Improved source code summarization using extended Tree-LSTM.
problem Challenges in applying LSTM to structured source code.
method Extended Tree-LSTM for abstract syntax trees (ASTs).
result Multi-way Tree-LSTM achieves better results than state-of-the-art techniques.
Tree-based LSTM improves sequential regression with missing data.
problem Regression for variable-length sequential data with missing samples.
method Tree architecture of LSTM networks, selecting LSTM networks based on presence-pattern of previous inputs.
result Significant performance improvements on financial and real-life datasets.
Study evaluates how limited training data affects streamflow predictions.
problem Limited historical meteorological and streamflow data affects streamflow prediction accuracy.
method Evaluated tree- and LSTM-based models on CAMELS dataset with varying training data sizes and time spans.
result Tree- and LSTM-based models provide similarly accurate predictions on small datasets, but LSTMs are superior with more training data.
Paper combines LSTM and XGB for better hypoxemia prediction.
problem Forecasting hypoxemia in operating rooms using time series data.
method Feature learning via LSTM networks and prediction via gradient boosting trees.
result Augmenting XGB with LSTM features improves prediction performance.
In this paper, we propose a probabilistic parsing model, which defines a proper conditional probability distribution over non-projective dependency trees for a given sentence, using neural representations as inputs. The neural network architecture is based on bi-directional LSTM-CNNs which benefits from both word- and …
New loss function improves classification accuracy.
problem Negative expected accuracy as an alternative to cross entropy for classification.
method Empirical investigation using modified leaky version of expected accuracy.
result Comparable or better accuracy compared to cross entropy, robust to label noise.
Framework combines symbolic expressions and black-box function evaluations for neural programming.
problem Lack of generalization performance on complex programs and large domains.
method Tree LSTMs and tree encoding for integrating symbolic expressions and function evaluations.
result Framework achieves high accuracies and better generalization to higher depth expressions.
Paper combines LSTM and Random Forest for better stock market predictions.
problem Improving stock market trading predictions by integrating technical and fundamental data.
method Integrates LSTM networks with Random Forest algorithms using financial and microeconomic data.
result Hybrid approach outperforms traditional methods combining both technical and fundamental variables.
The paper introduces a tensor-based approach to improve neural models' aggregation of structural context.
problem Sub-optimal use of simple aggregation functions in neural models for structured data.
method Tensor-based formulation and Tucker tensor decomposition to control parameter space size.
result Effective regulation of trade-off between expressivity, computational complexity, and generalisation.
Proposes D2D-LSTM for predicting mobile social network content diffusion paths.
problem Lack of accurate content popularity prediction considering time and location in mobile social networks.
method D2D-LSTM, a deep neural network combining user social features and files features.
result Significantly improved prediction accuracy (up to 85.858%) and faster convergence (less than 100 steps).
Tree-SMU enables strong compositional generalization in neural networks.
problem Zero-shot generalization to novel compositions of concepts.
method Tree Stack Memory Units (Tree-SMU) with Stack Memory Units (SMU).
result Tree-SMU achieves strong empirical results on mathematical reasoning benchmarks.
Paper predicts stock market values using machine learning.
problem Predicting stock market values for Tehran stock exchange groups.
method Used machine learning algorithms including Decision Tree, Bagging, Random Forest, Adaptive Boosting, Gradient Boosting, XGBoost, Artificial neural network, Recurrent Neural Network, and Long short-term memory (LSTM).
result LSTM shows highest accuracy among all algorithms tested.
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.
Machine learning improves American option pricing accuracy.
problem Complexities of American options and traditional models' limitations.
method Monte Carlo simulations combined with machine learning algorithms (Least Square Method, LSTM, GRU).
result GRU model outperforms LSTM in predicting bid prices, enhancing accuracy and stability.
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.
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.
SAPS synthesizes code from natural language specifications.
problem Efficiently translating complex NL specifications into executable code.
method Structure-aware neural network using abstract syntax trees and LSTM.
result SAPS produces correct programs in over 92% of cases.
This paper compares LSTMs and attention mechanisms for financial time series forecasting.
problem Improving financial time series forecasting accuracy.
method Implemented an LSTM with attention mechanism and compared it to a standard LSTM.
result An LSTM with attention can outperform standalone LSTMs, but further investigation is needed.
Novel unsupervised LSTM method for video frame prediction.
problem Video frame prediction with limited labeled data.
method Inception-based LSTM architecture.
result Inception-based LSTM outperforms convolutional LSTM and has better performance than Inception v2 LSTM.
CTRNNs improve blood glucose forecasting in ICU, outperforming traditional models.
problem Forecasting blood glucose in ICU with irregular measurements.
method Continuous time autoregressive recurrent neural networks (CTRNNs) using neural ODE or neural flow layers.
result CTRNNs generally outperform traditional autoregressive models in probabilistic forecasting of blood glucose.
LSTM-FCN and ALSTM-FCN improve time series classification performance.
problem Improving time series classification performance.
method Ablation tests on LSTM-FCN and ALSTM-FCN, comparing z-normalizing techniques, dimension shuffle impact, and GRU replacement.
result LSTM and FCN blocks perform better together, and z-normalizing the whole dataset is more effective.
MC-LSTM extends LSTM to conserve mass in neural networks.
problem Conservation laws in real-world systems.
method Extending LSTM's inductive bias to conserve mass.
result MC-LSTM sets new state-of-the-art for predicting peak flows.
Grow and prune LSTM to reduce parameters and latency.
problem Model redundancy, increased run-time delay, and overfitting in deep LSTM models.
method Hidden-layer LSTM (H-LSTM) with grow-and-prune (GP) training.
result Significant reduction in parameters, latency, and improvement in accuracy.
Proposes a model combining difference-attention and error-correction LSTMs for improved time series prediction.
problem Improving accuracy in time series prediction.
method Combines difference-attention LSTM and error-correction LSTM in a cascade approach.
result Improves prediction accuracy in time series.
Simplified LSTM models improve sentiment analysis on Twitter debate data.
problem Performing sentiment analysis on long sequence data from Twitter debates.
method Developed six parameter-reduced LSTM models (slim LSTM) for faster training and reduced computational cost.
result Slim LSTM models outperform standard LSTM model in sentiment analysis of GOP Debate Twitter dataset.
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.
CD interprets LSTM predictions by identifying word interactions.
problem LSTMs are black boxes; understanding their internal workings is difficult.
method Contextual decomposition (CD) to interpret LSTM predictions.
result CD reliably identifies word interactions and sentiment combinations.
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.
Study benchmarks LSTM units across PyTorch, TensorFlow, Lasagne, and Keras.
problem Comparing LSTM implementations in different deep learning frameworks.
method Benchmarked LSTM units in PyTorch, TensorFlow, Lasagne, and Keras across two speech recognition scenarios.
result Performance differences between LSTM implementations in various frameworks.
Variational Bi-LSTM improves Bi-LSTM performance by jointly optimizing forward and backward paths.
problem Improving performance of Bi-LSTMs by capturing richer data representation.
method Proposes a Variational Bi-LSTM architecture that connects forward and backward paths during training.
result Shows state-of-the-art performance on various benchmarks.
ARIMA-LSTM hybrid model predicts stock price correlation coefficients.
problem Predicting future stock price correlation coefficients for portfolio optimization.
method ARIMA-LSTM hybrid model combining ARIMA for linear tendencies and LSTM for non-linear temporal dependencies.
result ARIMA-LSTM model outperforms other models in predicting stock price correlation coefficients.
State Space LSTM models improve interpretability of LSTM with efficient SMC inference.
problem Combining interpretability of state space models with LSTM's performance.
method Introducing State Space LSTM models and an efficient SMC sampler for direct posterior sampling.
result Efficient SMC inference confirms superior and stable performance on various domains.
Deep neural nets predict aircraft flight paths from weather data.
problem Accurate prediction of aircraft trajectories for aviation efficiency.
method Deep generative convolutional recurrent neural network with tree-based matching.
result Model accurately predicts aircraft flight paths from weather data.
Paper predicts house prices in major Chinese cities using LSTM networks.
problem Predicting house prices in major Chinese cities.
method Used LSTM networks compared to ARIMA for prediction accuracy.
result LSTM networks showed superior accuracy in predicting house prices.
Solves exploding and vanishing gradient problem in LSTMs.
problem Exploding and vanishing gradient problem in LSTM optimization.
method Introduces a simple stochastic algorithm (h-detach) to prevent suppression of gradient components through the cell state path in LSTM.
result Significant improvements in convergence speed, robustness, and generalization over vanilla LSTM training.
Explains RNN and LSTM fundamentals, derives formulas, and addresses training issues.
problem Lack of detailed formulas and unrolling techniques in LSTM and RNN literature.
method Derives canonical RNN and LSTM formulas from differential equations, proposes unrolling technique, addresses training difficulties.
result Provides a comprehensive understanding of RNN and LSTM, including detailed formulas and unrolling techniques.
DA-LSTM adapts LSTM depth to non-uniform data, improving efficiency.
problem Non-uniform information distribution in sequential data cannot be accurately modeled by traditional LSTM.
method Developed DA-LSTM architecture that dynamically adjusts LSTM depth based on information distribution.
result DA-LSTM reduces computation resource usage and convergence time by 41.78% and 46.01% respectively.
BiLSTM models outperform LSTM and ARIMA in financial time series forecasting.
problem Comparing forecasting accuracy of ARIMA, LSTM, and BiLSTM models.
method Behavioral analysis and comparison of BiLSTM and LSTM models.
result BiLSTM models provide better predictions than ARIMA and LSTM models.
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.
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.
LSTMs show surprising few-shot learning ability, improving on MAML.
problem Few-shot learning with limited data.
method Revisited LSTM approach with Outer Product LSTM (OP-LSTM).
result OP-LSTM outperforms MAML on simple few-shot tasks, but not complex ones.
Proposes an interpretable LSTM for time series with exogenous variables.
problem Lack of variable importance characterization in recurrent neural networks.
method Develops a multi-variable LSTM with tensorized hidden states for learning variable-specific representations.
result Variable attention in real datasets is highly aligned with statistical causality.
Deep neural network predicts blood glucose levels for diabetes patients.
problem Diabetes patients need to anticipate hyperglycaemia and hypoglycaemia.
method Used a sequential model with LSTM and Bi-LSTM layers to predict blood glucose levels.
result The proposed network outperforms baseline methods in predicting blood glucose levels.
Paper questions RNN and LSTM's long-term memory and introduces a new definition.
problem Whether RNN and LSTM have long-term memory.
method Introduced a new definition of long-term memory and modified RNN and LSTM to test it.
result RNN and LSTM do not meet the new definition of long-term memory.
We present two simple ways of reducing the number of parameters and accelerating the training of large Long Short-Term Memory (LSTM) networks: the first one is "matrix factorization by design" of LSTM matrix into the product of two smaller matrices, and the second one is partitioning of LSTM matrix, its inputs and stat…
DP-LSTM predicts stock prices using financial news with improved accuracy and privacy.
problem Predicting stock prices with financial news articles.
method Integrates financial news articles into a sentiment-ARMA model, then uses an LSTM network with differential privacy.
result Achieves up to 65.79% improvement in MSE for S&P 500 prediction.
A new framework improves LSTM performance without adding more parameters.
problem Improving LSTM performance without increasing model complexity.
method A unifying framework of bilinear LSTMs that balances hidden state vector size and weight matrix approximation quality.
result Bilinear LSTMs achieve superior performance compared to linear LSTMs without additional parameters.
Model-free RL agents can learn effective planning without explicit model.
problem Challenging domains with combinatorial complexity.
method Model-free RL with inductive bias in neural networks.
result Model-free RL can exhibit planning characteristics without explicit model.