Summary of Energy-based Models with Applications to Speech and Language Processing, by Zhijian Ou
Energy-Based Models with Applications to Speech and Language Processing
by Zhijian Ou
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive introduction to Energy-Based Models (EBMs), a class of probabilistic models distinct from self-normalized models like HMMs, autoregressive models, GANs, and VAEs. EBMs have gained popularity in machine learning, with applications in speech, vision, NLP, and more, due to theoretical and algorithmic advancements. The sequential nature of speech and language requires a different approach from processing fixed-dimensional data like images. This monograph focuses on introducing the basics of EBMs, including classic models, recent neural-network-based models, sampling methods, and various learning algorithms. It also presents applications in three scenarios: modeling marginal, conditional, and joint distributions. The paper covers algorithmic progress and applications in speech and language processing, highlighting the use of EBMs for language modeling, speech recognition, sequence labeling, text generation, semi-supervised learning, and calibrated natural language understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a type of model called Energy-Based Models (EBMs). These models are different from other popular models used in machine learning. The goal of this paper is to explain what EBMs are, how they work, and why they’re important for certain tasks like speech recognition, language modeling, and text generation. The authors will introduce the basics of EBMs, including classic models and new methods that use neural networks. They’ll also show how EBMs can be used in different applications, such as modeling what a sentence sounds like or predicting what someone might say next. |
Keywords
» Artificial intelligence » Autoregressive » Language understanding » Machine learning » Neural network » Nlp » Semi supervised » Text generation
