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Found 9 papers, 5 papers with code
OmniMotionGPT: Animal Motion Generation with Limited Data
Our paper aims to generate diverse and realistic animal motion sequences from textual descriptions, without a large-scale animal text-motion dataset.
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM
This work proposes a method for using any generator network as the foundation of an Energy-Based Model (EBM).
EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling
This work presents strategies to learn an Energy-Based Model (EBM) according to the desired length of its MCMC sampling trajectories.
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models
Our contributions are 1) an improved method for training EBM's with realistic long-run MCMC samples, 2) an Expectation-Over-Transformation (EOT) defense that resolves theoretical ambiguities for stochastic defenses and from which the EOT attack naturally follows, and 3) state-of-the-art adversarial defense for naturally-trained classifiers and competitive defense compared to adversarially-trained classifiers on Cifar-10, SVHN, and Cifar-100.
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model
We treat this non-convergent short-run MCMC as a learned generator model or a flow 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.
Building a Telescope to Look Into High-Dimensional Image Spaces
However, characterizing a learned probability density to uncover the Hopfield memories of the model, encoded by the structure of the local modes, remains an open challenge.
