Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The…
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169,051 papers · 148 categories
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48 results for “Temporal Sequence”
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.
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.
GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregationcs.LG
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.
Survey on modeling event sequences through temporal processes.
problem Modeling phenomena with sequences of events over continuous time.
method Probabilistic models based on point processes, categorized into simple, marked, and spatio-temporal.
result Analysis of existing approaches and their applicability to prediction and modeling.
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.
CTCModel extends Keras for transparent CTC classification.
problem Handling unsegmented input sequences with labels related to subsets of frames.
method Combines Keras and CTC implementation in Tensorflow backend.
result CTCModel predicts sequences of labels from unsegmented input.
DGE learns event representations from image sequences without manual annotations.
problem Data hunger and domain adaptation issues in self-supervised learning for temporal segmentation.
method Dynamic Graph Embedding (DGE) learns event representations by iteratively updating a graph and its embedding.
result DGE achieves robust temporal segmentation on benchmark datasets, outperforming state-of-the-art methods.
Paper clusters event sequences using a reinforcement learning approach with policy mixture model.
problem Clustering event sequences with varying temporal patterns.
method Reinforcement learning with a policy mixture model, decomposing sequences into states and actions.
result Effective clustering of event sequences into underlying policies, outperforming existing methods.
Forman--Ricci Curvature on Contact-Sequence Temporal Networks via Spatiotemporal Prism Complexesmath.DG
Study develops curvature for contact-sequence networks, revealing temporal dynamics.
problem Lack of geometric analysis for temporal network sequences.
method Develops Forman--Ricci curvature on spatiotemporal prism complexes.
result Two curvature variants disagree on 56-67% of temporal edges.
Point Process Flowscs.LG
Proposes a new method to model event sequences using normalizing flows.
problem Modeling asynchronous and probabilistic event sequences.
method Intensity-free framework using normalizing flows.
result Effective at capturing stochasticity of discrete event sequences.
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.
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.
New insights into how encoder-decoder networks generate attention matrices.
problem Understanding how encoder-decoder networks use attention matrices.
method Decomposing hidden states into temporal and input-driven components.
result Attention matrices are formed based on task requirements, not architecture type.
Paper monitors system state sequences to detect and assess deviations.
problem Detecting and evaluating deviations in dynamic systems.
method Data reduction, symbolic representation, anomaly detection, Markov Chains, generalized Jensen-Shannon Divergence.
result The approach detects and assesses system deviations probabilistically.
Deep conditional generative models are developed to simultaneously learn the temporal dependencies of multiple sequences. The model is designed by introducing a three-way weight tensor to capture the multiplicative interactions between side information and sequences. The proposed model builds on the Temporal Sigmoid Be…
ASRNN adapts scales dynamically for better sequence modeling.
problem Fixed scales in multiscale RNNs don't match temporal patterns.
method Adaptively learns and adjusts scales based on temporal contexts.
result ASRNNs yield better performances with dynamical scaling.
Graph WaveNet models spatial-temporal graphs by learning hidden dependencies and long sequences.
problem Capturing hidden spatial dependencies and long-range temporal sequences in graphs.
method Graph WaveNet integrates adaptive dependency matrix learning and stacked dilated 1D convolution.
result Graph WaveNet outperforms existing methods on public traffic network datasets.
Recent works demonstrated the usefulness of temporal coherence to regularize supervised training or to learn invariant features with deep architectures. In particular, enforcing smooth output changes while presenting temporally-closed frames from video sequences, proved to be an effective strategy. In this paper we pro…
TIMeSynC combines financial service interactions for intent prediction.
problem Aligning and learning from multi-domain, multi-resolution sequences for accurate intent prediction.
method An encoder-decoder transformer model addressing sequence alignment, temporal dynamics, and dynamic/static sequence combination.
result Significant improvement in intent prediction over existing methods.
STCN combines TCNs with stochastic latent variables for sequence modeling.
problem Performance gap between TCNs and stochastic RNNs, especially with multiple layers of random variables.
method Proposes a hierarchy of stochastic latent variables in a modular architecture.
result Achieves state-of-the-art log-likelihoods across various tasks.
Neural M3 model adapts to diverse user behaviors over short and long timeframes.
problem Adapting to diverse user behaviors over short and long timeframes.
method Neural Multi-temporal-range Mixture Model (M3) combining short-term and long-term models with a learned gating mechanism.
result M3 consistently outperforms state-of-the-art sequential recommendation methods.
The paper tackles video prediction by estimating conditional densities implicitly.
problem Temporal prediction uncertainty and high-dimensional probabilistic inference in natural scenes.
method Score-based conditional density estimation using sequence-to-image networks trained on a resilience-to-noise objective.
result The method handles occlusion boundaries and weights predictive evidence by reliability.
New method embeds RNN Seq2Seq models to visualize spatiotemporal data.
problem Visualizing and interpreting spatiotemporal data in sequence prediction tasks.
method Embedding approach to visualize and interpret RNN Seq2Seq model representations.
result Embedding space projections of RNN Seq2Seq models capture spatiotemporal dynamics.
Deep learning model predicts traffic flows across entire network for multiple steps ahead.
problem Accurately forecasting future traffic flows across all network links.
method Spatial-Temporal Sequence to Sequence (STSeq2Seq) model combining seq2seq and graph convolution.
result STSeq2Seq achieves state-of-the-art performance in traffic forecasting.
Graph Hawkes Neural Network forecasts evolving graph sequences.
problem Modeling dynamic graph sequences with complex event types.
method Generalized Hawkes process to neural network, capturing complex event impacts.
result Effective at predicting future events in evolving graph sequences.
This paper studies mutual information in sequence models and finds Transformers excel in capturing long-range dependencies.
problem Understanding the expressive power of sequence models in capturing temporal dependencies.
method Theoretical and empirical analysis of linear and nonlinear RNNs, including Transformers.
result Transformers can capture long-range mutual information more efficiently than RNNs.
EventFlow forecasts event sequences without autoregression, improving accuracy.
problem Forecasting errors in autoregressive models for event sequences.
method EventFlow uses flow matching to learn joint distributions over event times directly.
result EventFlow reduces forecast error by 20%-53% compared to baselines.
We consider the general problem of modeling temporal data with long-range dependencies, wherein new observations are fully or partially predictable based on temporally-distant, past observations. A sufficiently powerful temporal model should separate predictable elements of the sequence from unpredictable elements, exp…
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.
Cloned HMMs efficiently learn variable order sequences without local minima issues.
problem Learning long-term temporal structure in sequences.
method Constrained HMMs with a sparsity structure that maps hidden states deterministically to emissions.
result Cloned HMMs can model temporal dependencies at arbitrarily long distances and recognize contexts with 'holes'.
New approach for making machine learning models understandable over time.
problem Making machine learning models interpretable for temporal data.
method Cooperative game between predictor and explainer for temporal models.
result Predictor can be tailored to fit interpretable temporal models.
We investigate the problem of modeling symbolic sequences of polyphonic music in a completely general piano-roll representation. We introduce a probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences. Our…
A deep learning model predicts traffic conditions over multiple steps.
problem Multistep traffic forecasting on road networks.
method Attention Graph Convolutional Sequence-to-Sequence model (AGC-Seq2Seq) with attention mechanism.
result AGC-Seq2Seq model outperforms other models in multistep traffic prediction.
The goal of temporal alignment is to establish time correspondence between two sequences, which has many applications in a variety of areas such as speech processing, bioinformatics, computer vision, and computer graphics. In this paper, we propose a novel temporal alignment method called least-squares dynamic time war…
FIBS extracts relevant features from IBTSs for classification.
problem Classifying interval-based temporal sequences (IBTSs) using common algorithms is challenging.
method FIBS extracts features from IBTSs based on relative frequency and temporal relations, incorporating a filter-based selection strategy to avoid irrelevant features.
result FIBS effectively represents IBTSs for classification algorithms, providing similar or better accuracy compared to state-of-the-art competitors.
We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical obs…
A new method aligns convolution filters for temporal sequences using Dynamic Time Warp.
problem Improving deep learning models' ability to handle temporal sequence data.
method Integrates Dynamic Time Warp algorithm into 1-D convolution layers for better alignment of input and filter.
result Exceeds or matches standard 1-D convolution layers in time series classification tasks.
Proposes models to analyze irregular healthcare time series data.
problem Irregular timestamps in healthcare time series data.
method Data augmentation, temporal coarsening, MultiResolution Ensemble (MRE) model.
result Improves mAP on mortality prediction task from 51.53% to 53.92%.
Enhanced LSTM model learns complex temporal dependencies.
problem Modeling long-term dependencies in sequential data.
method Gamma-LSTM with hierarchical memory units and gates.
result Gamma-LSTM outperforms regular and stacked LSTMs in sequence prediction.
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.
ADD-THIN improves TPP forecasting by handling long-term data sequences.
problem Sequential limitations in autoregressive models for TPPs.
method Diffusion model for TPPs that operates on entire sequences.
result ADD-THIN outperforms state-of-the-art models in forecasting.
Predict sepsis early from EHR data with aggregated clinical events.
problem Predict sepsis from clinical data in EHR with temporal interactions.
method Aggregates heterogeneous clinical events, captures temporal interactions with LSTM.
result Achieved high utility score (0.321) in PhysioNet/Computing in Cardiology Challenge 2019.
Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chainstat.AP
Next-generation sequencing (NGS) to profile temporal changes in living systems is gaining more attention for deriving better insights into the underlying biological mechanisms compared to traditional static sequencing experiments. Nonetheless, the majority of existing statistical tools for analyzing NGS data lack the c…
Meta-learning improves event prediction from short sequences.
problem Predicting events from short sequences is challenging.
method Meta-learning approach using recurrent neural networks and monotonic neural networks.
result Meta-learning enhances long-term prediction performance.
Survey of TPPs from Bayesian, neural, and LLM perspectives.
problem Modeling event sequences in continuous time.
method Bayesian, neural, and LLM approaches.
result Advances in LLMs offer new possibilities for TPP modeling.
The study formalizes temporal precision and recall for anomaly detection in sequences.
problem Insufficient understanding of precision and recall in sequential anomaly detection.
method Formalized temporal precision and recall measures, developed time-tolerant confusion matrices, and demonstrated statistical significance.
result Precision and recall may overestimate performance with temporal tolerance.
New method embeds time span into self-attention for better temporal pattern recognition.
problem Capturing temporal patterns in event sequences without recurrent networks.
method Functional time representation learning with Bochner's and Mercer's Theorems.
result Proposed methods outperform baseline models in various continuous-time event sequence prediction tasks.