Internship Report: Hangqian Li

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Related lectures (32)

Page 3 of 4

Compositional Representations and Systematic Generalization

Examines systematicity, compositionality, neural network challenges, and unsupervised learning in NLP.

Neural Word Embeddings: Learning Representations for Natural Language

Covers neural word embeddings and methods for learning word representations in natural language processing.

Data-Driven Insights: NLP and AI Applications

Explores building OS for heterogeneous hardware, data movement efficiency, AI advancements, and NLP challenges.

Transformer Architecture: Subquadratic Attention Mechanisms

Covers transformer architecture, focusing on encoder-decoder models and subquadratic attention mechanisms for efficient processing of input sequences.

Protein Structure Prediction: AlphaFold Evolution and OpenFold Insights

Explores the evolution of protein structure prediction with AlphaFold models and introduces OpenFold insights.

Deep Learning: Exploring Vision and Language Transformers

Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.

Modern NLP: Introduction

By Antoine Bosselut introduces Natural Language Processing and its challenges, advancements in neural models, and course goals.

Coreference Resolution

Delves into coreference resolution, discussing challenges, advancements, and evaluation methods.

Deep Learning Techniques: Recurring Networks and LSTM Models

Discusses the implementation and optimization of recurring networks using LSTM models in deep learning.

Words Tokens: Lexical Level Overview

Explores words, tokens, and language models in NLP, covering challenges in defining them, lexicon usage, n-grams, and probability estimation.