The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNN) by reducing parameters in the update and reset gates. We evaluate the three variant GRU models on MNIST and IMDB datasets and show that these GRU-RNN variant models perform as well as the original GRU RNN model while…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,181 papers · 148 categories
Trend · papers per month
48 results for “Gated Recurrent Units (GRUs)”
Improved GRU model with weighted time-delay feedback for long-term dependencies.
problem Modeling long-term dependencies in sequential data.
method Introducing a gated recurrent unit (GRU) with a weighted time-delay feedback mechanism.
result τ-GRU outperforms state-of-the-art models on various tasks.
New method improves learning of long-term dependencies in recurrent models.
problem Improving learning of long-term dependencies in recurrent neural networks.
method Proves learnable gates in recurrent models provide quasi-invariance to time transformations and recovers part of LSTM architecture from axiomatic approach.
result New chrono initialization of gate biases greatly improves learning of long-term dependencies.
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.
Sophisticated gated recurrent neural network architectures like LSTMs and GRUs have been shown to be highly effective in a myriad of applications. We develop an un-gated unit, the statistical recurrent unit (SRU), that is able to learn long term dependencies in data by only keeping moving averages of statistics. The SR…
RAU integrates attention into GRU for better sequence learning.
problem Lack of attention mechanism in GRU leads to information redundancy or loss.
method RAU adds an attention gate to GRU to adaptively focus on regions of interest.
result RAU consistently outperforms GRU and other methods in various tasks.
GRUs exhibit diverse dynamical behaviors but cannot mimic continuous attractors.
problem Understanding and predicting the dynamics of GRUs for neural data.
method Continuous time dynamical systems analysis of GRU networks.
result GRUs can represent stable limit cycles, multi-stable dynamics, and homoclinic bifurcations but not continuous attractors.
Recurrent neural networks with various types of hidden units have been used to solve a diverse range of problems involving sequence data. Two of the most recent proposals, gated recurrent units (GRU) and minimal gated units (MGU), have shown comparable promising results on example public datasets. In this paper, we int…
Gating units in GRUs and LSTMs create slow modes and control phase-space complexity.
problem Training challenges in RNNs due to exploding or vanishing gradients.
method Random matrix theory and mean-field theory applied to GRUs and LSTMs.
result Gates in GRUs and LSTMs lead to accumulation of slow modes and control phase-space complexity.
Study shows GRU model with dropout outperforms in Bitcoin price prediction.
problem Predicting Bitcoin price and volatility using machine learning.
method Advanced machine learning methods including GRU with recurrent dropout, feature engineering, and RMSE evaluation.
result Gated Recurrent Unit (GRU) model with recurrent dropout outperforms traditional models in Bitcoin price prediction.
New method estimates uncertainty in GRUs without sampling.
problem Uncertainty estimation in deep learning models.
method Exponential families for deterministic uncertainty quantification.
result Sampling-free uncertainty estimation for GRUs.
New eGRU unit improves keyword spotting on ultra-low-power devices.
problem Resource constraints of edge devices for neural network deployment.
method Optimized recurrent unit architecture for ultra-low power.
result eGRU is 60x faster and 10x smaller than GRU, maintaining accuracy.
Improved time series classification with GRU-FCN model.
problem Time series classification challenges.
method Hybrid LSTM-GRU model for univariate time series classification.
result GRU-FCN model outperforms state-of-the-art models.
Neural network predicts falls in elderly people up to 10 minutes in advance.
problem Falls prevention in elderly people, especially in aging societies.
method Gated Recurrent Unit (GRU) based neural networks model using heart rate and mean blood pressure signals.
result Predicted syncope occurrence approximately 10 minutes before manual markers.
Neural network combines GRU and SVM for better intrusion detection.
problem Improving accuracy in binary classification for network intrusion detection.
method Integrates GRU with SVM as final output layer, replacing Softmax and cross-entropy.
result GRU-SVM model outperforms conventional GRU-Softmax model in accuracy and prediction time.
GRU-D detects age-specific missing patterns in vital signs.
problem Temporal missingness in clinical time series data.
method Gated recurrent unit with decay mechanisms (GRU-D) trained on MIMIC-IV vital signs.
result GRU-D achieves AUROC 0.780 and AUPRC 0.810 on bootstrapped data.
Recurrent Neural Networks (RNNs), which are a powerful scheme for modeling temporal and sequential data need to capture long-term dependencies on datasets and represent them in hidden layers with a powerful model to capture more information from inputs. For modeling long-term dependencies in a dataset, the gating mecha…
Time-Aware Gated Recurrent Unit Networks for Road Surface Friction Prediction Using Historical Datacs.LG
Study proposes GRU-D networks for missing value handling in road surface friction prediction.
problem Missing values in road surface friction data affect prediction accuracy.
method Gated Recurrent Unit (GRU) network with decay mechanism.
result GRU-D networks outperform baseline models in road surface friction prediction.
DeepESNs outperform ESN and GRUs in multivariate time-series prediction.
problem Comparing DeepESNs and gated RNNs for multivariate time-series prediction.
method Experimental comparison of DeepESNs and gated RNNs (Gated Recurrent Units, Long Short-Term Memory) on 4 polyphonic music tasks.
result DeepESNs outperform ESN and GRUs in terms of prediction accuracy and efficiency.
A major contributing factor to the recent advances in deep neural networks is structural units that let sensory information and gradients to propagate easily. Gating is one such structure that acts as a flow control. Gates are employed in many recent state-of-the-art recurrent models such as LSTM and GRU, and feedforwa…
GRUs predict flu at state and city levels with lower error.
problem Influenza prediction at multiple spatial resolutions.
method Gated Recurrent Unit (GRU) neural network, real-time search data.
result GRU outperforms state-of-the-art methods for flu prediction.
Machine learning predicts movie genres from summaries with high accuracy.
problem Predicting movie genres from plot summaries.
method Used Naive Bayes, Word2Vec+XGBoost, Recurrent Neural Networks, and Gated Recurrent Units (GRU) for text classification and multi-label problem.
result GRU neural networks achieve the best result with a Jaccard Index of 50.0%, F-score of 0.56, and hit rate of 80.5%.
MCRM improves LSTM-GRU memory by compactly nesting them.
problem Improving recurrent neural network memory for temporal sequence tasks.
method Introducing MCRM, a nested LSTM-GRU architecture with compact memory.
result MCRMs outperform existing architectures on specific tasks.
Paper introduces a simpler gated RNN structure to better capture long-term dependencies.
problem Difficulty in learning long-term dependencies in RNNs.
method Proposes a grouped distributor unit (GDU) with partitioned hidden states and adaptive update rates.
result GDU outperforms LSTM and GRU on various tasks, including pathological and natural data.
Enhances spoken speech quality using EEG signals.
problem Improves speech clarity in noisy environments.
method Generative adversarial network (GAN), gated recurrent unit (GRU), temporal convolutional network (TCN) regression models.
result Significant improvement in speech enhancement quality compared to traditional methods.
We present a novel recurrent neural network (RNN) based model that combines the remembering ability of unitary RNNs with the ability of gated RNNs to effectively forget redundant/irrelevant information in its memory. We achieve this by extending unitary RNNs with a gating mechanism. Our model is able to outperform LSTM…
Paper compares GRU and LSTM for predicting wildfire spread direction.
problem Predicting wildfire spread direction with limited data.
method Comparison of Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks.
result GRU performs better for longer time series than LSTM.
Recurrent neural networks improve process instance classification.
problem Classifying ongoing process instances based on activities.
method Applied recurrent neural networks, specifically GRU, to classify business process instances.
result GRU outperforms LSTM in training time with similar accuracy.
Integrates CNN and GRU for precise stock market risk alerts.
problem Predicting future stock market risks and providing early warnings.
method Uses CNN for feature extraction and GRU for time series analysis.
result Effective early warnings of future stock market risks.
DeepProteomics uses neural networks to classify protein families efficiently.
problem Lack of functional annotation for many protein sequences in databases.
method Used RNN, LSTM, GRU, and deep neural network models on a dataset of 40,433 proteins.
result Achieved maximum 78% accuracy in classifying protein families.
A new framework combines CNN and GRU for better structural damage detection.
problem Improving damage detection in structural engineering using machine learning.
method Hierarchical CNN and Gated Recurrent Unit (GRU) framework to model spatial and temporal relations.
result The proposed HCG framework significantly outperforms existing methods for structural damage detection.
Amobee won 3rd and 1st place in SemEval 2018 sentiment classification tasks.
problem Sentiment classification in multiple languages.
method Training GRU-CNN model with word embeddings and stacking ensembles.
result 3rd and 1st place in valence ordinal classification sub-tasks in English and Spanish.
Improved video prediction with bijective Gated Recurrent Units.
problem Ill-posed future video prediction with high variability and error propagation.
method Introduces bijective Gated Recurrent Units for state sharing in auto-encoders.
result Significant reduction in computational cost and memory usage compared to state-of-the-art approaches.
Study improves stock index prediction accuracy using TPE-GRNN models.
problem Enhancing prediction of stock index prices in volatile markets.
method Gated recurrent neural networks (LSTM, GRU) combined with TPE Bayesian optimization.
result TPE-LSTM method shows lowest MAPE (best accuracy) for NIFTY 50 index prediction.
Real-time fetal abdominal aorta measurement from ultrasound images.
problem Automating the challenging task of measuring fetal abdominal aorta diameter from ultrasound images.
method Proposes a neural network architecture with three blocks: convolutional layer, Convolution Gated Recurrent Unit (C-GRU), and CyclicLoss.
result Significantly improved accuracy and real-time execution speed compared to previous methods.
Tiny Eats GRU detects eating episodes on a microcontroller.
problem Automatic dietary monitoring on low-power devices.
method Shallow gated recurrent unit (GRU) architecture on Arm Cortex M0+.
result Tiny Eats GRU achieves 95.15% accuracy with 4% memory usage and 6 ms latency.
This paper uses LSTM and GRU models to forecast EBIT/EV for better stock selection.
problem Selecting stocks based on EBIT/EV for long-term investors.
method Sequence prediction models (LSTM and GRU) to forecast EBIT/EV.
result GRU model outperforms LSTM model in forecasting EBIT/EV.
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.
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.
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a.…
This paper uses ODE to improve RNN models for time series data.
problem Improving RNN models for irregularly sampled time series data.
method Extending RNNs with Neural Ordinary Differential Equations (ODEs).
result New ODE-based RNN models reduce training and evaluation time.
Hybrid method forecasts distribution feeder loads using LSTM and GRU networks.
problem Forecasting annual load of distribution feeders.
method Hybrid modeling combining LSTM and GRU networks for multi-year data.
result Proposed method outperforms traditional models in real-world application.
Gated Recurrent Units Learning for Optimal Deployment of Visible Light Communications Enabled UAVseess.SP
Optimizes UAV deployment for VLC-enabled UAVs considering illumination distribution.
problem Optimizing UAV deployment for VLC-enabled UAVs with illumination distribution consideration.
method Formulated as an optimization problem, solved using GRUs and Gaussian mixture model.
result Achieves up to 22.1% reduction in transmit power compared to conventional methods.
CARRNN tackles deep learning for sporadic data, improving prediction errors in healthcare.
problem Challenges in learning temporal patterns from sporadic multivariate longitudinal data.
method Developed a novel deep learning architecture combining RNN and CAR models, using a generalized discrete-time autoregressive model.
result CARRNN achieves the lowest prediction errors in multivariate time-series regression tasks.
Study enhances neural network predictions for wave height using topological features.
problem Challenges in predicting wave heights due to short-term and long-term factors.
method Hybridization of persistent homology with neural networks for feature engineering.
result Significant improvements in score and reductions in errors for various neural network models.
In this work, we propose a novel recurrent neural network (RNN) architecture. The proposed RNN, gated-feedback RNN (GF-RNN), extends the existing approach of stacking multiple recurrent layers by allowing and controlling signals flowing from upper recurrent layers to lower layers using a global gating unit for each pai…
Neural networks predict US recessions with SHAP method.
problem Forecasting US recessions using machine learning.
method Long short-term memory (LSTM) and gated recurrent unit (GRU) models compared to linear models. SHAP method applied for interpretation.
result Neural networks can capture business cycle asymmetries and nonlinearities.
Paper proposes a deep learning model to predict stock prices using sentiment analysis.
problem Predicting future stock movement using financial textual and numerical data.
method A blending ensemble deep learning model with two levels of RNNs, LSTM, and GRU followed by a fully connected neural network.
result The model improves prediction accuracy compared to traditional methods.