints for Thin Deep Nets]], Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio   - [[http://​arxiv.org/​abs/​1412.6550|FitNets:​ Hints for Thin Deep Nets]], Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio
Line 46: Line 46:
   - [[http://​arxiv.org/​abs/​1412.7489|A Unified Perspective on Multi-Domain and Multi-Task Learning]], Yongxin Yang and Timothy Hospedales   - [[http://​arxiv.org/​abs/​1412.7489|A Unified Perspective on Multi-Domain and Multi-Task Learning]], Yongxin Yang and Timothy Hospedales
   - [[http://​arxiv.org/​abs/​1412.6856|Object detectors emerge in Deep Scene CNNs]], Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba   - [[http://​arxiv.org/​abs/​1412.6856|Object detectors emerge in Deep Scene CNNs]], Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba
 +
 +
 +====Workshop Papers====
 +
   - [[http://​arxiv.org/​abs/​1412.7272 ​ | Learning Non-deterministic Representations with Energy-based Ensembles]],​ Maruan Al-Shedivat,​ Emre Neftci, and Gert Cauwenberghs   - [[http://​arxiv.org/​abs/​1412.7272 ​ | Learning Non-deterministic Representations with Energy-based Ensembles]],​ Maruan Al-Shedivat,​ Emre Neftci, and Gert Cauwenberghs
   - [[http://​arxiv.org/​abs/​1412.7063 ​ | Diverse Embedding Neural Network Language Models]], Kartik Audhkhasi, Abhinav Sethy, and Bhuvana Ramabhadran   - [[http://​arxiv.org/​abs/​1412.7063 ​ | Diverse Embedding Neural Network Language Models]], Kartik Audhkhasi, Abhinav Sethy, and Bhuvana Ramabhadran
Line 67: Line 71:
   - [[http://​arxiv.org/​abs/​1410.8516 ​ | NICE: Non-linear Independent Components Estimation]],​ Laurent Dinh, David Krueger, and Yoshua Bengio   - [[http://​arxiv.org/​abs/​1410.8516 ​ | NICE: Non-linear Independent Components Estimation]],​ Laurent Dinh, David Krueger, and Yoshua Bengio
   - [[http://​arxiv.org/​abs/​1412.6583 ​ | Discovering Hidden Factors of Variation in Deep Networks]], Brian Cheung, Jesse Livezey, Arjun Bansal, and Bruno Olshausen   - [[http://​arxiv.org/​abs/​1412.6583 ​ | Discovering Hidden Factors of Variation in Deep Networks]], Brian Cheung, Jesse Livezey, Arjun Bansal, and Bruno Olshausen
-  - [[http://​arxiv.org/​abs/​1412.7004 ​ | Tailoring Word Embeddings for Bilexical Predictions:​ An Experimental Comparison]],​ Pranava Swaroop Madhyastha, Xavier Carreras, and Ariadna Quattoni ​                          | +  - [[http://​arxiv.org/​abs/​1412.7004 ​ | Tailoring Word Embeddings for Bilexical Predictions:​ An Experimental Comparison]],​ Pranava Swaroop Madhyastha, Xavier Carreras, and Ariadna Quattoni 
-| 30 | [[http://​arxiv.org/​abs/​1412.6881 ​ | On Learning Vector Representations in Hierarchical Label Spaces]], Jinseok Nam and Johannes Fürnkranz ​                                                                         | +  ​- ​[[http://​arxiv.org/​abs/​1412.6881 ​ | On Learning Vector Representations in Hierarchical Label Spaces]], Jinseok Nam and Johannes Fürnkranz 
-| 31 | [[http://​arxiv.org/​abs/​1412.6614 ​ | In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning]], Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro ​                           | +  ​- ​[[http://​arxiv.org/​abs/​1412.6614 ​ | In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning]], Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro 
-| 33 | [[http://​arxiv.org/​abs/​1412.6452 ​ | Algorithmic Robustness for Semi-Supervised (ϵ, γ, τ)-Good Metric Learning]], Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, and Massih-Reza Amini            | +  ​- ​[[http://​arxiv.org/​abs/​1412.6452 ​ | Algorithmic Robustness for Semi-Supervised (ϵ, γ, τ)-Good Metric Learning]], Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, and Massih-Reza Amini 
-| 35 | [[http://​arxiv.org/​abs/​1504.00028 | Real-World Font Recognition Using Deep Network and Domain Adaptation]],​ Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jon Brandt, and Thomas Huang | +  ​- ​[[http://​arxiv.org/​abs/​1504.00028 | Real-World Font Recognition Using Deep Network and Domain Adaptation]],​ Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jon Brandt, and Thomas Huang 
-| 36 | [[http://​arxiv.org/​abs/​1412.6514 ​ | Score Function Features for Discriminative Learning]], Majid Janzamin, Hanie Sedghi, and Anima Anandkumar ​                                                                     | +  ​- ​[[http://​arxiv.org/​abs/​1412.6514 ​ | Score Function Features for Discriminative Learning]], Majid Janzamin, Hanie Sedghi, and Anima Anandkumar 
-| 38 | [[http://​arxiv.org/​abs/​1410.7455 ​ | Parallel training of DNNs with Natural Gradient and Parameter Averaging]],​ Daniel Povey, Xioahui Zhang, and Sanjeev Khudanpur ​                                                 | +  ​- ​[[http://​arxiv.org/​abs/​1410.7455 ​ | Parallel training of DNNs with Natural Gradient and Parameter Averaging]],​ Daniel Povey, Xioahui Zhang, and Sanjeev Khudanpur 
-| 40 | [[http://​arxiv.org/​abs/​1504.04054 | A Generative Model for Deep Convolutional Learning]], Yunchen Pu, Xin Yuan, and Lawrence Carin                                                                                 | +  ​- ​[[http://​arxiv.org/​abs/​1504.04054 | A Generative Model for Deep Convolutional Learning]], Yunchen Pu, Xin Yuan, and Lawrence Carin 
-| 41 | [[http://​arxiv.org/​abs/​1412.5083 ​ | Random Forests Can Hash]], Qiang Qiu, Guillermo Sapiro, and Alex Bronstein ​                                                                                                    | +  ​- ​[[http://​arxiv.org/​abs/​1412.5083 ​ | Random Forests Can Hash]], Qiang Qiu, Guillermo Sapiro, and Alex Bronstein 
-| 42 | [[http://​arxiv.org/​abs/​1412.2693 ​ | Provable Methods for Training Neural Networks with Sparse Connectivity]],​ Hanie Sedghi, and Anima Anandkumar ​                                                                  | +  ​- ​[[http://​arxiv.org/​abs/​1412.2693 ​ | Provable Methods for Training Neural Networks with Sparse Connectivity]],​ Hanie Sedghi, and Anima Anandkumar 
-| 43 | [[http://​arxiv.org/​abs/​1411.7676 ​ | Visual Scene Representations:​ sufficiency,​ minimality, invariance and approximation with deep convolutional networks]], Stefano Soatto and Alessandro Chiuso ​                  | +  ​- ​[[http://​arxiv.org/​abs/​1411.7676 ​ | Visual Scene Representations:​ sufficiency,​ minimality, invariance and approximation with deep convolutional networks]], Stefano Soatto and Alessandro Chiuso ​                  | 
-| 44 | [[http://​arxiv.org/​abs/​1412.6651 ​ | Deep learning with Elastic Averaging SGD]], Sixin Zhang, Anna Choromanska,​ and Yann LeCun                                                                                      | +  ​- ​[[http://​arxiv.org/​abs/​1412.6651 ​ | Deep learning with Elastic Averaging SGD]], Sixin Zhang, Anna Choromanska,​ and Yann LeCun 
-| 45 | [[http://​arxiv.org/​abs/​1412.6177 ​ | Example Selection For Dictionary Learning]], Tomoki Tsuchida and Garrison Cottrell ​                                                                                            | +  ​- ​[[http://​arxiv.org/​abs/​1412.6177 ​ | Example Selection For Dictionary Learning]], Tomoki Tsuchida and Garrison Cottrell 
-| 46 | [[http://​arxiv.org/​abs/​1412.6618 ​ | Permutohedral Lattice CNNs]], Martin Kiefel, Varun Jampani, and Peter Gehler ​                                                                                                  | +  ​- ​[[http://​arxiv.org/​abs/​1412.6618 ​ | Permutohedral Lattice CNNs]], Martin Kiefel, Varun Jampani, and Peter Gehler 
-- [[http://​arxiv.org/​abs/​1412.4385 ​ | Unsupervised Domain Adaptation with Feature Embeddings]],​ Yi Yang and Jacob Eisenstein +  - [[http://​arxiv.org/​abs/​1412.4385 ​ | Unsupervised Domain Adaptation with Feature Embeddings]],​ Yi Yang and Jacob Eisenstein 
-- [[http://​arxiv.org/​abs/​1412.6645 ​ | Weakly Supervised Multi-embeddings Learning of Acoustic Models]], Gabriel Synnaeve and Emmanuel Dupoux+  - [[http://​arxiv.org/​abs/​1412.6645 ​ | Weakly Supervised Multi-embeddings Learning of Acoustic Models]], Gabriel Synnaeve and Emmanuel Dupoux
  
-| 2 | [[http://​arxiv.org/​abs/​1412.6830 ​ | Learning Activation Functions to Improve Deep Neural Networks]], Forest Agostinelli,​ Matthew Hoffman, Peter Sadowski, and Pierre Baldi                                  | +  - [[http://​arxiv.org/​abs/​1412.6830 ​ | Learning Activation Functions to Improve Deep Neural Networks]], Forest Agostinelli,​ Matthew Hoffman, Peter Sadowski, and Pierre Baldi                                  | 
-| 3 | [[http://​arxiv.org/​abs/​1406.3407 ​ | Restricted Boltzmann Machine for Classification with Hierarchical Correlated Prior]], Gang Chen and Sargur Srihari ​                                                     | +  ​- ​[[http://​arxiv.org/​abs/​1406.3407 ​ | Restricted Boltzmann Machine for Classification with Hierarchical Correlated Prior]], Gang Chen and Sargur Srihari 
-| 4 | [[http://​arxiv.org/​abs/​1407.2538 ​ | Learning Deep Structured Models]], Liang-Chieh Chen, Alexander Schwing, Alan Yuille, and Raquel Urtasun ​                                                                | +  ​- ​[[http://​arxiv.org/​abs/​1407.2538 ​ | Learning Deep Structured Models]], Liang-Chieh Chen, Alexander Schwing, Alan Yuille, and Raquel Urtasun 
-| 5 | [[http://​arxiv.org/​abs/​1412.6277 ​ | N-gram-Based Low-Dimensional Representation for Document Classification]],​ Rémi Lebret and Ronan Collobert ​                                                             | +  ​- ​[[http://​arxiv.org/​abs/​1412.6277 ​ | N-gram-Based Low-Dimensional Representation for Document Classification]],​ Rémi Lebret and Ronan Collobert 
-| 6 | [[http://​arxiv.org/​abs/​1412.7024 ​ | Low precision arithmetic for deep learning]], Matthieu Courbariaux,​ Yoshua Bengio, and Jean-Pierre David                                                                | +  ​- ​[[http://​arxiv.org/​abs/​1412.7024 ​ | Low precision arithmetic for deep learning]], Matthieu Courbariaux,​ Yoshua Bengio, and Jean-Pierre David 
-| 7 | [[http://​arxiv.org/​abs/​1412.2302 ​ | Theano-based Large-Scale Visual Recognition with Multiple GPUs]], Weiguang Ding, Ruoyan Wang, Fei Mao, and Graham Taylor ​                                               | +  ​- ​[[http://​arxiv.org/​abs/​1412.2302 ​ | Theano-based Large-Scale Visual Recognition with Multiple GPUs]], Weiguang Ding, Ruoyan Wang, Fei Mao, and Graham Taylor 
-| 8 | [[http://​arxiv.org/​abs/​1412.6568 ​ | Improving zero-shot learning by mitigating the hubness problem]], Georgiana Dinu and Marco Baroni ​                                                                      | +  ​- ​[[http://​arxiv.org/​abs/​1412.6568 ​ | Improving zero-shot learning by mitigating the hubness problem]], Georgiana Dinu and Marco Baroni 
-| 9 | [[http://​arxiv.org/​abs/​1412.5836 ​ | Incorporating Both Distributional and Relational Semantics in Word Representations]],​ Daniel Fried and Kevin Duh                                                        | +  ​- ​[[http://​arxiv.org/​abs/​1412.5836 ​ | Incorporating Both Distributional and Relational Semantics in Word Representations]],​ Daniel Fried and Kevin Duh 
-| 10 | [[http://​arxiv.org/​abs/​1412.6581 ​ | Variational Recurrent Auto-Encoders]],​ Otto Fabius and Joost van Amersfoort ​                                                                                            | +  ​- ​[[http://​arxiv.org/​abs/​1412.6581 ​ | Variational Recurrent Auto-Encoders]],​ Otto Fabius and Joost van Amersfoort 
-| 11 | [[http://​arxiv.org/​abs/​1412.7155 ​ | Learning Compact Convolutional Neural Networks with Nested Dropout]], Chelsea Finn, Lisa Anne Hendricks, and Trevor Darrell ​                                            | +  ​- ​[[http://​arxiv.org/​abs/​1412.7155 ​ | Learning Compact Convolutional Neural Networks with Nested Dropout]], Chelsea Finn, Lisa Anne Hendricks, and Trevor Darrell 
-| 13 | [[http://​arxiv.org/​abs/​1412.3708 ​ | Compact Part-Based Image Representations:​ Extremal Competition and Overgeneralization]],​ Marc Goessling and Yali Amit                                                   | +  ​- ​[[http://​arxiv.org/​abs/​1412.3708 ​ | Compact Part-Based Image Representations:​ Extremal Competition and Overgeneralization]],​ Marc Goessling and Yali Amit 
-| 15 | [[http://​arxiv.org/​abs/​1504.02518 | Unsupervised Feature Learning from Temporal Data]], Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, and Yann LeCun                                            | +  ​- ​[[http://​arxiv.org/​abs/​1504.02518 | Unsupervised Feature Learning from Temporal Data]], Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, and Yann LeCun 
-| 16 | [[http://​arxiv.org/​abs/​1412.6567 ​ | Classifier with Hierarchical Topographical Maps as Internal Representation]],​ Pitoyo Hartono, Paul Hollensen, and Thomas Trappenberg ​                                   | +  ​- ​[[http://​arxiv.org/​abs/​1412.6567 ​ | Classifier with Hierarchical Topographical Maps as Internal Representation]],​ Pitoyo Hartono, Paul Hollensen, and Thomas Trappenberg 
-| 18 | [[http://​arxiv.org/​abs/​1412.5673 ​ | Entity-Augmented Distributional Semantics for Discourse Relations]],​ Yangfeng Ji and Jacob Eisenstein ​                                                                  | +  ​- ​[[http://​arxiv.org/​abs/​1412.5673 ​ | Entity-Augmented Distributional Semantics for Discourse Relations]],​ Yangfeng Ji and Jacob Eisenstein 
-| 20 | [[http://​arxiv.org/​abs/​1412.5474 ​ | Flattened Convolutional Neural Networks for Feedforward Acceleration]],​ Jonghoon Jin, Aysegul Dundar, and Eugenio Culurciello ​                                          | +  ​- ​[[http://​arxiv.org/​abs/​1412.5474 ​ | Flattened Convolutional Neural Networks for Feedforward Acceleration]],​ Jonghoon Jin, Aysegul Dundar, and Eugenio Culurciello 
-| 22 | [[http://​arxiv.org/​abs/​1504.02902 | Gradual Training Method for Denoising Auto Encoders]], Alexander Kalmanovich and Gal Chechik ​                                                                           | +  ​- ​[[http://​arxiv.org/​abs/​1504.02902 | Gradual Training Method for Denoising Auto Encoders]], Alexander Kalmanovich and Gal Chechik 
-| 23 | [[http://​arxiv.org/​abs/​1411.1045 ​ | Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet]], Matthias Kümmerer, Lucas Theis, and Matthias Bethge ​                                 | +  ​- ​[[http://​arxiv.org/​abs/​1411.1045 ​ | Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet]], Matthias Kümmerer, Lucas Theis, and Matthias Bethge 
-| 24 | [[http://​arxiv.org/​abs/​1412.7525 ​ | Difference Target Propagation]],​ Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, Antoine Biard, and Yoshua Bengio ​                                                         | +  ​- ​[[http://​arxiv.org/​abs/​1412.7525 ​ | Difference Target Propagation]],​ Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, Antoine Biard, and Yoshua Bengio 
-| 25 | [[http://​arxiv.org/​abs/​1411.3815 ​ | Predictive encoding of contextual relationships for perceptual inference, interpolation and prediction]],​ Mingmin Zhao, Chengxu Zhuang, Yizhou Wang, and Tai Sing Lee   | +  ​- ​[[http://​arxiv.org/​abs/​1411.3815 ​ | Predictive encoding of contextual relationships for perceptual inference, interpolation and prediction]],​ Mingmin Zhao, Chengxu Zhuang, Yizhou Wang, and Tai Sing Lee 
-| 27 | [[http://​arxiv.org/​abs/​1412.6249 ​ | Purine: A Bi-Graph based deep learning framework]],​ Min Lin, Shuo Li, Xuan Luo, and Shuicheng Yan                                                                       | +  ​- ​[[http://​arxiv.org/​abs/​1412.6249 ​ | Purine: A Bi-Graph based deep learning framework]],​ Min Lin, Shuo Li, Xuan Luo, and Shuicheng Yan 
-| 28 | [[http://​arxiv.org/​abs/​1504.01989 | Pixel-wise Deep Learning for Contour Detection]],​ Jyh-Jing Hwang and Tyng-Luh Liu                                                                                       | +  ​- ​[[http://​arxiv.org/​abs/​1504.01989 | Pixel-wise Deep Learning for Contour Detection]],​ Jyh-Jing Hwang and Tyng-Luh Liu 
-| 29 | [[http://​arxiv.org/​abs/​1412.5335 ​ | Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews]], Grégoire Mesnil, Tomas Mikolov, Marc'​Aurelio Ranzato, and Yoshua Bengio ​| +  ​- ​[[http://​arxiv.org/​abs/​1412.5335 ​ | Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews]], Grégoire Mesnil, Tomas Mikolov, Marc'​Aurelio Ranzato, and Yoshua Bengio 
-| 30 | [[http://​arxiv.org/​abs/​1503.08873 | Fast Label Embeddings for Extremely Large Output Spaces]], Paul Mineiro and Nikos Karampatziakis ​                                                                       | +  ​- ​[[http://​arxiv.org/​abs/​1503.08873 | Fast Label Embeddings for Extremely Large Output Spaces]], Paul Mineiro and Nikos Karampatziakis 
-| 31 | [[http://​arxiv.org/​abs/​1412.6597 ​ | An Analysis of Unsupervised Pre-training in Light of Recent Advances]], Tom Paine, Pooya Khorrami, Wei Han, and Thomas Huang                                            | +  ​- ​[[http://​arxiv.org/​abs/​1412.6597 ​ | An Analysis of Unsupervised Pre-training in Light of Recent Advances]], Tom Paine, Pooya Khorrami, Wei Han, and Thomas Huang 
-| 33 | [[http://​arxiv.org/​abs/​1412.7144 ​ | Fully Convolutional Multi-Class Multiple Instance Learning]], Deepak Pathak, Evan Shelhamer, Jonathan Long, and Trevor Darrell ​                                         | +  ​- ​[[http://​arxiv.org/​abs/​1412.7144 ​ | Fully Convolutional Multi-Class Multiple Instance Learning]], Deepak Pathak, Evan Shelhamer, Jonathan Long, and Trevor Darrell 
-| 35 | [[http://​arxiv.org/​abs/​1504.02485 | What Do Deep CNNs Learn About Objects?]], Xingchao Peng, Baochen Sun, Karim Ali, and Kate Saenko ​                                                                       | +  ​- ​[[http://​arxiv.org/​abs/​1504.02485 | What Do Deep CNNs Learn About Objects?]], Xingchao Peng, Baochen Sun, Karim Ali, and Kate Saenko 
-| 36 | [[http://​arxiv.org/​abs/​1412.6134 ​ | Representation using the Weyl Transform]],​ Qiang Qiu, Andrew Thompson, Robert Calderbank, and Guillermo Sapiro ​                                                         | +  ​- ​[[http://​arxiv.org/​abs/​1412.6134 ​ | Representation using the Weyl Transform]],​ Qiang Qiu, Andrew Thompson, Robert Calderbank, and Guillermo Sapiro 
-| 38 | [[http://​arxiv.org/​abs/​1412.7210 ​ | Denoising autoencoder with modulated lateral connections learns invariant representations of natural images]], Antti Rasmus, Harri Valpola, and Tapani Raiko            | +  -[[http://​arxiv.org/​abs/​1412.7210 ​ | Denoising autoencoder with modulated lateral connections learns invariant representations of natural images]], Antti Rasmus, Harri Valpola, and Tapani Raiko 
-| 40 | [[http://​arxiv.org/​abs/​1412.5068 ​ | Towards Deep Neural Network Architectures Robust to Adversarial Examples]], Shixiang Gu and Luca Rigazio ​                                                               | +  ​- ​[[http://​arxiv.org/​abs/​1412.5068 ​ | Towards Deep Neural Network Architectures Robust to Adversarial Examples]], Shixiang Gu and Luca Rigazio 
-| 41 | [[http://​arxiv.org/​abs/​1412.6615 ​ | Explorations on high dimensional landscapes]],​ Levent Sagun, Ugur Guney, and Yann LeCun                                                                                 | +  ​- ​[[http://​arxiv.org/​abs/​1412.6615 ​ | Explorations on high dimensional landscapes]],​ Levent Sagun, Ugur Guney, and Yann LeCun 
-| 42 | [[http://​arxiv.org/​abs/​1412.7009 ​ | Generative Class-conditional Autoencoders]],​ Jan Rudy and Graham Taylor ​                                                                                                | +  ​- ​[[http://​arxiv.org/​abs/​1412.7009 ​ | Generative Class-conditional Autoencoders]],​ Jan Rudy and Graham Taylor 
-| 43 | [[http://​arxiv.org/​abs/​1412.7054 ​ | Attention for Fine-Grained Categorization]],​ Pierre Sermanet, Andrea Frome, and Esteban Real                                                                            | +  ​- ​[[http://​arxiv.org/​abs/​1412.7054 ​ | Attention for Fine-Grained Categorization]],​ Pierre Sermanet, Andrea Frome, and Esteban Real 
-| 44 | [[http://​arxiv.org/​abs/​1412.6574 ​ | A Baseline for Visual Instance Retrieval with Deep Convolutional Networks]], Ali Sharif Razavian, Josephine Sullivan, Atsuto Maki, and Stefan Carlsson ​                 | +  ​- ​[[http://​arxiv.org/​abs/​1412.6574 ​ | A Baseline for Visual Instance Retrieval with Deep Convolutional Networks]], Ali Sharif Razavian, Josephine Sullivan, Atsuto Maki, and Stefan Carlsson 
-| 45 | [[http://​arxiv.org/​abs/​1412.6607 ​ | Visual Scene Representation:​ Scaling and Occlusion]],​ Stefano Soatto, Jingming Dong, and Nikolaos Karianakis ​                                                           | +  ​-  ​[[http://​arxiv.org/​abs/​1412.6607 ​ | Visual Scene Representation:​ Scaling and Occlusion]],​ Stefano Soatto, Jingming Dong, and Nikolaos Karianakis 
-| 46 | [[http://​arxiv.org/​abs/​1412.7479 ​ | Deep networks with large output spaces]], Sudheendra Vijayanarasimhan,​ Jon Shlens, Jay Yagnik, and Rajat Monga                                                          | +  ​- ​[[http://​arxiv.org/​abs/​1412.7479 ​ | Deep networks with large output spaces]], Sudheendra Vijayanarasimhan,​ Jon Shlens, Jay Yagnik, and Rajat Monga 
-| 47 | [[http://​arxiv.org/​abs/​1412.7091 ​ | Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets]], Pascal Vincent ​                                                            | +  ​- ​[[http://​arxiv.org/​abs/​1412.7091 ​ | Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets]], Pascal Vincent 
-| 49 | [[http://​arxiv.org/​abs/​1412.6563 ​ | Self-informed neural network structure learning]], David Warde-Farley,​ Andrew Rabinovich, and Dragomir Anguelov ​                                                        |+  ​- ​[[http://​arxiv.org/​abs/​1412.6563 ​ | Self-informed neural network structure learning]], David Warde-Farley,​ Andrew Rabinovich, and Dragomir Anguelov