Pix2seq: A Language Modeling Framework for Object Detection | 0 | 0.34 | 2022 |
Unsupervised Part Representation by Flow Capsules | 0 | 0.34 | 2021 |
Teaching with Commentaries | 0 | 0.34 | 2021 |
Deep learning for AI | 5 | 0.45 | 2021 |
Teaching with Commentaries. | 0 | 0.34 | 2021 |
CvxNet: Learnable Convex Decomposition | 7 | 0.42 | 2020 |
Imputer: Sequence Modelling via Imputation and Dynamic Programming | 0 | 0.34 | 2020 |
Big Self-Supervised Models are Strong Semi-Supervised Learners | 0 | 0.34 | 2020 |
A Simple Framework for Contrastive Learning of Visual Representations | 2 | 0.36 | 2020 |
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. | 0 | 0.34 | 2020 |
The Next Generation of Neural Networks | 0 | 0.34 | 2020 |
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss. | 1 | 0.35 | 2019 |
Similarity of Neural Network Representations Revisited. | 2 | 0.36 | 2019 |
When Does Label Smoothing Help? | 7 | 0.40 | 2019 |
Learning Sparse Networks Using Targeted Dropout. | 1 | 0.36 | 2019 |
Cerberus: A Multi-headed Derenderer. | 0 | 0.34 | 2019 |
Stacked Capsule Autoencoders. | 0 | 0.34 | 2019 |
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures | 0 | 0.34 | 2018 |
Who Said What: Modeling Individual Labelers Improves Classification | 12 | 0.65 | 2018 |
Large scale distributed neural network training through online distillation. | 0 | 0.34 | 2018 |
Illustrative Language Understanding: Large-Scale Visual Grounding With Image Search | 0 | 0.34 | 2018 |
Large scale distributed neural network training through online distillation. | 18 | 0.68 | 2018 |
Matrix capsules with EM routing | 47 | 1.52 | 2018 |
DARCCC: Detecting Adversaries by Reconstruction from Class Conditional Capsules. | 5 | 0.42 | 2018 |
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. | 95 | 3.02 | 2017 |
Distilling a Neural Network Into a Soft Decision Tree. | 18 | 0.64 | 2017 |
Regularizing Neural Networks by Penalizing Confident Output Distributions. | 42 | 1.24 | 2017 |
Regularizing Neural Networks by Penalizing Confident Output Distributions. | 0 | 0.34 | 2017 |
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. | 0 | 0.34 | 2017 |
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models | 0 | 0.34 | 2016 |
Layer Normalization. | 0 | 0.34 | 2016 |
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units. | 94 | 3.55 | 2015 |
Distilling the Knowledge in a Neural Network. | 696 | 21.80 | 2015 |
Guest Editorial: Deep Learning | 3 | 0.38 | 2015 |
Application of Deep Belief Networks for Natural Language Understanding | 83 | 2.32 | 2014 |
Grammar as a Foreign Language. | 237 | 10.73 | 2014 |
Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models. | 0 | 0.34 | 2014 |
Dropout: a simple way to prevent neural networks from overfitting | 3420 | 130.13 | 2014 |
Where do features come from? | 6 | 0.42 | 2014 |
Tensor Analyzers. | 0 | 0.34 | 2013 |
Modeling Documents with Deep Boltzmann Machines. | 64 | 3.62 | 2013 |
Discovering Multiple Constraints that are Frequently Approximately Satisfied | 12 | 4.73 | 2013 |
New types of deep neural network learning for speech recognition and related applications: an overview | 181 | 8.00 | 2013 |
Modeling Natural Images Using Gated MRFs | 25 | 1.65 | 2013 |
Improving deep neural networks for LVCSR using rectified linear units and dropout. | 83 | 5.62 | 2013 |
Speech recognition with deep recurrent neural networks | 454 | 23.81 | 2013 |
Efficient parametric projection pursuit density estimation | 1 | 1.32 | 2012 |
Deep Lambertian Networks. | 1 | 0.35 | 2012 |
Conditional Restricted Boltzmann Machines for Structured Output Prediction | 36 | 2.24 | 2012 |
Visualizing non-metric similarities in multiple maps | 28 | 1.18 | 2012 |