Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models
Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …
the distribution of training samples. Research has fragmented into various interconnected …
Energy-based out-of-distribution detection
Determining whether inputs are out-of-distribution (OOD) is an essential building block for
safely deploying machine learning models in the open world. However, previous methods …
safely deploying machine learning models in the open world. However, previous methods …
Implicit generation and modeling with energy based models
Y Du, I Mordatch - Advances in Neural Information …, 2019 - proceedings.neurips.cc
Energy based models (EBMs) are appealing due to their generality and simplicity in
likelihood modeling, but have been traditionally difficult to train. We present techniques to …
likelihood modeling, but have been traditionally difficult to train. We present techniques to …
Plug & play generative networks: Conditional iterative generation of images in latent space
Generating high-resolution, photo-realistic images has been a long-standing goal in
machine learning. Recently, Nguyen et al. 2016 showed one interesting way to synthesize …
machine learning. Recently, Nguyen et al. 2016 showed one interesting way to synthesize …
Learning generative vision transformer with energy-based latent space for saliency prediction
Vision transformer networks have shown superiority in many computer vision tasks. In this
paper, we take a step further by proposing a novel generative vision transformer with latent …
paper, we take a step further by proposing a novel generative vision transformer with latent …
Object representations as fixed points: Training iterative refinement algorithms with implicit differentiation
Current work in object-centric learning has been motivated by developing learning
algorithms that infer independent and symmetric entities from the perceptual input. This often …
algorithms that infer independent and symmetric entities from the perceptual input. This often …
[HTML][HTML] Una revisión sistemática sobre aula invertida y aprendizaje colaborativo apoyados en inteligencia artificial para el aprendizaje de programación
CG Hidalgo Suárez, JM Llanos Mosquera… - Tecnura, 2021 - scielo.org.co
Objetivo: La implementación de estrategias pedagógicas como el aula invertida (AI) y el
aprendizaje colaborativo (AC) han contribuido en la enseñanza de programación de …
aprendizaje colaborativo (AC) han contribuido en la enseñanza de programación de …
Learning non-convergent non-persistent short-run mcmc toward energy-based model
This paper studies a curious phenomenon in learning energy-based model (EBM) using
MCMC. In each learning iteration, we generate synthesized examples by running a non …
MCMC. In each learning iteration, we generate synthesized examples by running a non …
On the anatomy of mcmc-based maximum likelihood learning of energy-based models
This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in
unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of …
unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of …
Generalized energy based models
We introduce the Generalized Energy Based Model (GEBM) for generative modelling. These
models combine two trained components: a base distribution (generally an implicit model) …
models combine two trained components: a base distribution (generally an implicit model) …