TG-GAN models dynamic graph evolution for continuous-time temporal graphs.
problem Challenges in modeling dynamic temporal graphs, especially in continuous time.
method Temporal Graph Generative Adversarial Network (TG-GAN) that models truncated edge sequences, time budgets, and node attributes.
result TG-GAN significantly outperforms existing methods in efficiency and effectiveness.
Generative models with memory improve temporal data prediction.
problem Modeling temporal data with long-range dependencies.
method Generative Temporal Models augmented with external memory systems within variational inference.
result These models outperform existing models like LSTMs on tasks with sparse, long-term dependencies.
Generative ConvNet models and synthesizes dynamic video patterns.
problem Modeling and synthesizing dynamic patterns in video sequences.
method A spatial-temporal generative ConvNet learns from training sequences through an iterative 'analysis by synthesis' algorithm.
result The model can synthesize realistic dynamic patterns.
Framework for dynamic node embeddings from graph streams.
problem Temporal prediction-based applications using graph stream data.
method ε-graph time-series representation, temporal reachability graphs, weighted temporal summary graphs.
result Dynamic embedding methods achieve better predictive performance.
CODA simulates future data to generalize models across different datasets.
problem Concept drift in real-world machine learning models.
method CODA framework using a predicted feature correlation matrix to simulate future data.
result CODA effectively achieves temporal domain generalization across different model architectures.
New method predicts spatio-temporal data with short and long-range dependence.
problem Uncertainty in predicting the distribution of mixed moving average fields.
method Theory-guided machine learning approach using generalized Bayesian algorithm.
result Fixed-time and any-time PAC Bayesian bounds for ensemble forecasts.
Improves spatio-temporal forecasting by reducing errors between training and inference.
problem Accumulation of small errors in Seq2Seq models during inference due to different distributions of training and inference phases.
method Curriculum learning based on Temporal Progressive Growing Sampling to replace some ground-truth context with generated predictions.
result Better models long-term dependencies and outperforms baseline approaches on two datasets.
Generative Link Sequence Modeling predicts future links in evolving networks.
problem Predicting future links in networks with evolving structures.
method Sequence modeling framework with self-tokenization to capture temporal link formation patterns.
result GLSM achieves best performance on AUC metrics compared to existing methods.
GTEA learns node representations in temporal interaction graphs.
problem Inductive representation learning on temporal interaction graphs.
method Integrates sequence model with time encoder and self-attention scheme for edge and node embeddings.
result GTEA learns comprehensive node representations capturing temporal and structural characteristics.
Modeling solar ramping events with spatio-temporal point processes.
problem Predicting solar ramping events influenced by weather conditions.
method Novel spatio-temporal categorical point process model.
result Effective modeling of spatio-temporal correlations in solar ramping events.
We model GitHub interactions as a temporal knowledge graph for software engineering questions.
problem Insufficient performance of existing temporal models on extrapolated queries and time prediction.
method Introduced an extension to current temporal models using relative temporal information.
result Improved performance on extrapolated queries and time prediction.
Improved incremental sequence classification with temporal consistency.
problem Updating predictions as new sequence elements are revealed.
method Temporal-difference learning and a temporal-consistency condition for successive predictions.
result Optimizing a novel loss function improves data efficiency and predictive accuracy.
SNP extends Neural Processes to handle temporal dependencies in sequences.
problem Handling temporal dependencies in sequences of stochastic processes.
method Integrates a temporal state-transition model into Neural Processes.
result First 4D model capable of dynamic 3D scene modeling.
Modeling disease progression in brain images using monotonic Gaussian Processes.
problem Disentangling spatio-temporal disease trajectories from brain imaging data.
method Spatio-temporal matrix factorization with anatomically plausible priors, monotonic Gaussian Processes, and sparse codes.
result Monotonic Gaussian Processes model realistic disease trajectories in brain imaging data.
Generative model identifies temporal count data components with regime-dependent contributions.
problem Modeling temporal count data with regime-dependent dynamics.
method Generative framework combining regime-adaptive dynamics with Poisson log-normal emissions.
result Established identifiability of the model and revealed co-variation patterns and regime shifts.
New framework for unbiased sampling of temporal networks.
problem Challenges in analyzing and modeling large, continuous temporal networks.
method General framework for unbiased temporal network sampling with online, single-pass algorithms and unbiased estimators.
result Effective algorithms for fast, accurate, and memory-efficient statistical estimation of temporal network patterns and properties.
ESN enhances forecasting for nonlinear spatio-temporal data.
problem Forecasting nonlinear spatio-temporal processes efficiently.
method Enhanced ESN machine learning approach.
result Reasonable uncertainty quantification for long-lead forecasts.
The paper proposes a deep generative model for complex disease trajectories.
problem Modeling and analyzing complex disease trajectories.
method Deep generative time series approach with semi-supervised latent processes.
result The model can discover novel aspects of diseases and cluster them into new sub-types.
HyperST-Net uses hypernetworks to improve spatio-temporal forecasting.
problem Forecasting spatio-temporal data is challenging due to complex spatial and temporal factors.
method Proposes a framework based on hypernetworks with three modules: spatial, temporal, and deduction.
result Models achieve significant improvements over state-of-the-art baselines.
STG2Seq predicts multi-step passenger demand with graph and hierarchical structure.
problem Predicting passenger demand over multiple time horizons is challenging due to nonlinear and dynamic spatial-temporal dependencies.
method Proposes a graph-based model with a hierarchical graph convolutional structure to capture spatial and temporal correlations.
result Consistently outperforms baseline and state-of-the-art models on real-world datasets.
New model infers causal relationships from spatio-temporal data, even with unobserved confounders.
problem Challenges in inferring causal relationships from spatio-temporal data due to unobserved confounders.
method Spatio-Temporal Hierarchical Causal Models (ST-HCMs) that extend hierarchical causal modeling to the spatio-temporal domain, using the Spatio-Temporal Collapse Theorem.
result Validated the effectiveness of ST-HCMs on both synthetic and real-world datasets, demonstrating robust causal inference in complex dynamic systems.
Proposes a new model for complex multivariate event data.
problem Modeling complex multivariate event data with spatio-temporal dynamics.
method Integrates spatial information into latent state evolution through learned temporal and spatial decay dynamics.
result Successfully recovers sensible temporal and spatial intensity structure in multivariate spatio-temporal point patterns.
New method learns dynamic node embeddings from temporal networks.
problem Temporal networks' temporal information is often ignored or approximated.
method Continuous-time dynamic network embeddings (CTDNEs) using temporal walks.
result CTDNEs achieve an average gain of 11.9% in AUC across all methods and graphs.
Bayesian RNN model forecasts and quantifies uncertainty in spatio-temporal data.
problem Uncertainty quantification in nonlinear spatio-temporal systems.
method Developed a Bayesian RNN model to forecast and quantify uncertainty rigorously.
result The model maintains forecast accuracy while quantifying uncertainty formally.
Diffusion Transformer captures spatial-temporal dependencies in sequential data.
problem Capturing rich spatial and temporal dependencies in sequential data.
method Established theoretical guarantees for diffusion transformers learning Gaussian process data.
result Spatial-temporal dependencies are captured within attention layers of diffusion transformers.
DMSTF models spatio-temporal data with deep Markov priors.
problem Analyzing nonlinear multimodal spatio-temporal dynamics.
method Deep Markov spatio-temporal factorization with stochastic variational inference.
result DMSTF outperforms other methods in predictive performance and clustering.
Generative model learns spatial-temporal env. features for reinforcement learning.
problem Training reinforcement learning agents in complex environments.
method World model learns compressed spatial-temporal representation of env. without supervision.
result Compact policy trained on hallucinated dream of world model.
New model tackles complex spatio-temporal causal inference with dynamic confounders and functional data.
problem Complex spatio-temporal dynamics and unmeasured confounders hinder causal inference.
method PFD-BDCM, a unified generative framework for spatio-temporal dependencies, functional data, and dynamic confounding.
result PFD-BDCM outperforms existing methods across observational, interventional, and counterfactual queries.
Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural…
HRTPP improves TPP interpretability and accuracy in medical event modeling.
problem Lack of interpretability in TPPs for medical event sequences.
method Hybrid-Rule Temporal Point Processes (HRTPP) integrating temporal logic rules and numerical features.
result HRTPP outperforms state-of-the-art interpretable TPPs in predictive performance and clinical interpretability.
Factored TSBN improves sequence learning with side information.
problem Learning temporal dependencies in multiple sequences.
method Introducing a three-way weight tensor and factoring transition matrices.
result Achieves state-of-the-art performance on sequential data.
Develops a new model for spatio-temporal data using imitation learning.
problem Capturing complex spatio-temporal dependence in discrete event data.
method NEST point process model with imitation learning for efficient model fitting.
result The imitation learning approach leads to more robust and interpretable results.
New neural model learns from sequences without time backpropagation.
problem Learning useful temporal generative models from sequential data.
method Developed Temporal Neural Coding Network and Discrepancy Reduction algorithm based on predictive coding.
result Algorithm shows promise on bouncing balls generative modeling problem.
TD-VAE learns beliefs about future states in complex environments.
problem Lack of models that can build abstract states, form beliefs, and perform temporal abstraction.
method Temporal Difference Variational Auto-Encoder (TD-VAE) trained on pairs of time points using temporal difference learning.
result TD-VAE generates representations with beliefs about future states, allowing direct rolling out without single-step transitions.
New method estimates GGLM parameters, overcoming non-convexity.
problem Estimating parameters in GGLM with dependencies.
method Monotone operator-based variational inequality method.
result Guarantees for parameter recovery in GLM and GGLM.
IMPaCT improves node classification in chronological split temporal graphs.
problem Domain adaptation challenges in graph data due to chronological splits.
method IMPaCT proposes a method to impose invariant properties based on realistic assumptions derived from temporal graph structures.
result IMPaCT achieves a 3.8% performance improvement over current SOTA method on the ogbn-mag graph dataset.
Unified model improves object-centric world modeling with new abilities.
problem Integration of recent advances and temporal imagination abilities.
method Generative Structured World Models (G-SWM) framework.
result G-SWM achieves best or comparable performance in temporal generation.
Generative model for SSc disease trajectories using deep learning.
problem Modeling complex disease trajectories in Systemic Sclerosis.
method Semi-supervised deep generative model with latent temporal processes.
result Learned latent processes enable personalized monitoring and prediction.
HawkesLLM models text generation with temporal influence, improving semantic alignment under limited memory.
problem Path-dependent uncertainty in agentic text-simulation systems.
method HawkesLLM framework separates temporal influence modeling from text generation, using a multivariate Hawkes process and a language model.
result HawkesLLM improves late-stage semantic alignment under a compact prompt-memory budget.
Researchers develop a method to count and analyze motifs in temporal networks.
problem Understanding the role of network motifs in temporal networks.
method Developed a notion of temporal network motifs and designed fast algorithms for counting them.
result Different motifs occur at different time scales, providing insights into temporal network structure and function.
Enhances SNNs for spatio-temporal feature extraction.
problem Insufficient temporal dependencies in existing SNN synaptic structures.
method Integrates temporal convolution and attention mechanisms into synaptic connections.
result Improves SNN performance on classification tasks.
TOQ-Nets learn to recognize complex temporal events with varying objects and sequences.
problem Recognizing complex relational-temporal events with varying numbers of objects and sequence lengths.
method Neuro-symbolic networks with reasoning layers for finite-domain quantification over objects and time.
result TOQ-Nets can generalize to scenarios with more objects than training data and temporal warpings.
Paper develops Dense NN models for temporal-spatial data with improved performance.
problem Improving predictive performance and robustness in temporal-spatial modeling.
method Fully connected neural networks with ReLU activation, non-asymptotic bounds, manifold modeling, short-range dependence.
result Demonstrates superior performance in temporal-spatial modeling across various synthetic functions.
ATiSE embeds temporal information into KGs using time series decomposition.
problem Improving KG embedding models by incorporating temporal information.
method ATiSE uses Additive Time Series decomposition to map temporal KGs into multi-dimensional Gaussian distributions.
result ATiSE achieves state-of-the-art performance on link prediction over four temporal KGs.
Method predicts spatio-temporal patterns varying by region.
problem Predicting spatio-temporal processes with varying temporal patterns across regions.
method Localized spatio-temporal covariance model and sequential covariance fitting.
result Accurately predicts missing data in spatial regions over time.
EnScale learns to downscale climate models efficiently, capturing both spatial and temporal consistency.
problem Downscaling climate models from coarse to high-resolution data is computationally expensive and challenging.
method EnScale uses generative models and proper scoring rules to map GCM data to RCM data, reducing computational cost.
result EnScale achieves competitive performance and computational efficiency in downscaling multiple climate variables.
TMM improves clustering of temporal data, outperforming existing models.
problem Capturing temporal evolution of clusters in time-driven data.
method Temporal Multinomial Mixture (TMM) model that optimizes feature co-occurrences and temporal smoothness.
result TMM outperforms other clustering models in instance-oriented temporal data.
Proposes a method to forecast spatial-temporal data with limited training data.
problem Forecasting with nodes having no temporal training data.
method Temporal data augmentation and spatial graph topology learning.
result Improves forecasting performance on nodes without training data.