Fine-grained Video Classification and Captioning. | 1 | 0.35 | 2018 |
Evaluating visual "common sense" using fine-grained classification and captioning tasks. | 0 | 0.34 | 2018 |
Generating images with recurrent adversarial networks. | 36 | 3.49 | 2016 |
Regularizing RNNs by Stabilizing Activations | 0 | 0.34 | 2016 |
Incorporating long-range consistency in CNN-based texture generation. | 1 | 0.35 | 2016 |
Architectural Complexity Measures of Recurrent Neural Networks. | 21 | 0.99 | 2016 |
Conservativeness of Untied Auto-Encoders. | 1 | 0.35 | 2016 |
Deep Learning Vector Quantization. | 0 | 0.34 | 2016 |
EmoNets: Multimodal deep learning approaches for emotion recognition in video. | 65 | 1.78 | 2016 |
Neural Networks with Few Multiplications | 0 | 0.34 | 2015 |
On Using Very Large Target Vocabulary For Neural Machine Translation | 223 | 10.44 | 2015 |
The Potential Energy of an Autoencoder | 14 | 0.66 | 2015 |
Recurrent Neural Networks for Emotion Recognition in Video. | 61 | 1.80 | 2015 |
RATM: Recurrent Attentive Tracking Model | 15 | 0.59 | 2015 |
Zero-bias autoencoders and the benefits of co-adapting features | 0 | 0.34 | 2015 |
Montreal Neural Machine Translation Systems for WMT'15. | 0 | 0.34 | 2015 |
Deep learning: Architectures, algorithms, applications | 0 | 0.34 | 2015 |
Real-time activity recognition via deep learning of motion features. | 0 | 0.34 | 2015 |
Montreal Neural Machine Translation Systems for WMT'15. | 26 | 1.19 | 2015 |
Regularizing RNNs by Stabilizing Activations | 1 | 0.35 | 2015 |
Learning Visual Odometry with a Convolutional Network. | 21 | 0.79 | 2015 |
Dropout as data augmentation. | 12 | 0.71 | 2015 |
How far can we go without convolution: Improving fully-connected networks. | 3 | 0.46 | 2015 |
Denoising Criterion for Variational Auto-Encoding Framework | 9 | 0.78 | 2015 |
Modeling sequential data using higher-order relational features and predictive training. | 5 | 0.46 | 2014 |
A unified approach to learning depth and motion features | 0 | 0.34 | 2014 |
The role of spatio-temporal synchrony in the encoding of motion | 0 | 0.34 | 2014 |
Zero-bias autoencoders and the benefits of co-adapting features. | 0 | 0.34 | 2014 |
Modeling Deep Temporal Dependencies with Recurrent Grammar Cells"". | 33 | 2.06 | 2014 |
Combining modality specific deep neural networks for emotion recognition in video | 103 | 3.02 | 2013 |
The role of spatio-temporal synchrony in the encoding of motion. | 0 | 0.34 | 2013 |
Feature grouping from spatially constrained multiplicative interaction | 0 | 0.34 | 2013 |
Learning to relate images. | 44 | 2.28 | 2013 |
Feature grouping from spatially constrained multiplicative interaction | 1 | 0.35 | 2013 |
Learning invariant features by harnessing the aperture problem. | 3 | 0.50 | 2013 |
On autoencoder scoring. | 4 | 0.42 | 2013 |
Unsupervised learning of depth and motion. | 13 | 1.40 | 2013 |
Shared Kernel Information Embedding for Discriminative Inference | 18 | 0.83 | 2012 |
On multi-view feature learning | 8 | 0.62 | 2012 |
Learning to relate images: Mapping units, complex cells and simultaneous eigenspaces | 2 | 0.43 | 2011 |
Gradient-based learning of higher-order image features | 31 | 1.37 | 2011 |
Gated Softmax Classification. | 21 | 0.98 | 2010 |
Learning to represent spatial transformations with factored higher-order Boltzmann machines. | 110 | 7.13 | 2010 |
Shared Kernel Information Embedding For Discriminative Inference | 24 | 1.13 | 2009 |
Unsupervised Learning of Image Transformations | 85 | 6.77 | 2007 |
Learning to solve QBF | 27 | 1.21 | 2007 |
Kernel information embeddings | 11 | 1.20 | 2006 |
Principal surfaces from unsupervised kernel regression. | 41 | 2.39 | 2005 |
Improving dimensionality reduction with spectral gradient descent. | 5 | 0.57 | 2005 |
Multiple Relational Embedding | 17 | 1.30 | 2004 |