Natural Language Generation: Decoding Techniques and Training Challenges

Related lectures (29)

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Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

On NLG covers basics, autoregressive models, decoding methods, and challenges in text generation.

Introduces the Boltzmann Machine, covering expectation consistency, data clustering, and probability distribution functions.

Explores sampling a probability distribution and structure functions in statistical analysis.

Explores max entropy, Shannon's entropy, Lagrange multipliers, and Monte Carlo sampling techniques.

Covers the implementation of Markov Chain Monte Carlo (MCMC) with the Metropolis algorithm for sampling from posterior distributions.

Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.

Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.

Explores sampling in maximum likelihood estimation and its implications on the joint probability and likelihood contribution.

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.