EB-SeqGANs use energy-based models for better recommendation.
problem Creating accurate and efficient mappings for new item recommendations.
method EB-SeqGANs learn a generative model for user-preferred item sequences, interpreting the energy function as a feature function.
result EB-SeqGANs can be seen as a form of maximum-entropy imitation learning.
Paper proposes CoopFlow, a two-flow generator for energy-based models.
problem Training energy-based models with Langevin flow and normalizing flow.
method CoopFlow trains an energy-based model using a normalizing flow initialization and a short-run Langevin flow revision.
result CoopFlow converges to a moment matching estimator and synthesizes realistic images.
A method to automatically learn proposal distributions for energy-based regression models.
problem Manual design and initial estimate of proposal distributions for energy-based regression models.
method Introduces a method to learn an effective proposal distribution automatically, parameterized by a separate network head, and derives a unified training objective to minimize KL divergence and negative log-likelihood.
result Consistently outperforms conventional MDN training on four real-world regression tasks within computer vision.
Proposes Learnergy, a Python framework for energy-based machine learning.
problem Lack of research and implementations around energy-based systems.
method Built upon PyTorch, Learnergy provides a more friendly environment and faster prototyping.
result Learnergy speeds up computational time using CUDA.
New method learns latent energy models using particle algorithms.
problem Learning latent variable models with energy priors.
method Continuous-time SDEs for MMLE, particle-based discretization.
result Practical algorithm converges to solve MMLE problem.
BiDVL improves EBLVMs for visual tasks by optimizing two variational distributions.
problem Training EBLVMs is challenging due to intractable distributions.
method Bi-level doubly variational learning with two tractable distributions.
result BiDVL achieves impressive image generation and reconstruction performance.
Paper trains two models to improve synthesis quality and unsupervised learning.
problem Improving synthesis quality and unsupervised learning.
method Joint training of an energy-based model and a flow-based model using adversarial value function.
result Significant improvement in synthesis quality of the flow model.
This paper improves image generation models using a multi-grid method.
problem Improving image generation models.
method A multi-grid method for learning energy-based generative ConvNet models.
result The multi-grid method outperforms traditional models.
CEBMs learn flexible latent mappings from data.
problem Learning flexible latent mappings from data.
method CEBMs decompose joint density into tractable posterior over latent variables.
result CEBMs achieve competitive results in image modeling and latent space predictive power.
Reinterprets classifiers as energy-based models for joint distributions.
problem Improving classifier performance and calibration.
method Interprets discriminative classifiers as energy-based models, trains on unlabeled data, and improves model quality.
result Improves calibration, robustness, and out-of-distribution detection.
A new approach to distill unnormalized EBM for energy-based seq2seq models.
problem Training unnormalized EBM for energy-based seq2seq models is challenging.
method Relating the problem to distributional RL, proposing a general distillation approach.
result General approach applicable to any sequential EBM, illustrated on GAM experiments.
Energy-based model learns cost functions from expert demonstrations for optimal control.
problem Learning unknown cost functions from expert demonstrations for optimal control.
method Maximum likelihood estimation via analysis by synthesis, combining Langevin dynamics with optimization and cooperative learning.
result The method can learn suitable cost functions for optimal control tasks.
Unified framework for training generator, energy model, and inference model.
problem Training of generator, energy model, and inference model in a unified probabilistic formulation.
method Divergence Triangle framework integrating variational learning, adversarial learning, wake-sleep algorithm, and contrastive divergence.
result Unified training of generator, energy model, and inference model without costly Markov chain Monte Carlo methods.
Efficiently samples and learns densities with symmetries using equivariant methods.
problem Efficiently sampling and learning densities with symmetries.
method Equivariant Stein Variational Gradient Descent (SVGD) and equivariant energy based models.
result Improves and scales up training of energy based models.
This paper prioritizes experience replay in robotics using energy-based principles.
problem Randomly replaying experience in HER leads to inefficient learning.
method Developed an energy-based framework to prioritize hindsight experience in robotic manipulation tasks.
result EBP outperforms state-of-the-art approaches in robotic manipulation tasks.
Meta-learning approach for fast and compressive energy-based memory models.
problem Learning associative memory with fast and compressive models for complex data.
method Meta-learning approach to energy-based memory models (EBMM) using arbitrary neural architectures.
result Demonstrated associative retrieval outperforming existing systems in reconstruction error and compression rate.
Proposes a neural generator network for efficient energy-based model learning.
problem Challenges in maximum likelihood estimation of energy-based models.
method Uses a neural generator network to approximate the log-likelihood gradient and maximizes entropy of generated samples.
result Generates sharp images with competitive Inception and FID scores, and is robust to mode collapse.
Paper proposes a method to improve MCMC sampling for energy-based models.
problem MCMC sampling of energy-based models is often not mixing in high-dimensional data.
method Proposes using a flow-based model as a backbone to correct the energy-based model, enabling mixing in latent space.
result MCMC sampling of the corrected EBM in the latent space mixes well and traverses modes in the data space.
Unified view of contrastive and supervised learning improves model performance.
problem Improving model performance in classification, robustness, and detection.
method Hybrid discriminative-generative training of energy-based models.
result Improved performance on classification, robustness, out-of-distribution detection, and calibration.
A new loss function ED simplifies training energy-based models without scores.
problem Training energy-based models is computationally expensive.
method Energy Discrepancy (ED) loss function that does not rely on scores or MCMC.
result ED effectively interpolates between score matching and negative log-likelihood.
EBMs improve continual learning without external memory or regularization.
problem Improving continual learning without external memory or regularization.
method Energy-Based Models with contrastive divergence training objective.
result EBMs outperform baseline methods on various benchmarks.
Energy-based models can generate complex images by combining simpler concepts.
problem Generating natural images that satisfy complex logical combinations of concepts.
method Energy-based models combine probability distributions of simpler concepts to generate compositions.
result Energy-based models can generate images that satisfy conjunctions, disjunctions, and negations of concepts.
This paper tackles sampling issues in latent space EBMs by introducing diffusion-based amortization.
problem Degenerate MCMC sampling quality hinders latent space EBM learning and generation quality.
method Introduces diffusion-based amortization for long-run MCMC sampling.
result The learned amortization of MCMC is a valid long-run MCMC sampler.
New algorithms improve learning deep energy models.
problem Learning deep energy models efficiently and accurately.
method Proposed new algorithms combining GAN-style methods with traditional energy-based learning.
result SteinCD performs well in test likelihood, SteinGAN in generating realistic images.
EMIX minimizes surprise in multi-agent reinforcement learning.
problem Surprise and approximation bias in multi-agent reinforcement learning.
method Energy-based MIXer (EMIX) for minimizing surprise across multiple agents.
result EMIX demonstrates consistent stable performance in challenging StarCraft II scenarios.
Energy-based diffusion models improve molecular sampling and simulation.
problem Inconsistency between diffusion model scores and equilibrium distributions.
method Fokker-Planck regularization to enforce consistency.
result Improved consistency and efficient sampling of biomolecular systems.
Unified model explains AT's generative ability.
problem Understanding the generative ability of AT.
method Contrastive Energy-based Models (CEM).
result Improved sample quality in supervised and unsupervised learning.
EB-RANSAC uses energy-based model for robust estimation without complex sampling.
problem Robust estimation of parameters in noisy data.
method EB-RANSAC combines RANSAC's sampling scheme with an energy-based model, simplifying the process and reducing hyperparameter requirements.
result EB-RANSAC effectively solves linear regression and maximum likelihood estimation problems.
This paper proposes a method to train energy-based models using variational auto-encoders for efficient sampling.
problem Training energy-based models by maximum likelihood is challenging due to intractable partition functions and difficult sampling from the model distribution.
method The authors propose using a variational auto-encoder to initialize finite-step MCMC sampling, specifically Langevin dynamics, to train the energy-based model.
result The proposed method enables training energy-based models using maximum likelihood, generating samples comparable to GANs and EBMs.
Paper optimizes energy-based controller for swinging up a pendulum using entropy search.
problem Finding optimal parameters for energy-based controllers is hard.
method Bayesian optimization (Entropy Search) applied to energy-based controller design.
result Optimal controller improves performance of a swinging pendulum.
The paper improves safety in autonomous systems using adversarial learning.
problem Ensuring safety in real-time control systems of autonomous vehicles.
method The paper introduces a dual anomaly detection framework (CFAM and SFAM) using generative adversarial networks (GANs) and video prediction.
result Demonstrated effectiveness on both indoor and outdoor autonomous ground vehicles.
GEBM combines energy function and base distribution for better generative modeling.
problem Improving generative models with better quality samples and performance.
method Alternating training between energy function and base distribution, using MCMC for sampling.
result GEBMs produce higher quality samples and better performance than GANs.
Regularizes model-based planning using energy-based models for efficient learning.
problem Challenges in using learned dynamics models for accurate planning.
method Regularization using energy estimates of state transitions.
result Proposes effective regularization method for planning with pre-trained dynamics models.
Persistently trained EBMs generate images and estimate complex densities.
problem Challenges in ML learning for energy-based models, especially non-convergence of MCMC.
method Introduce diffusion data, learn a joint EBM through persistent training with enhanced sampling.
result First simultaneous achievement of stability, post-training image generation, and superior out-of-distribution detection for image data.
Paper proposes an alternative to MCMC for sampling in energy-based models.
problem Difficulty in generating samples from the current energy function in contrastive approaches.
method Viewing the evolution of the modeling distribution as the evolution of the energy function and samples from this distribution along a time-dependent vector field.
result The proposed method efficiently matches the current distribution in a finite time, unlike MCMC.
A deep learning method solves nonlinear filtering problems efficiently.
problem Nonlinear filtering problem
method Deep splitting method combined with energy-based neural network approximation
result Computational efficiency and performance comparable to Kalman and bootstrap filters
Scaling algorithms improve joint training of deep energy-based models.
problem Joint training of deep energy-based models often fails and delivers worse results.
method Proposed online and offline scaling algorithms to fix joint training.
result Scaling algorithms improve joint training and deliver better results.
STANLEY improves sampling for complex data models.
problem Training Energy-Based models with intractable normalizing constants.
method Anisotropic Langevin Dynamics with gradient-informed covariance.
result Geometrically uniformly ergodic Markov Chain for sampling.
Versatile model for High Energy Physics events.
problem Modeling complex interactions in high-energy physics data.
method Energy-based probabilistic model with multi-purpose architecture.
result Achieves success in diverse applications like simulation, anomaly detection, and particle identification.
A new method to learn EBM in latent space for better data modeling.
problem Efficiently modeling data with complex structures.
method Joint learning of latent space EBM and top-down network using maximum likelihood and MCMC sampling.
result Simple EBM in latent space captures data regularities effectively and performs well in generation and anomaly detection.
The paper controls complex systems using energy-based methods.
problem Controlling nonlinear infinite-dimensional systems with dissipation.
method Energy-based control using Casimir functionals and energy balancing.
result Demonstrated control of a nonlinear Euler-Bernoulli beam.
VAV method optimizes learning rate for faster, stable SGD convergence.
problem Optimizing learning rate for efficient and stable machine learning models.
method Energy-based self-adaptive learning rate with auxiliary variable r. result VAV method achieves faster convergence and superior stability with larger learning rates.
EBIL simplifies IL by estimating expert energy as reward, achieving effective performance.
problem Recovering optimal policy from expert demonstrations without reward signals.
method EBIL uses a two-stage solution: first estimating expert energy as reward, then learning policy.
result EBIL achieves effective performance and interpretable reward signals.
Contrastive learning simplifies statistical inference for complex models.
problem Computational intractability of likelihood functions for certain models.
method Contrastive learning as an alternative for parameter estimation and inference.
result Contrastive learning enables practical methods for diverse statistical problems.
A new method improves training of energy-based models.
problem Training energy-based models is challenging.
method Diffusive Classification (DiffCLF) objective.
result DiffCLF enables EBMs with higher fidelity and broader applicability.
iEFM trains CNF models from unnormalized densities efficiently.
problem Training generators from energy functions or unnormalized densities.
method Iterated energy-based flow matching (iEFM) with simulation-free objective.
result iEFM outperforms existing methods in probabilistic modeling.
New model improves transfer learning and semi-supervised learning.
problem Improving transfer learning and semi-supervised learning performance.
method Developed a new conditional energy-based model (ICE-BeeM) based on nonlinear ICA.
result Identifiable representations learned by ICE-BeeM improve performance in transfer learning and semi-supervised learning tasks.
Future autonomous systems need reliable world models and complex action sequences.
problem Current automated systems lack reliable world models and complex action sequences.
method Introduce energy-based and latent variable models combined in a hierarchical joint embedding predictive architecture (H-JEPA).
result Combining energy-based and latent variable models in H-JEPA can lead to reliable world models and complex action sequences.