New multiplicative LSTM architecture outperforms RNNs in character prediction.
problem Optimizing and contrasting RNN architectures for better time series modeling.
method Hessian free optimization applied to various RNN architectures including multiplicative LSTM.
result Multiplicative LSTM hybrid architecture outperformed LSTM and multiplicative RNNs.
mLSTM improves sequence modeling with better autoregressive density estimation.
problem Improving autoregressive density estimation in sequence modeling.
method Introduces mLSTM, a recurrent neural network combining LSTM and multiplicative recurrent networks.
result mLSTM outperforms standard LSTM and its variants in character-level language modeling tasks.
Enhanced LSTM with multiple kernels and attention improves video action recognition.
problem Improving motion understanding in video analysis.
method Proposed a Network-in-LSTM approach with multiple convolutional kernels and layers, and an attention-based mechanism.
result Improves accuracy in supervised classification on UCF-101 and Sports-1M datasets.
Combines LSTM and medians for accurate multistep time series prediction.
problem Accurate multistep time series forecasting for web traffic.
method Uses LSTM for long-term trends and medians for seasonality in multiple time series.
result Improves forecasting accuracy compared to using only LSTM or medians alone.
Study predicts cryptocurrency trends using LSTM model.
problem Predicting cryptocurrency price trends.
method Combination of window-sliding and prediction range method with LSTM model.
result Established model for cryptocurrency price trend prediction.
LSTM-MSNet forecasts time series with multiple seasonal patterns using a unified model.
problem Forecasting time series with multiple seasonal cycles.
method Decomposition-based, unified prediction framework using LSTM.
result LSTM-MSNet outperforms state-of-the-art methods on various datasets.
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.
A forget-gate-only LSTM outperforms standard LSTM on benchmark datasets.
problem The necessity of all gates in LSTM networks.
method A forget-gate-only LSTM with chrono-initialized biases.
result The forget-gate-only LSTM outperforms standard LSTM on MNIST and pMNIST datasets.
DeepDA uses LSTM to track multiple targets in clutter.
problem NP-hard combinatorial optimization in multi-target tracking with clutter.
method LSTM-based deep learning for data association.
result Significant performance on association ratio, target ID switching, and time-consuming tracking.
EA-LSTM improves LSTM for time series prediction by evolving attention.
problem LSTMs struggle with assigning varying attention to sub-windows in time series data.
method Evolutionary attention-based LSTM with competitive random search.
result EA-LSTM achieves competitive performance in multivariate time series prediction.
Model predicts fine dust concentrations in Seoul using LSTM.
problem Predicting concentrations of fine dust in Seoul due to complex interactions.
method Used LSTM based deep learning model to capture non-linear interactions.
result Successfully predicted fine dust concentrations at 25 districts in Seoul.
LSTM models improve macroeconomic forecasting with mixed frequency data.
problem Improving accuracy of macroeconomic forecasts using mixed frequency data.
method Adapted LSTM model to mixed frequency data, using U-MIDAS scheme.
result Proposed LSTM models outperform conventional MIDAS models in out-of-sample predictive performance.
Study compares LSTM and Transformer models in financial time series prediction.
problem Comparing LSTM and Transformer models for financial time series prediction.
method Various LSTM-based and Transformer-based models compared on financial tasks; DLSTM and new Transformer architecture designed.
result Transformer-based models show limited advantage in absolute price sequence prediction, while LSTM-based models perform better on difference sequences.
Research develops a water quality prediction model using LSTM.
problem Global degradation of water resources and need for optimal water quality monitoring.
method Developed a multivariate water quality prediction model using LSTM and historical data.
result Multiple step LSTM model achieved RMSE of 0.227 mg/L.
Paper proposes using LSTM for LSH-based sequence alignment.
problem Sequence alignment using deep learning models.
method Deep bidirectional LSTM for feature learning and LSH-based sequence alignment.
result Higher accuracy achieved with LSTM-based model.
Modeling student behaviors and multiple predictions for early intervention.
problem Predicting student outcomes and interactions among multiple tasks.
method Proposes a variant of LSTM and soft-attention mechanism for heterogeneous behaviors, and co-attention mechanism for task interactions.
result Demonstrated effectiveness in predicting student outcomes and interactions.
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.
Paper proposes binary-valued gates for better LSTM training.
problem LSTMs struggle with soft gates, leading to unclear information flow.
method Introduces binary-valued gates to control information flow more clearly.
result Binary-valued gates improve LSTM performance and generalization.
Paper improves neural network models for MOOC student course prediction.
problem Improving neural network-based predictive methods for MOOC student course trajectory modeling.
method Enhanced LSTM networks and Transformer architecture applied to MOOC student data.
result Improved accuracy in predicting MOOC student course trajectories.
Improved traffic forecasting model handles missing data.
problem Short-term traffic forecasting with missing values.
method Proposed SBU-LSTM architecture with bidirectional and unidirectional LSTM.
result Superior performance in accuracy and robustness for network-wide traffic prediction.
Improved hydrological models using LSTM networks on large datasets.
problem Traditional hydrological models degrade when applied to multiple basins.
method Long Short-Term Memory (LSTM) networks trained on 531 basins from CAMELS data.
result Single LSTM model outperforms regional and basin-specific models.
Study shows adding correlated features doesn't improve LSTM model interpretability for oil stocks.
problem Improving interpretability of LSTM models for predicting oil company stocks.
method Designed and trained Standard LSTM networks using various correlated datasets.
result Adding correlated features does not enhance LSTM model interpretability.
LSTM improves cross-network recommendations by capturing user preference changes and irregular time intervals.
problem Offline cross-network recommender solutions fail to capture user preference changes and dynamic environments.
method Proposes a multi-layered LSTM network with attention mechanisms, higher order interactions, and time-aware gates.
result The model consistently outperforms state-of-the-art in accuracy, diversity, and novelty.
Study compares tessellation strategies for taxi demand-supply forecasting models.
problem Improving taxi demand-supply forecasting using neural networks.
method Compared Voronoi tessellation and Geohash tessellation for LSTM models.
result Variable-sized polygon tessellation yields superior performance in LSTM models.
LSTM networks improve stock price prediction accuracy.
problem Enhancing stock price forecasting accuracy.
method LSTM networks with hyperparameter tuning and feature selection.
result 53% improvement in predictive accuracy.
A new LSTM variant retains semantic information across time.
problem Maintaining semantic information in LSTM hidden states over time.
method Persistent Recurrent Unit (PRU) with a feedforward layer.
result PRU outperforms conventional LSTM in three tasks.
A new model predicts wind speed using multiple meteorological variables.
problem Precise wind speed forecasting for wind power producers and grid operators.
method Multi-variable Stacked Long Short-Term Memory (LSTM) network.
result The proposed MSLSTM model outperforms traditional methods in wind speed prediction.
This paper proposes a semi-tied unit approach to improve efficiency in LSTM and highway networks.
problem Efficiency issues in gating mechanisms of LSTM and highway networks.
method Semi-tied units (STUs) using one shared weight matrix with extra scaling factors.
result Reduced calculation and storage costs by a factor of three for highway networks and four for LSTMs.
This research improves emotion detection from speech, enhancing CCC by 30%.
problem Improving emotion detection from speech for categorical emotions.
method Used LSTM and TC-LSTM networks, trained with multiple datasets and robust features.
result Improved CCC for valence by 30% compared to baseline.
Improved sentiment analysis explanations using LRP for RNNs.
problem Creating understandable explanations for recurrent neural network predictions.
method Extending Layer-wise Relevance Propagation (LRP) to recurrent neural networks (RNNs), specifically to multiplicative connections in LSTMs and GRUs.
result Better explanation quality for sentiment analysis tasks using LRP compared to gradient-based methods.
This work improves LSTM and GRU training stability and generalization.
problem Training instabilities in LSTMs and GRUs on long sequences.
method Developed a mean field theory to optimize initialization hyperparameters.
result Eliminates or reduces training instabilities and improves generalization.
Enhanced multivariate GARCH model using LSTM for better volatility forecasting.
problem Limitations of traditional multivariate GARCH in capturing persistent volatility and co-movement.
method Integrates deep learning (LSTM) into multivariate GARCH models to capture nonlinear and dynamic dependence structures.
result Superior out-of-sample portfolio risk forecast compared to traditional methods.
HFformer outperforms LSTM in high-frequency trading with multiple signals.
problem Improving high-frequency trading performance using deep learning models.
method Introducing HFformer, a hybrid Transformer model for time series forecasting.
result HFformer achieves higher cumulative PnL than LSTM in backtesting.
Extended LSTMs improve volatility prediction by 20%.
problem Predicting asset price volatility with long memory.
method Extended LSTMs with multiple flexible timescales.
result Extended LSTMs outperform rough volatility predictions by 20%.
Paper uses machine learning to optimize UAV deployment for traffic offloading.
problem Optimizing UAV deployment for efficient traffic offloading from ground BSs.
method LSTM for traffic prediction, KEG algorithm for service area determination, multi-access techniques comparison.
result RSMA reduces up to 24% total power consumption compared to conventional methods.
Paper proposes a deep learning approach for hand movement classification from EEG.
problem Classifying hand movements from EEG for brain-computer interfaces.
method Uses a deep attention-based LSTM network to analyze EEG signals.
result Improves classification accuracy over benchmarks and state-of-the-art methods.
Novel spatio-temporal LSTM model forecasts oceanic variables across sensors and scales.
problem Data sparsity and lack of connected spatial and temporal information in environmental datasets.
method SPATIAL LSTM architecture that learns across spatial and temporal scales.
result Framework accurately forecasts oceanic variables with comparable performance to state-of-the-art models.
Proposes POPQORN to quantify robustness of RNNs, including LSTMs and GRUs.
problem Vulnerability to adversarial attacks in recurrent neural networks.
method Develops POPQORN, a general algorithm to quantify robustness of RNNs.
result Demonstrates effectiveness on different network architectures and shows insights from robustness quantification on individual steps.
FLDCRF improves sequence labeling performance with latent dynamics interactions.
problem Sequence labeling with improved performance and latent dynamics interactions.
method Factored Latent-Dynamic Conditional Random Fields (FLDCRF) with multiple latent dynamics interactions.
result FLDCRF outperforms state-of-the-art models across multiple datasets.
Method predicts NBA players' multi-modal movement trajectories.
problem Understanding NBA players' decision-making during games.
method LSTM-based architecture with multi-modal loss function.
result Method outperforms state-of-the-art in predicting realistic trajectories.
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%.
The study predicts stock volatility using LSTM and GARCH models.
problem Accurately predicting stock price volatility is challenging.
method Multiple volatility models (GARCH, GJR-GARCH, EGARCH, LSTM) applied to three sectors.
result LSTM outperformed other models in pharma sector volatility prediction.
Paper presents a new Wi-Fi RSS and geomagnetic field database for indoor localization and trajectory estimation.
problem Indoor localization and trajectory estimation challenges.
method Convolutional neural network (CNN) for RSS data and LSTM network for geomagnetic field intensity.
result CNN and LSTM networks show feasibility for localization and trajectory estimation.
High-order RNNs reduce speech recognition errors.
problem Vanishing gradients in RNNs.
method High-order RNNs with multiple connections from previous time steps.
result HORNNs reduce WER by 4.2% and 6.3% over RNNs.
Hybrid model improves traffic flow prediction accuracy.
problem Predicting traffic flow with high accuracy in short-term future.
method A hybrid model combining hidden Markov model and LSTM.
result Significant performance gains over conventional methods.
A-LSTM improves emotion recognition by better modeling time dependencies.
problem Conventional LSTM's time dependency modeling is limited.
method Proposed A-LSTM for better temporal context modeling in RNNs.
result A-LSTM outperforms conventional LSTM by 5.5% in emotion recognition.
Paper shows comparing single performance scores is insufficient for non-deterministic systems, proposing to compare score distributions.
problem Insufficient comparison of non-deterministic sequence tagging systems.
method Compare score distributions based on multiple executions of LSTM-networks.
result LSTM-networks produce superior and more stable performance when compared using score distributions.
Stacked LSTM improves weather forecasting accuracy by incorporating spatial information.
problem Improving temperature prediction accuracy in weather forecasting.
method 2-layer spatio-temporal stacked LSTM model with independent LSTM models per location in the first layer and combined hidden states in the second layer.
result The stacked LSTM model outperforms single LSTM models in most cases by utilizing spatial information.