Detects weak changes in networked event data.
problem Detecting changes in dynamic systems from streaming event data.
method Sequential hypothesis test, EM-like algorithm for likelihood ratios, distributed computation.
result Achieves weak signal detection through aggregated local statistics.
Estimates network structure from incomplete event data.
problem Estimating network structure from incomplete event data.
method Developed a novel approach using an unbiased estimator of the complete data log-likelihood function.
result Proposed a computationally efficient estimation algorithm.
ProxiModel extracts high-quality news events from news corpora.
problem Mining high-quality structured event knowledge from noisy news data.
method ProxiModel uses a proximity-network to model event correlation within and across news corpora.
result ProxiModel efficiently and effectively extracts high-quality event descriptors and attributes.
Paper proposes PP-GCN for fine-grained social event categorization.
problem Challenges in mining social events due to heterogeneous event elements and social network structures.
method Design an event meta-schema, build an HIN, propose PP-GCN, and use KIES.
result PP-GCN outperforms other techniques in social event detection and clustering.
Develops a neural model to predict event occurrence and timing.
problem Standard event time models ignore the distinction between event occurrence probability and predicted time.
method Introduces a conditional event time model using a neural network with a binary stochastic layer.
result Shows superior event occurrence and timing predictions on various datasets.
Model traffic congestion events using multi-modal data and attention-based neural networks.
problem Capture non-homogeneous temporal and directional spatial dependencies in traffic congestion events.
method Attention-based neural networks for point processes, adapted tail-up model for spatial statistics.
result Superior performance compared to state-of-the-art methods on synthetic and real data.
EvAn detects anomalies in event-based camera data with reduced complexity.
problem Anomaly detection in event-based camera data.
method Dual discriminator cGAN on event data with learned representation.
result Reduction in computational complexity compared to state-of-the-art methods.
Proposes OC4Seq for detecting anomalies in discrete event sequences.
problem Challenges in detecting anomalies in discrete event sequences, including data imbalance, discrete events, and sequential nature.
method Integrates anomaly detection with recurrent neural networks (RNNs) to embed sequences into latent spaces and designs a multi-scale RNN framework to capture multi-scale sequential patterns.
result OC4Seq consistently outperforms various baselines on three benchmark datasets.
Generative model creates EEG data for RSVP experiments.
problem Limited EEG data for training deep learning models.
method Wasserstein Generative Adversarial Network (WGAN-GP) with gradient penalty.
result Improved event classification performance with class-conditioned WGAN-GP.
Model predicts event sequences using neural networks.
problem Predicting future events based on past patterns.
method Neural Hawkes Process with LSTM for evolving event intensities.
result Generative model achieves competitive performance.
Paper examines adversarial attacks on event cause analysis in power grids.
problem Adversarial attacks manipulate data to mislead event classifiers.
method Investigated adversarial attacks on CNN-based event cause analysis.
result Demonstrated adversaries can misclassify events through stealthy data manipulations.
Paper introduces a neural network-based non-stationary influence kernel for complex event data.
problem Modeling complex, non-stationary, and dependent discrete event data.
method Neural Spectral Marked Point Processes (NSMPP) with a versatile non-stationary influence kernel.
result NSMPP outperforms state-of-the-art models on synthetic and real data.
The paper tackles error event identification in network logs.
problem Identifying error events from network message logs.
method Transformed the problem into topic discovery in documents using a non-parametric change-point detection algorithm.
result The algorithm identifies error events from message logs efficiently and accurately.
We present the Infinite Latent Events Model, a nonparametric hierarchical Bayesian distribution over infinite dimensional Dynamic Bayesian Networks with binary state representations and noisy-OR-like transitions. The distribution can be used to learn structure in discrete timeseries data by simultaneously inferring a s…
Paper introduces a novel point process model for graph data using GNNs.
problem Modeling discrete event data over graphs with influence kernel.
method Combines Hawkes kernel and Graph Neural Networks (GNN) for event prediction.
result Achieves superior predictive performance compared to state-of-the-art.
DREAM model improves computational efficiency for non-linear effects in relational event models.
problem Efficiently modeling non-linear effects in dynamic relational networks.
method Introduces Deep Relational Event Additive Model (DREAM) using Neural Additive Models.
result Demonstrates superior computational efficiency compared to traditional REM approaches.
DDP models dynamic comorbidity networks from event data.
problem Understanding complex temporal patterns of co-occurring diseases.
method Developed deep diffusion processes (DDP) to model dynamic comorbidity networks.
result DDP enables accurate risk prediction and interpretable disease trajectories.
Method trains deep neural networks on weakly labeled audio data efficiently.
problem Limited training data and lack of temporal labels for audio event detection.
method Multi-instance learning with a new loss function for stacked CNN-RNN.
result Improved performance on low-resource audio datasets.
SurvCORN predicts survival curves using conditional ordinal ranking networks.
problem Challenges in survival analysis with censored data.
method SurvCORN: Conditional Ordinal Ranking Neural Network.
result SurvCORN improves accuracy in predicting time-to-event outcomes.
An unsupervised neural network learns event truths from social network data.
problem Estimating event truths from conflicting opinions in social networks.
method Autoencoder learns relationships, Bayesian network models agent reliability and social relationships, variational inference estimates hidden variables and parameters.
result The approach outperforms state-of-the-art methods on real datasets.
EvoNet predicts events in time-series data by evolving state graphs.
problem Predicting events in time-series data with interpretable patterns.
method Evolutionary State Graph (ESG) and EvoNet model.
result EvoNet outperforms baselines and provides insights into event predictions.
NESA learns user preferences and calendar contexts for efficient event scheduling.
problem Challenges in understanding user preferences and complex calendar contexts for automated event scheduling.
method Leverages deep neural networks to learn user preferences and calendar context from raw online calendars.
result Significantly outperforms previous models in personal and multi-attendee event scheduling tasks.
Adversarial model estimates event-time distributions from health data.
problem Nonparametric estimation of event-time distributions in time-to-event analysis.
method Adversarial learning approach with a principled cost function for censored events.
result Proposed model yields significant performance gains over parametric alternatives.
Graph neural networks detect anomalies in object-centric business processes.
problem Detecting anomalies in graph-like business processes.
method Graph convolutional autoencoder architecture for anomaly detection.
result Promising performance in detecting anomalies at the activity type and attributes level.
Improved neural models for diverse user event sequences.
problem Challenges in modeling diverse user event sequences.
method Mixtures of latent embeddings with amortized variational inference.
result Systematic improvements over existing work for various predictive metrics.
Proposes online learning for Hawkes processes with network structure and event interaction.
problem Modeling complex interactions and latent structures in network events.
method Online learning approach for mixture of multivariate Hawkes processes.
result Efficacy demonstrated on synthetic and real-world data.
Study infers tree topology from customer data using contrastive learning.
problem Inferring local network topology from customer data.
method Contrastive learning approach for binary event encoding from continuous time series.
result Preliminary results show potential for valuable encoder learning.
Models for predicting the risk of cardiovascular events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used risk prediction models have been built from carefully selected epidemiological cohorts. However, the homogeneity and limited size of such coho…
Modeling event sequences with RNNs for predictive maintenance.
problem Predicting the intensity function of asynchronous event sequences.
method Use two RNNs: one for background and another for history effects.
result End-to-end training of the model for black-box event intensity prediction.
Cascading chains of events are a salient feature of many real-world social, biological, and financial networks. In social networks, social reciprocity accounts for retaliations in gang interactions, proxy wars in nation-state conflicts, or Internet memes shared via social media. Neuron spikes stimulate or inhibit spike…
Paper optimizes NeuCube for better pattern recognition and event prediction in stream data.
problem Improving pattern recognition and event prediction in stream data.
method Optimized mapping of temporal variables into NeuCube spiking neural network.
result Improved accuracy in pattern recognition and event prediction.
SurvLatent ODE predicts VTE risk for cancer patients, outperforming current methods.
problem Predicting clinical outcomes from irregularly sampled EHR data with competing events.
method Neural ODE-based Recurrent Neural Networks (ODE-RNN) for flexible survival time estimation.
result SurvLatent ODE outperforms Khorana Risk scores for VTE risk prediction.
Proposes neural network-enhanced Cox model for time-to-event prediction.
problem Improving time-to-event prediction accuracy.
method Extends Cox proportional hazards model with neural networks, using a scaled loss function.
result Proposed methodology outperforms existing methods in terms of Brier score and binomial log-likelihood.
Deep neural network predicts event ticket prices considering spatial-temporal data sparsity.
problem Predicting future ticket prices from sparse and spatiotemporal data.
method Bi-level optimizing deep neural network with coarsening and refining layers, bi-level loss function.
result Our model outperforms other methods in real-world ticket price prediction.
A new neural network model for predicting event times without distributional assumptions.
problem Predicting event times from censored data with complex assumptions.
method Deep AFT Rank-regression model (DART) using Gehan's rank statistic.
result DART significantly improves performance on various benchmark datasets.
New methods for inferring, predicting, and estimating continuous-time, discrete-event processes.
problem Inferring, predicting, and estimating entropy rate of continuous-time, discrete-event processes.
method Bayesian structural inference extended with neural networks.
result Methods are competitive for prediction and entropy-rate estimation with state-of-the-art.
Event-triggered learning reduces communication in networked control systems.
problem Reduction of communication in networked control systems.
method Triggered learning experiments when communication performance is poor, using statistical properties of inter-communication times.
result Event-triggered learning improves robustness and communication efficiency.
Theory reconstructs network connectivity from event timings.
problem Reconstructing network connectivity from incomplete continuous-time data.
method Linearizes event space mapping to reveal direct influences.
result Reveals synapse presence and inhibitory/activating nature.
Deep learning for particle classification on large event images.
problem Training deep learning models on large, high-fidelity event images from MicroBooNE is challenging and time-consuming.
method Scaling training to multiple GPUs and architectures, using simulated MicroBooNE events.
result Demonstrated successful scaling of particle classification training to multiple GPUs and architectures.
A new method of event reweighting using boosted decision trees is presented.
problem Quality control of reweighting step in machine learning models.
method Novel event reweighting method based on boosted decision trees.
result Improved quality control of reweighting step in machine learning models.
Optimizes seismic monitoring networks using Bayesian OED.
problem Improve seismic event identification and location.
method Bayesian optimal experimental design (OED) to configure sensor networks.
result Optimized sensor network improves seismic event identification and location.
New method infers causality from short memory-less transition data.
problem Inferring causality from short time series data.
method Composition of Transitions (COT) and machine learning models.
result Highly accurate in inferring causal relationships from short data.
Event-based control improves neural network training speed and accuracy.
problem Optimizing learning rate and gradient tuning for CNN convergence.
method Two Event-Based control loops for adjusting learning rate in E/PD algorithm.
result Event-Based E/PD control leads to higher final accuracy and lower final loss.
MIM-based GAN improves rare event generation in GANs.
problem Improving rare event generation in GANs.
method Adopting MIM (exponential form of information metric) to replace KL divergence in GANs.
result MIM-based GAN achieves state-of-the-art performance in anomaly detection.
Online algorithm detects community structure in dynamic event streams.
problem Community detection in networks with temporal event streams.
method Continuous-time point process latent network models with fast online variational inference.
result Online inference achieves comparable community recovery to non-online methods but with computational gains.
Proposes a new model for time-to-event prediction with uncertainty quantification.
problem Lack of uncertainty in time-to-event predictions using recurrent neural networks.
method Deep Kernel Accelerated Failure Time models combining RNN and sparse Gaussian Process.
result Model delivers better uncertainty estimates compared to related methods.
Machine learning improves accuracy of running gait event detection from tibial acceleration.
problem Accurate detection of running gait events from tibial acceleration data.
method Structured machine learning models compared to heuristic methods.
result Structured recurrent neural network model offers most accurate estimation of gait events.
Nonparametric neural-network estimation of current-status data
problem Estimation of conditional cumulative distribution function with current-status data
method Neural-network sieve maximum likelihood estimator
result Explicit convergence rate for Hölder smoothness