Neural Nets for NLP 2021 - Structured Prediction with Local Independence Assumptions
Explore structured prediction in NLP, focusing on local independence assumptions and Conditional Random Fields. Learn about sequence labeling, potential functions, and dynamic programming models.
Neural Nets for NLP 2021 - Attention
Explore attention mechanisms in neural networks for NLP, covering key concepts, improvements, and specialized varieties with practical examples and applications.
Neural Nets for NLP 2021 - Building A Neural Network Toolkit for NLP
Implement a minimal neural network toolkit for NLP, covering model definition, graph creation, forward/backward calculation, and parameter updates.
CMU Multilingual NLP 2020 - Text Classification and Sequence Labeling
Explore text classification and sequence labeling in multilingual NLP, covering models, datasets, and neural networks for various language processing tasks.
Neural Nets for NLP - Models with Latent Random Variables
Explore neural network models with latent random variables, covering variational autoencoders and their applications in NLP. Learn about generative vs. discriminative models and handling discrete latent variables.
Neural Nets for NLP 2020 - Structured Prediction with Local Independence Assumptions
Explore structured prediction in NLP, covering local independence assumptions and Conditional Random Fields for improved sequence labeling and output consistency.
Neural Nets for NLP 2020 - Attention
Explore attention mechanisms in neural networks for NLP, covering types, improvements, and specialized varieties. Dive into the "Attention is All You Need" paper and its impact on the field.
Neural Nets for NLP 2020: Conditioned Generation
Learn about encoder-decoder models, conditional generation, search techniques, and evaluation methods for neural networks in natural language processing.
Neural Nets for NLP 2020 - Efficiency Tricks for Neural Nets
Efficiency techniques for neural networks in NLP, covering GPU training, parallel processing, and softmax approximations like negative sampling and hierarchical softmax.
Neural Nets for NLP 2020 - Convolutional Neural Networks for Text
Comprehensive exploration of convolutional neural networks for text processing, covering key concepts, applications, and advanced techniques in natural language processing.
Neural Nets for NLP 2019 - Document Level Models
Explore document-level NLP models, covering coreference resolution and discourse parsing techniques. Learn about entity representations, neural coreference, and hierarchical attention networks for discourse analysis.
Neural Nets for NLP - Transition-based Parsing
Explore transition-based parsing for NLP, covering shift-reduce methods, Stack LSTM, phrase structure models, and linearized trees. Gain insights into advanced parsing techniques.
Neural Nets for NLP - Latent Random Variables
Explore latent random variables in neural networks for NLP, covering variational autoencoders, discrete latent variables, and applications in language processing and text generation.
CMU Low Resource NLP Bootcamp 2020 - Speech Synthesis
Explore speech synthesis techniques, from unit selection to neural networks, with insights on data sets, challenges, and multilingual applications in this comprehensive lecture.
CMU Neural Nets for NLP: Model Interpretation
Learn techniques for interpreting neural networks in NLP, including probing sentence embeddings, analyzing model behavior, and generating explanations for model decisions.
Neural Nets for NLP 2020 - Machine Reading with Neural Nets
Explore machine reading techniques for NLP, covering datasets, context encoding, multi-hop reasoning, and dataset caveats. Learn about attention mechanisms and question-answering models.
Neural Nets for NLP 2020 - Neural Nets + Knowledge Bases
Explore methods for learning knowledge bases from neural embeddings and incorporating them into neural models. Covers relation extraction, distant supervision, and reasoning over text corpora as knowledge bases.
Neural Nets for NLP 2020 - Document Level Models
Explore document-level NLP techniques including language modeling, coreference resolution, and discourse parsing. Learn advanced approaches for handling long-range dependencies and entity relationships in text.
CMU Neural Nets for NLP 2020 - Multitask and Multilingual Learning
Explore multitask and multilingual learning in NLP, covering methods, objectives, and challenges. Gain insights into cross-lingual transfer, data balancing, and zero-shot approaches.
Neural Nets for NLP 2020 - Unsupervised and Semi-supervised Learning of Structure
Explore unsupervised and semi-supervised learning methods for NLP, covering feature learning, structure learning, and design decisions with practical examples and applications.
Neural Nets for NLP 2020 - Search-Based Structured Prediction
Learn search-based structured prediction techniques for NLP, including structured perceptron, max-margin objectives, and exposure bias remedies. Explore global models and cost-augmented training for improved sequence prediction.