Neural Nets for NLP - Structured Prediction Basics
Explore structured prediction in NLP, covering perceptron algorithms, max-margin objectives, and exposure bias remedies. Learn techniques for modeling output interactions and training globally normalized models.
Neural Nets for NLP - Debugging Neural Nets
Practical strategies for identifying and resolving issues in neural networks for NLP, covering training and test-time problems, model architecture, and data analysis.
Neural Nets for NLP 2019: Sentence and Contextualized Word Representations
Explore sentence representations and contextual word embeddings for NLP tasks. Learn about multi-task learning, transfer learning, and pre-training techniques to improve model performance.
Neural Nets for NLP 2019 - Attention
Explore attention mechanisms in neural networks for NLP, covering basic concepts, improvements, specialized varieties, and a case study on the Transformer model.
Neural Nets for NLP - Recurrent Networks for Sentence or Language Modeling
Explore recurrent networks, LSTMs, and their applications in sentence modeling and NLP. Learn about vanishing gradients, pre-training, and the strengths and weaknesses of recurrent models.
Neural Nets for NLP 2019 - Convolutional Neural Networks for Language
Explore convolutional neural networks for NLP, covering bag-of-words, context windows, sentence modeling, stacked and dilated convolutions, and structured convolution for sentence pairs.
Neural Nets for NLP 2019 - Word Vectors
Explore word embeddings, their training methods, evaluation techniques, and advanced concepts in natural language processing. Learn to represent words numerically for machine learning applications.
Neural Nets for NLP 2018 - Learning from-for Knowledge Graphs
Explore neural network techniques for knowledge graph construction, relation extraction, and embedding refinement in natural language processing.
CMU Neural Nets for NLP 2018 - Document-Level Models
Explore document-level NLP models, covering language modeling, topic modeling, coreference resolution, and discourse parsing. Learn advanced techniques for analyzing and processing longer texts.
Neural Nets for NLP 2018 - Models of Dialogue
Explore models of dialogue in neural networks, covering generation-based approaches, discourse-level coherence, diversity promotion, evaluation methods, and personality infusion in conversational AI.
CMU Neural Nets for NLP - Models with Latent Random Variables
Explore neural network models with latent random variables for NLP, covering VAEs, generative models, and techniques for discrete latent variables and controllable text generation.
Neural Nets for NLP 2018 - Neural Semantic Parsing
Explore neural semantic parsing techniques, including tree-based models, code generation, weakly supervised learning, and various meaning representations. Learn about graph structures and neural approaches to semantic role labeling.
CMU Neural Nets for NLP - Structured Prediction Basics
Explore structured prediction in NLP, covering sequence labeling, model interactions, training algorithms, and cost-augmented decoding for improved language understanding and generation.
CMU Neural Nets for NLP 2018 - Conditioned Generation
Explore advanced techniques in neural language models, including conditioned generation, sampling methods, ensembling, and evaluation paradigms for natural language processing tasks.
Neural Nets for NLP - Debugging Neural Nets for NLP
Practical strategies for debugging and optimizing neural networks in NLP, covering issues like training, overfitting, initialization, and evaluation metrics.
Neural Nets for NLP: Recurrent Neural Networks
Explore recurrent neural networks for NLP, covering LSTM structures, mini-batching, and handling long sequences. Learn about RNN strengths, weaknesses, and applications in language modeling.
CMU Neural Nets for NLP 2018 - Models of Words
Explore neural network techniques for natural language processing, focusing on word models, embeddings, and evaluation methods. Learn about distributional representations and their applications in NLP systems.
Neural Nets for NLP 2017 - Multilingual and Multitask Learning
Explore multitask, domain adaptation, and multilingual learning in neural networks for NLP. Gain insights into feature extraction, data augmentation, and cross-lingual techniques for improved language processing.
CMU Neural Nets for NLP 2017 - Advanced Search Algorithms
Explore advanced search algorithms for NLP, including beam search and A*-type search, with a focus on their applications, benefits, and challenges in neural network-based language processing.
Debugging Neural Nets for NLP
Comprehensive guide to diagnosing and resolving issues in neural networks for NLP, covering training, decoding, overfitting, and optimization techniques.
Neural Nets for NLP 2017 - Models of Dialog
Explore neural network approaches for chat-based and task-based dialog systems, covering generation models, diversity promotion, personality infusion, and retrieval-based methods.