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Deep Learning

66,648 papers (25 classified directly here)

Subtopics

Learning Types (28,663) Architectures (27,448) Models (16,617) Techniques (8,241) Optimization & Theory (6,829) Application Areas (46)

Papers per year

2
65
428
6150
10854
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Papers (including subtopics)

QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings SEMEVAL 2017
ResSim at SemEval-2017 Task 1: Multilingual Word Representations for Semantic Textual Similarity SEMEVAL 2017
SEF@UHH at SemEval-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector SEMEVAL 2017
UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity SEMEVAL 2017
MITRE at SemEval-2017 Task 1: Simple Semantic Similarity SEMEVAL 2017
SCIR-QA at SemEval-2017 Task 3: CNN Model Based on Similar and Dissimilar Information between Keywords for Question Similarity SEMEVAL 2017
NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings SEMEVAL 2017
HLP@UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors SEMEVAL 2017
NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture SEMEVAL 2017
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing SEMEVAL 2017
TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers SEMEVAL 2017
Tagger: Deep Unsupervised Perceptual Grouping NIPS 2016
Conditional Generative Moment-Matching Networks NIPS 2016
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks NIPS 2016
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks NIPS 2016
Generating Videos with Scene Dynamics NIPS 2016
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering NIPS 2016
Exponential Family Embeddings NIPS 2016
Integrated perception with recurrent multi-task neural networks NIPS 2016
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks NIPS 2016
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks NIPS 2016
Convolutional Neural Fabrics NIPS 2016
Variational Bayes on Monte Carlo Steroids NIPS 2016
Maximal Sparsity with Deep Networks? NIPS 2016
Single-Image Depth Perception in the Wild NIPS 2016