Please enable JavaScript.

Coggle requires JavaScript to display documents.

CS224n: Natural Language Processing with Deep Learning (Schedule and…

- - - - Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models]
      - [On Using Very Large Target Vocabulary for Neural Machine Translation]
      - [Neural Machine Translation of Rare Words with Subword Units]
      - [Pointing the Unknown Words]
    - - [paper]
      - [slides]
  - - - 1. [Sequence to Sequence Learning with Neural Networks]
      - 3. [Effective Approaches to Attention-based Neural Machine Translation]
      - 2. [Neural Machine Translation by Jointly Learning to Align and Translate]
    - - [paper]
      - [slides]
  - - - [slides]
      - Paper
        
        [paper 1]
        
        [paper 3]
        
        [paper 2]
  - - - [paper]
      - [slides]
    - - [vanishing grad notebook]
      - [vanishing grad example]
  - - - [paper]
      - [slides]
    - - [TensorFlow Basic Usage]
  - - - 1. Joakim Nivre. 2004. Incrementality in Deterministic Dependency Parsing.
        Workshop on Incremental Parsing.
      - 6. Universal Dependencies website
      - 2. Danqi Chen and Christopher D. Manning. 2014. A Fast and Accurate Dependency Parser using Neural Networks. EMNLP 2014.
      - 3. Sandra Kübler, Ryan McDonald, Joakim Nivre. 2009. Dependency Parsing. Morgan and Claypool. [Free access from Stanford campus, only!]
      - 5. Marie-Catherine de Marneffe, Timothy Dozat, Natalia Silveira, Katri Haverinen, Filip Ginter, Joakim Nivre, and Christopher D. Manning. 2014. Universal Stanford Dependencies: A cross-linguistic typology. Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014). Revised version for UD v1.
      - 4. Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, and Michael Collins. 2016. Globally Normalized Transition-Based Neural Networks. ACL 2016.
    - - [paper]
      - [slides]
  - - - 4. [More Optimization (SGD) Review]
      - 1. [Linear Algebra Review]
      - 3. [Convex Optimization Review]
      - 2. [Probability Review]
  - - - 3. [Efficient Estimation of Word Representations in Vector Space]
      - 2. [Distributed Representations of Words and Phrases and their Compositionality]
      - ‍‍1. [Word2Vec Tutorial - The Skip-Gram Model]
    - - [paper]
      - [slides]
  - - - ‍‍1. [GloVe: Global Vectors for Word Representation]
      - ‍‍‍‍‍‍ ‍‍3. [Evaluation methods for unsupervised word embeddings]
      - ‍‍2. [Improving Distributional Similarity with Lessons Learned fromWord Embeddings]
    - - [paper]
      - [slides]
  - - - ‍‍‍‍‍‍4. [Learning Representations by Backpropogating Errors]
      - ‍‍‍‍‍‍1. ‍‍cs231n notes on [backprop] and [network architectures]
      - ‍‍3. [Natural Language Processing (almost) from Scratch]
      - ‍‍2. [Review of differential calculus]
  - - - [slides]
      - [paper]
    - - ‍‍‍‍‍‍‍‍2. Section 4 of [A Primer on Neural Network Models for Natural Language Processing]
      - ‍‍‍‍‍‍1. ‍‍[Vector, Matrix, and Tensor Derivatives]
  - - - [slides]
      - [paper]
    - - 1 . A Convolutional Neural Network for Modelling Sentences
      - 2. Convolutional Neural Networks for Sentence Classification
  - - - [slides]
      - [paper]
    - - [Parsing with Compositional Vector Grammars]
      - [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank]
      - [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks]
  - - - [slides]
      - [paper]