Survey on combining causal models with deep generative models for improved explainability and fairness.
problem Deep generative models lack explainability, induce spurious correlations, and poor out-of-distribution extrapolation.
method Structural causal models (SCMs) combined with deep generative models to address shortcomings.
result Causal generative models offer robustness, fairness, and interpretability.
The bias potential model explains how generative models can generalize or memorize samples.
problem Understanding and achieving generalization in generative models like GANs.
method Introducing the bias potential model to analyze the behavior of generative models.
result Dimension-independent generalization accuracy can be achieved with early stopping in the bias potential model.
Class-conditional generative models are crucial tools for data generation from user-specified class labels. Existing approaches for class-conditional generative models require nontrivial modifications of backbone generative architectures to model conditional information fed into the model. This paper introduces a plug-…
Diffusion models generate new samples by adding and removing noise.
problem Generating new samples from data.
method Apply noise to data, reverse the process to generate new samples.
result Diffusion models can improve classifier performance on imbalanced data.
Unified approach unites GANs and diffusion models using particle methods.
problem Combining GANs and diffusion models for generative tasks.
method Proposes a unified framework where generator training is seen as a generalization of particle models.
result Demonstrates that GANs and diffusion models can be integrated within a unified framework.
PMM uses Bayesian inference to generate data from noisy approximations.
problem Creating flexible generative models for various data types.
method Bayesian inference and conjugate pairs of distributions.
result PMM achieves performance competitive with existing generative models.
Generative models explain machine learning predictions with counterfactual instances.
problem Generating human-interpretable insights into machine learning model predictions.
method Sparse counterfactual explanations using conditional generative models.
result Single forward pass generates batches of counterfactual instances.
Generative models can be unfair and unstable; new methods improve fairness and stability.
problem Generative models unfairly penalize minority data and suffer from MADness.
method Intentionally designed hypernetworks and regularization terms.
result Generative models are more fair, stable, and unbiased with new methods.
Generative models of eye gaze help identify viewers from images.
problem Identifying viewers from images based on their eye movements.
method Derived Fisher kernels from generative models of eye gaze to train a discriminative classifier.
result Performance of the classifier improves with better underlying generative models.
Consistency models generate high-quality samples fast and without iterative sampling.
problem Slow generation in diffusion models.
method Direct mapping of noise to data, supporting fast one-step generation and multistep sampling.
result Consistency models achieve state-of-the-art FID scores and outperform diffusion models in one-step generation.
Prior domain knowledge can greatly help to learn generative models. However, it is often too costly to hard-code prior knowledge as a specific model architecture, so we often have to use general-purpose models. In this paper, we propose a method to incorporate prior knowledge of feature relations into the learning of g…
Survey on deep models for graph generation.
problem Improving fidelity of generated graphs.
method Taxonomy and comparison of deep generative models.
result Advances in deep generative models for graph generation.
Survey of multimodal deep generative models for diverse data types.
problem Inference of shared representations and cross-modal generation from heterogeneous multimodal data.
method Variational autoencoders and other deep generative models.
result A comprehensive survey of multimodal deep generative models.
Model criticism tool evaluates text coherence and structure in generated long-form text.
problem Evaluate the high-level structure of generated text for coherence, coreference, and topicality.
method Apply model criticism in latent space to compare real and generated data distributions.
result Transformer-based models struggle with maintaining structural coherence and coreference.
Synthetic tabular data improves privacy while maintaining model performance.
problem Protecting privacy in synthetic data generation for machine learning.
method Deep generative models for tabular data, emphasizing privacy and model performance.
result Deep generative models enhance synthetic data generation for tabular datasets.
The paper extends statistical inference methods for black-box generative models.
problem Understanding and validating black-box generative models without access to their internal data.
method Develops model-level statistical inference tasks using generative model representations.
result Model-level representations are effective for multiple inference tasks.
This study compares deep generative models to traditional methods for generating financial time series.
problem Generating realistic multivariate financial time series for risk management and portfolio optimization.
method Systematic comparison of deep generative models (DGMs) against state-of-the-art parametric models on synthetic and empirical data.
result Deep generative models outperform traditional parametric models in generating financial time series.
We identify causal models with unobserved confounding using bijective generation mechanisms.
problem Identifying causal relationships with unobserved confounders.
method Establish counterfactual identifiability for BGMs and propose a learning method.
result Learned BGMs enable efficient counterfactual estimation.
A new deep generative model uses BSDEs for high-dimensional data generation.
problem Generating high-dimensional complex data, especially images.
method Combines BSDEs with deep neural networks for training with MMD loss.
result BSDE-Gen effectively generates high-dimensional data with stochasticity.
Framework generates personalized insulin treatment strategies using deep models.
problem Developing optimal personalized treatment strategies for diabetes patients.
method Combines deep generative time series models with decision theory.
result Demonstrated improved personalized insulin treatment strategies for diabetes patients.
Deep models generate geometric objects with global properties.
problem Comparing neural models' global properties from generated samples.
method Training on datasets of reflexive polytopes, comparing different representations.
result Models learn non-trivial global properties of geometric objects.
This research explores discrete diffusion models for natural language generation.
problem Challenges in applying diffusion models to discrete data, especially natural language.
method Investigates Discrete Denoising Diffusion Probabilistic Model (D3PM) and compares it with autoregressive models.
result Discrete diffusion models achieve better processing speed than autoregressive models.
Improves sample quality of generative models using energy-based methods.
problem Low sample quality in generative models.
method Constructs an energy function on latent space, trains an energy-based model, and generates improved samples.
result Significant improvement in sample quality with minimal computational overhead.
Pruning improves model generalization in over-parameterized models, contradicting traditional theories.
problem Pruning's effect on generalization in over-parameterized models.
method Empirical study on standard pruning algorithms and additional regularization effects.
result Pruning leads to better training and regularization, improving generalization.
Humans have an impressive ability to reason about new concepts and experiences from just a single example. In particular, humans have an ability for one-shot generalization: an ability to encounter a new concept, understand its structure, and then be able to generate compelling alternative variations of the concept. We…
Proposes CLSM for better subsequence generation in music sequences.
problem Editing subsequences in music sequences without losing context.
method Context-informed prior and decoder for generative model, context position-informed encoder for inference.
result Contextual latent space is smoother in interpolation and generates higher quality samples.
RSGMs extend SGMs to Riemannian manifolds for better data modeling.
problem Current SGMs are limited to Euclidean spaces; RSGMs handle Riemannian manifolds.
method RSGMs use a noising stage with a diffusion process and a denoising model approximating the time-reversal of the diffusion on Riemannian manifolds.
result RSGMs improve generative modeling for data on Riemannian manifolds.
We investigate the use of entropy-regularized optimal transport (EOT) cost in developing generative models to learn implicit distributions. Two generative models are proposed. One uses EOT cost directly in an one-shot optimization problem and the other uses EOT cost iteratively in an adversarial game. The proposed gene…
DeepWeightFlow generates diverse neural network weights efficiently.
problem Generating complete neural network weights efficiently and accurately.
method Flow Matching in weight space with Git Re-Basin and TransFusion.
result DeepWeightFlow generates high-accuracy neural networks without fine-tuning.
CoFinDiff generates synthetic financial data capturing stylized facts and meeting specified conditions.
problem Limited data availability and difficulty in controlling synthetic financial data generation.
method Conditional diffusion model with cross-attention to incorporate conditions derived from price data.
result Synthetic data generated by CoFinDiff accurately meets specified conditions for trends and volatility.
The paper studies stability of generative models trained on mixed data.
problem Training generative models on mixed datasets (real and synthetic data).
method Developed a framework to rigorously study stability under specific conditions.
result Proved the stability of iterative training under certain conditions.
The paper provides concentration bounds for embeddings of generative models.
problem Establishing accurate statistical analysis of generative models.
method Data Kernel Perspective Space embedding method.
result Required number of sample responses for accurate approximation.
Proves bounds on copyright risk for generative models.
problem Generative models risk producing similar content to copyrighted data.
method Defines NAF, proves bounds, and provides learning algorithms.
result Generative models can be made less likely to produce copyrighted content.
Refines deep generative models to improve data density precision.
problem Achieving precise representation of data probability density in deep models.
method Iterated generative modeling to refine latent space, addressing topological obstructions.
result Latent Space Refinement (LaSeR) protocol improves generative model precision.
New metric evaluates generative models across domains, diagnosing fidelity, diversity, and generalization.
problem Evaluating generative models in diverse domains with limited metrics.
method Introduces a 3D evaluation metric (α-Precision, β-Recall, Authenticity) for domain-agnostic diagnostics. result Unified metric characterizes fidelity, diversity, and generalization, diagnosing model performance.
Generative models enhance BO for large batch optimization.
problem Efficiently sampling solutions in high-dimensional, combinatorial design spaces.
method Train generative models to sample solutions proportional to expected utility.
result Generative models can approximate optimal target distributions under certain conditions.
IFH models graph generation with adjustable sequentiality.
problem Designing flexible graph generation models between one-shot and sequential approaches.
method Based on DDPM, IFH uses a node removal process to generate graphs with adjustable sequentiality.
result IFH models improve graph generation quality and efficiency compared to current methods.
Generative models are known to be difficult to assess. Recent works, especially on generative adversarial networks (GANs), produce good visual samples of varied categories of images. However, the validation of their quality is still difficult to define and there is no existing agreement on the best evaluation process. …
This paper studies the cooperative training of two generative models for image modeling and synthesis. Both models are parametrized by convolutional neural networks (ConvNets). The first model is a deep energy-based model, whose energy function is defined by a bottom-up ConvNet, which maps the observed image to the ene…
Scalable method bounds Lipschitz constant of generative models.
problem Bounding the Lipschitz constant of generative models.
method Layerwise convex approximations using zonotopes.
result Efficient and tight bounds on generative models.
Quantum model generates financial data with fewer parameters.
problem Generating financial data with fewer parameters.
method Applied time-series quantum generative model to financial data.
result Fewer parameters required compared to classical methods.
The paper establishes concentration bounds for embeddings of generative models.
problem Analyzing statistical properties of generative models.
method High probability concentration bounds on sample vector embeddings using Data Kernel Perspective Space.
result Determines the number of samples needed for accurate approximation of generative model embeddings.
Generative model improves safety in self-driving simulators and human motion generation.
problem Improving generative models for constrained domains like safety-critical applications.
method Developed Gen-neG, a denoising diffusion model that uses oracle-assisted guidance.
result Empirically validated Gen-neG for collision avoidance and safety-guarded human motion generation.
SHADOWCAST generates graphs with user-specified attributes.
problem Controlling graph generation with understandable structures.
method Conditional generative adversarial network guided by Markov model.
result Competitive performance in generating desired graphs.
Generates financial time series with stylized facts using diffusion models.
problem Generating realistic synthetic financial time series with statistical properties like fat tails, volatility clustering, and seasonality.
method Utilizes denoising diffusion probabilistic models (DDPMs) with wavelet transformation to convert and generate financial time series.
result Demonstrates that the proposed approach satisfies stylized financial time series properties.
FastFID efficiently trains generative models with FID loss.
problem Efficiently training generative models with FID loss.
method Introduces FastFID to train generative models with FID as a loss function.
result Improves FID for GANs using FID as an additional loss.
Proposes a method to use generators as EBM foundations without latent inference.
problem Training EBMs from generator outputs without latent variables.
method Formulates a Hat EBM using generator outputs and residual variables.
result Strong performance on various generator tasks.
Improved score-based models generate high-quality images up to 256x256.
problem Training score-based models for high-resolution images is unstable and limited.
method Theoretical analysis, exponential moving average of model weights.
result Score-based models can generate high-fidelity images up to 256x256.