Energy-Based Models with Applications to Speech and Language Processing
DOI10.1561/2000000117arXiv2403.10961OpenAlexW4392811557MaRDI QIDQ6125998
Publication date: 9 April 2024
Published in: Foundations and Trends® in Signal Processing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2403.10961
stochastic optimizationMarkov chain Monte Carlostatistical signal processinggraphical modelsvariational inferencedeep learningprobability and statisticsclassification and predictionstatistical/machine learningspeech and spoken language processing
Computational methods in Markov chains (60J22) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Artificial neural networks and deep learning (68T07) Monte Carlo methods (65C05) Numerical optimization and variational techniques (65K10) Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Numerical analysis or methods applied to Markov chains (65C40)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Parametric inference for imperfectly observed Gibbsian fields
- Connectionist learning of belief networks
- Exponential convergence of Langevin distributions and their discrete approximations
- Modeling visual patterns by integrating descriptive and generative methods
- Stochastic approximation and its applications
- An introduction to variational methods for graphical models
- Weak Convergence Rates of Population Versus Single-Chain Stochastic Approximation MCMC Algorithms
- Training Products of Experts by Minimizing Contrastive Divergence
- An Introduction to Conditional Random Fields
- Graphical Models, Exponential Families, and Variational Inference
- Probabilistic Networks and Expert Systems
- Stochastic Approximation in Monte Carlo Computation
- Stability of Stochastic Approximation under Verifiable Conditions
- Probability of error of some adaptive pattern-recognition machines
- A Fast Learning Algorithm for Deep Belief Nets
- A Stochastic Approximation Method
- Graphical models
- Monte Carlo strategies in scientific computing
This page was built for publication: Energy-Based Models with Applications to Speech and Language Processing