Search Results for author:
Found 27 papers, 9 papers with code
Improving Adversarial Energy-Based Model via Diffusion Process
Generative models have shown strong generation ability while efficient likelihood estimation is less explored.
Learning Energy-based Model via Dual-MCMC Teaching
To address this issue, we present a joint learning framework that interweaves the maximum likelihood learning algorithm for both the EBM and the complementary generator model.
Learning Hierarchical Features with Joint Latent Space Energy-Based Prior
In this paper, we propose a joint latent space EBM prior model with multi-layer latent variables for effective hierarchical representation learning.
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator
To tackle this issue and learn more expressive prior models, we propose an energy-based model (EBM) on the joint latent space over all layers of latent variables with the multi-layer generator as its backbone.
Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting
To search for molecules with desired properties, we propose a sampling with gradual distribution shifting (SGDS) algorithm, so that after learning the model initially on the training data of existing molecules and their properties, the proposed algorithm gradually shifts the model distribution towards the region supported by molecules with desired values of properties.
Learning Sparse Latent Representations for Generator Model
In this paper, we present a new unsupervised learning method to enforce sparsity on the latent space for the generator model with a gradually sparsified spike and slab distribution as our prior.
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model
Instead, we propose to use noise contrastive estimation (NCE) to discriminatively learn the EBM through density ratio estimation between the latent prior density and latent posterior density.
Learning from the Tangram to Solve Mini Visual Tasks
Current pre-training methods in computer vision focus on natural images in the daily-life context.
Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs
We further define three diagnosis roles in each visit based on the variation of node properties to model disease transition processes.
Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI
This workshop pays a special interest in theoretic foundations, limitations, and new application trends in the scope of XAI.
STAR: Sparse Transformer-based Action Recognition
The cognitive system for human action and behavior has evolved into a deep learning regime, and especially the advent of Graph Convolution Networks has transformed the field in recent years.
Generative Text Modeling through Short Run Inference
It is initialized from the prior distribution of the latent variable and then runs a small number (e. g., 20) of Langevin dynamics steps guided by its posterior distribution.
Transformers satisfy
The Propositional Satisfiability Problem (SAT), and more generally, the Constraint Satisfaction Problem (CSP), are mathematical questions defined as finding an assignment to a set of objects that satisfies a series of constraints.
From em-Projections to Variational Auto-Encoder
This paper reviews the em-projections in information geometry and the recent understanding of variational auto-encoder, and explains that they share a common formulation as joint minimization of the Kullback-Leibler divergence between two manifolds of probability distributions, and the joint minimization can be implemented by alternating projections or alternating gradient descent.
Learning Latent Space Energy-Based Prior Model for Molecule Generation
Deep generative models have recently been applied to molecule design.
Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling
This paper proposes a latent space energy-based prior model for semi-supervised learning.
Learning Latent Space Energy-Based Prior Model
Due to the low dimensionality of the latent space and the expressiveness of the top-down network, a simple EBM in latent space can capture regularities in the data effectively, and MCMC sampling in latent space is efficient and mixes well.
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model
This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and the latent energy-based model (EBM).
Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference
Learning such a generative model requires inferring the latent variables for each training example based on the posterior distribution of these latent variables.
Deep Unsupervised Clustering with Clustered Generator Model
We propose the clustered generator model for clustering which contains both continuous and discrete latent variables.
Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model
This paper proposes the divergence triangle as a framework for joint training of a generator model, energy-based model and inference model.
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models
On the other hand, ConvNet potentials learned with non-convergent MCMC do not have a valid steady-state and cannot be considered approximate unnormalized densities of the training data because long-run MCMC samples differ greatly from observed images.
Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model
This paper proposes the divergence triangle as a framework for joint training of generator model, energy-based model and inference model.
A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models
In this paper, we review three families of probability models, namely, the discriminative models, the descriptive models, and the generative models.
Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry
We present a deformable generator model to disentangle the appearance and geometric information for both image and video data in a purely unsupervised manner.
Replicating Active Appearance Model by Generator Network
A recent Cell paper [Chang and Tsao, 2017] reports an interesting discovery.
Alternating Back-Propagation for Generator Network
This paper proposes an alternating back-propagation algorithm for learning the generator network model.
