GCRNNs improve graph problem solving with fewer parameters.
problem Graph process problems like earthquake epicenter identification and weather prediction.
method GCRNNs use convolutional filter banks and time-gated variations of GCRNNs (Gated GCRNNs) to improve performance.
result GCRNNs significantly improve performance over GNNs and another graph recurrent architecture.
GCRN models graph-structured sequences with CNN and RNN.
problem Predicting structured sequences of data.
method Combines CNN and RNN on graphs.
result Improves precision and learning speed.
AGCRN forecasts traffic using adaptive graph and recurrent learning.
problem Forecasting traffic dynamics with complex spatial and temporal correlations.
method Adaptive Graph Convolutional Recurrent Network (AGCRN) with Node Adaptive Parameter Learning (NAPL) and Data Adaptive Graph Generation (DAGG).
result AGCRN outperforms state-of-the-art models without pre-defined graphs.
Paper uses neural networks to detect anomalies in graph time series data.
problem Anomaly detection on graph time series data.
method Combines RNN, VI, and graph convolutional network for anomaly detection.
result Demonstrates improved anomaly detection capability on traffic flow data.
Enhances graph neural networks with Relational Pooling for better graph classification.
problem Improving graph neural networks for better graph classification.
method Relational Pooling (RP) based on finite partial exchangeability theory.
result Improved performance on graph classification tasks.
T-GCN predicts traffic using neural networks for spatial and temporal data.
problem Accurate real-time traffic forecasting in urban networks.
method Combines GCN for spatial and GRU for temporal data analysis.
result T-GCN outperforms state-of-the-art baselines on real-world traffic datasets.
SHARE predicts city-wide parking availability using a hierarchical graph neural network.
problem Predicting city-wide parking availability is challenging due to spatial and temporal autocorrelation.
method SHARE uses a hierarchical graph convolution structure with contextual and soft clustering blocks, a recurrent neural network, and a parking availability approximation module.
result SHARE outperforms state-of-the-art baselines in predicting city-wide parking availability.
Deep learning framework predicts traffic patterns on road networks.
problem Challenges in spatiotemporal traffic forecasting.
method Proposes TGC-LSTM, a graph convolutional LSTM neural network.
result Outperforms baseline methods in real-world traffic datasets.
Overview of structured data representation methods.
problem Structured data lacks vectorial form, complicating machine learning.
method Various approaches including kernel, distance, neural networks, and graph convolutional networks.
result New approaches like metric learning and recurrent decoder networks have emerged.
EWS-GCN improves credit scoring by analyzing money transfer connections.
problem Improving credit scoring in transactional banking data.
method Edge Weight-Shared Graph Convolutional Network (EWS-GCN) combining graph and recurrent neural networks.
result EWS-GCN outperforms state-of-the-art models in credit scoring.
Predicts fracture evolution and material failure in brittle materials.
problem Predicting how fractures propagate and materials fail in brittle materials.
method Recurrent graph convolutional neural networks trained on simulation data.
result Predictions within 3% for fracture damage and 15% for time to failure.
DCRNN improves traffic forecasting by 12-15% on large road networks.
problem Challenges in spatiotemporal forecasting, especially for traffic.
method DCRNN models traffic as a diffusion process on a graph, incorporating spatial and temporal dependencies.
result Consistent improvement of 12-15% over state-of-the-art baselines on real-world datasets.
ST-UNet models spatio-temporal graphs by pooling and unpooling operations.
problem Lack of effective means to extract dynamic features from spatio-temporal graphs.
method Designing a multi-scale architecture, Spatio-Temporal U-Net (ST-UNet), with paired sampling operations.
result Achieves substantial improvements in spatio-temporal prediction tasks.
PSTN improves traffic condition forecasting with deep neural networks.
problem Challenges in accurately forecasting traffic conditions due to complex spatiotemporal correlations.
method Proposes PSTN with three modules: graph convolutional network, temporal convolutional network, and gated recurrent unit framework.
result Significantly outperforms state-of-the-art benchmarks in short-term traffic conditions forecasting.
EvolveGCN adapts GCN for dynamic graphs without node embeddings.
problem Learning graph dynamics with frequent node set changes.
method Adapts GCN using RNN to evolve parameters without node embeddings.
result Generally higher performance on link prediction, edge classification, and node classification tasks.
STGCN uses deep learning to forecast traffic, capturing spatial and temporal dependencies.
problem Accurate traffic forecasting for urban control and guidance.
method Spatio-Temporal Graph Convolutional Networks (STGCN) on graphs with complete convolutional structures.
result STGCN outperforms state-of-the-art baselines on various real-world traffic datasets.
A3T-GCN improves traffic forecasting by capturing spatial and temporal dependencies.
problem Accurate real-time traffic forecasting in complex road networks.
method Attention Temporal Graph Convolutional Network (A3T-GCN) integrating recurrent units and graph convolutional network.
result Improved prediction accuracy through attention mechanism and global temporal information.
Convolutional LSTM networks outperform GRU in EEG seizure detection.
problem Seizure detection in EEG signals.
method Comparison of LSTM and GRU units, hybrid CNN-RNN architecture, various initialization and regularization methods.
result Convolutional LSTM networks achieve 30% sensitivity at 6 false alarms per 24 hours.
RecNets use RNNs to process image channels in a compact, recurrent way.
problem Creating efficient neural network architectures for computer vision.
method Introducing RecNets with CRC layers that simulate recurrent processing of image channels.
result RecNets achieve superior size-accuracy trade-off compared to other compact models.
Transfer learning improves highway traffic forecasting using graph neural networks.
problem Lack of historical data for traffic forecasting on large highway networks.
method Developed a transfer learning approach for DCRNN, a graph neural network for highway forecasting.
result TL-DCRNN can forecast traffic on unseen regions of the highway network with high accuracy.
RNNs learn combinatorial graph problems with sample complexity bounds.
problem Learning efficient approximations for real-valued combinatorial graph problems.
method Upper bounds the sample complexity for learning real-valued RNNs.
result Real-valued RNNs can be learned with polynomial number of samples.
Predict stock movement by considering cross effects among stocks.
problem Challenges in predicting stock price movement due to cross effects among stocks.
method Multi-GCGRU framework combining GCN and GRU, encoding cross effects from financial domain knowledge and data-driven relationships.
result Our model outperforms other baselines in predicting stock movement.
Explains three neural network types: feedforward, convolutional, and recurrent.
problem None explicitly stated; focuses on technical introduction.
method Detailed explanation of neural network architectures and backpropagation.
result Derivation of forward pass and backpropagation rules for each network.
Geometric matrix completion learns graph patterns and non-linear diffusion efficiently.
problem Efficiently learn graph patterns and non-linear diffusion from user/item graphs.
method Geometric deep learning on graphs with graph convolutional and recurrent neural networks.
result Outperforms state-of-the-art techniques on synthetic and real datasets.
Unified detection of isolated and overlapping audio events using CNN-RNN.
problem Detecting both isolated and overlapping audio events simultaneously.
method Multi-label multi-task framework based on CNN-RNN, with sequential losses.
result Good generalization on isolated and overlapping audio event detection datasets.
Study compares deep learning models for traffic forecasting, highlighting graph elements' impact.
problem Challenges in forecasting spatial-temporal traffic patterns.
method In-depth comparative study of four deep neural network models with different basic elements.
result Graph attention improves long-term predictions in traffic forecasting models.
Graph-partitioning-based DCRNN improves traffic forecasting for large highways.
problem Challenges in accurately forecasting traffic on large highway networks.
method Graph-partitioning method to decompose large networks into smaller, independent networks.
result Demonstrated improved traffic forecasting on a large California highway network.
This study compares RNN and CNN for predicting wave propagation using the Saint-Venant equations.
problem Predicting wave propagation over long time periods using deep learning.
method Investigated recurrent and convolutional neural networks for their performance in predicting surface waves governed by the Saint-Venant equations.
result Convolutional networks perform at least as well as recurrent networks in predicting wave propagation.
CRAN extracts music highlights using attention and recurrent layers.
problem Extracting valuable music highlights from signals.
method Convolutional Recurrent Attention Networks (CRAN) with attention mechanism.
result CRAN outperforms three baseline methods in highlighting extraction.
DeepAffinity predicts compound-protein affinity from sequences, outperforming existing methods.
problem Lack of methods to predict compound-protein affinity from sequences alone.
method Unified RNN/GCNN-CNN model that unifies recurrent and convolutional neural networks.
result Model outperforms conventional options in predicting affinities with high accuracy.
Recurrent CNNs improve image classification in low light conditions.
problem Poor performance of CNNs in noisy images.
method Added recurrent connections to CNN layers to enhance robustness.
result gruCNNs outperform cCNNs in low signal-to-noise ratio images.
FGNN generalizes graph neural networks to capture higher-order dependencies.
problem Capturing higher-order dependencies in graph-structured data.
method Introducing a factor graph neural network (FGNN) that can represent Max-Product Belief Propagation.
result FGNN effectively represents Max-Product Belief Propagation and performs well on both synthetic and real datasets.
Proposes TDNs for learning complex video structures.
problem Complex temporal dependencies in sequential data, especially videos.
method Temporal Dependency Networks (TDNs) using graph representations and graph convolutions.
result Efficiently learns complex semantic structures of video data.
Differentiable mask prunes deep networks for vision and text.
problem Efficiently compressing deep networks for edge devices.
method Introduces a differentiable mask for sparsity induction.
result Successfully prunes weights, filters, and nodes of convolutional and recurrent networks.
CMDRNN predicts user location using WiFi fingerprints with deep learning.
problem Predicting user activity with WiFi fingerprints is challenging due to high dimensionality.
method Combines CNN, RNN, and MDN to model high-dimensional time-series data.
result CMDRNN effectively predicts user location using WiFi fingerprints.
Statistical model for analyzing longitudinal data on Riemannian manifolds.
problem Analyzing longitudinal data on non-Euclidean spaces.
method Developed a recurrent statistical model for ordered data on Riemannian manifolds.
result Efficient algorithm with competitive performance and fewer parameters.
Paper predicts travel costs across regions using neural networks.
problem Predicting travel costs in sparse, stochastic OD matrices.
method Recurrent Multi-Graph Neural Networks (R-MGNN) for sparse, stochastic OD matrix forecasting.
result Framework effectively predicts future OD matrices without empty elements.
Deep learning predicts real-time parking occupancy using multiple data sources.
problem Predicting real-time parking occupancy in spatio-temporal networks.
method Graph-Convolutional Neural Networks (GCNN) for spatial relations, Recurrent Neural Networks (RNN) with Long-Short Term Memory (LSTM) for temporal features, multiple data sources.
result The model outperforms other methods with an average testing MAPE of 10.6%.
A new neural network learns from acoustic scenes by suppressing irrelevant patterns.
problem Acoustic scenes are rich and redundant, making classification challenging.
method Spatio-temporal attention pooling layer coupled with a convolutional recurrent neural network.
result The method outperforms a strong convolutional neural network baseline and sets new state-of-the-art performance.
TCNs can approximate complex input-output maps with limited memory.
problem Approximating complex input-output maps with limited memory.
method Proved TCNs can approximate a wide class of input-output maps with arbitrary error tolerance.
result Deep ReLU TCNs can approximate input-output maps with finite memory to arbitrary error.
Trellis networks improve sequence modeling performance.
problem Sequence modeling challenges.
method Temporal convolutional network with weight tying and direct input injection.
result Trellis networks outperform state-of-the-art methods on benchmarks.
New optimization algorithms on orthogonal group for machine learning.
problem Efficient optimization on the orthogonal group for machine learning tasks.
method Stochastic geometric algorithms on Lie groups.
result Strong performance on diverse machine learning tasks.
Deep neural network detects heart murmur with high accuracy.
problem Detecting heart murmur from heart sound recordings.
method Parallel combination of RNN-BiLSTM and CNN.
result 96-100% sensitivity and specificity, 98% F1 score.
NNs can learn efficient algorithms for certain problems.
problem Learning efficient algorithms for specific problems.
method Recurrent Convolutional Neural Networks (RCNNs) that learn efficiently.
result RCNNs can learn as well as efficient algorithms described by a constant-sized program.
Automated protein structure prediction from cryo-EM data.
problem Challenging to build atomic models from cryo-EM densities without prior structure.
method Uses GCN and LSTM to automate model building from amino acid identities and candidate locations.
result Automated approach reduces time and eliminates human intervention for protein structure determination.
CNN-RNNs detect bird sounds with high accuracy.
problem Automated detection of bird sounds in varied environments.
method Convolutional Recurrent Neural Networks (CNN-RNNs) for feature extraction and dependency capture.
result 88.5% AUC score on unseen data.
Paper presents RGNN for better graph node representation learning.
problem Node representation learning with graph neural networks.
method Recurrent Graph Neural Network (RGNN) with recurrent units.
result RGNN achieves state-of-the-art results on three benchmarks.
Graph Neural Networks (GNNs) model graph data for tasks like physics, molecular, and disease prediction.
problem Modeling graph data for various learning tasks.
method Message passing between graph nodes using GNN variants like GCN, GAT, GRN.
result GNNs have achieved breakthrough performance in deep learning tasks.