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Richard Socher
http://cs224d.stanford.edu/
CS224d: Deep Learning for Natural Language Processing

April 23, 2015 Mountain View

• M. Iyyer, J. Boyd-Graber, L. Claudino, R. Socher, J. Daume. A Neural Network for Factoid Question Answering over Paragraphs

• R. Socher, J. Bauer, C.D. Manning, A.Y. Ng. Parsing with Compositional Vector Grammars

same as

April 15 2015 San Francisco

http://www.meetup.com/Silicon-Valley-Data-Science-Journal-Club/events/221698151/

The papers for the meeting is:

• M. Iyyer, J. Boyd-Graber, L. Claudino, R. Socher, J. Daume. A Neural Network for Factoid Question Answering over Paragraphs

• R. Socher, J. Bauer, C.D. Manning, A.Y. Ng. Parsing with Compositional Vector Grammars

March 3, 2015

• D. Chen, R. Socher, C.D. Manning, A.Y. Ng. Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors 

• R. Socher, B. Huval, C.D. Manning, A.Y. Ng. Semantic Compositionality through Recursive Matrix-Vector Spaces 

Past Papers

http://www.meetup.com/Silicon-Valley-Data-Science-Journal-Club/messages/boards/thread/40111312

1. Large scale image annotation: learning to rank with joint
word-image embeddings
Jason Weston ·Samy Bengio ·Nicolas Usunier

http://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/36573.pdf

2. DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf

Conference on Computer Vision and Pattern Recognition (CVPR)

https://www.facebook.com/publications/546316888800776/

3. Lior Wolf, Tel Aviv University
Technion
Intel Project

http://icri-ci.technion.ac.il/files/2014/05/36-Lior-Wolf-140513.pdf

4. DeViSE: A Deep Visual-Semantic Embedding Model
http://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/41473.pdf

5. ImageNet Classification with Deep Convolutional
Neural Networks
http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf

6. 1) Jeffrey Pennington, Richard Socher, and Christopher D. Manning. "GloVe: Global Vectors for Word Representation." EMNLP, 2014. (Thanks Stoney!) 

http://stanford.edu/~jpennin/papers/glove.pdf

http://nlp.stanford.edu/projects/glove/ 

2) Remi Lebret and Ronan Collobert. "Word Embeddings through Hellinger PCA." 2013. (Thanks Andre and Diego!) 

http://arxiv.org/pdf/1312.5542v2.pdf 

7. 1) Omer Levy and Yoav Goldberg. "Linguistic Regularities in Sparse and Explicit Word Representations." CoNLL, 2014. (Thanks Andre!) 

http://levyomer.wordpress.com/2014/04/25/linguistic-regularities-in-sparse-and-explicit-word-representations/ 

http://levyomer.files.wordpress.com/2014/04/linguistic-regularities-in-sparse-and-explicit-word-representations-conll-2014.pdf

word2vec Explained: Deriving Mikolov et al.’s
Negative-Sampling Word-Embedding Method
Yoav Goldberg and Omer Levy
{yoav.goldberg,omerlevy}@gmail.com

February 14, 2014
http://arxiv.org/pdf/1402.3722v1.pdf

http://arxiv.org/abs/1402.3722

2) Marco Baroni, Georgiana Dinu, and German Kruszewski. "Don’t count, predict! A systematic comparison ofcontext-counting vs. context-predicting semantic vectors." Proceedings of ACL, 2014. (Thanks Andre!) 

http://clic.cimec.unitn.it/marco/publications/acl2014/baroni-etal-countpredict-acl2014.pdf