Paper predicts EEG features from acoustic features using RNN and GAN.
problem Predicting EEG features from acoustic features.
method Recurrent Neural Network (RNN) and Generative Adversarial Network (GAN).
result Lower RMSE and normalized RMSE values compared to generating acoustic features from EEG features.
RPNN-EOFs model improves time series forecasting accuracy.
problem Improving time series forecasting accuracy for complex systems.
method Combines higher-order neural networks with error-output feedbacks.
result RPNN-EOFs outperformed other models in forecasting the Mackey-Glass time series.
Probability calibration trees improve accuracy of probability estimates.
problem Improving accuracy and calibration of probability estimates from classifiers.
method Probability calibration trees modify logistic model trees to learn different models in regions of the input space.
result Probability calibration trees outperform isotonic regression and Platt scaling in terms of root mean squared error.
The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been…
Unified RNN architecture improves multi-time-horizon solar forecasting.
problem Large prediction errors in traditional solar forecasting methods.
method Recurrent Neural Network (RNN) for multi-time-horizon solar forecasting.
result Lower root-mean-squared prediction error compared to previous methods.
This paper uses GAN and ERMSE to improve stock price movement prediction accuracy.
problem Predicting stock price movement direction is challenging due to complex, incomplete, and fuzzy information.
method The paper proposes a deep learning model using GAN and ERMSE to forecast stock market trends.
result The GAN model outperformed LSTM in predicting stock price movement direction with a 4.35% improvement.
Machine learning models estimate nutrient concentrations from water quality surrogates.
problem Estimating high frequency nutrient concentrations from limited in-situ measurements.
method Used machine learning (Random Forests) to estimate nutrient concentrations using surrogate measures.
result Reduced RMSE by up to 60.1% compared to linear models, with additional sensors not providing significant benefits.
Paper uses LSTM neural networks to forecast commodity prices.
problem Forecasting accuracy of traditional methods like ARIMA.
method Long Short-Term Memory (LSTM) neural networks complement traditional methods.
result Forecast averaging of LSTM and ARIMA models improves forecast accuracy.
The paper compares machine learning models for forecasting residential gas demand, highlighting the impact of temperature forecasts.
problem Forecasting residential gas demand for optimal energy planning.
method Implemented and compared five models: Ridge Regression, GP, k-Nearest Neighbour, ANN, and Torus Model.
result ANN is the best model in terms of RMSE, while GP is the best in terms of MAE.
Bayesian optimization selects wavelengths for sugar content estimation in NIR spectroscopy.
problem Improving prediction accuracy and interpretability of spectral data for sugar content estimation.
method Formulated as a binary black-box optimization problem, proposed method uses Bayesian optimization with a sparse quadratic surrogate model and Thompson sampling.
result Improves prediction accuracy of partial least squares regression and yields more consistent wavelength regions.
Deep neural networks predict electricity consumption accurately.
problem Predicting future electricity consumption for better management.
method Used Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) networks to predict electricity consumption based on past data.
result Both RNN and LSTM achieved an average Root Mean Square error of 0.1.
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.
In this paper we describe a novel implementation of adaboost for prediction of survival function. We take different variations of the algorithm and compare the algorithms based on system run time and root mean square error. Our construction includes right censoring data and competing risk data too. We take different da…
This paper models yearly exchange rates between USD/KZT, EUR/KZT and SGD/KZT, and compares the actual data with developed forecasts using time series analysis over the period from 2006 to 2014. The official yearly data of National Bank of the Republic of Kazakhstan is used for present study. The main goal of this paper…
This paper proposes and analyses a new multilevel Monte Carlo method for the estimation of mean exit times for multi-dimensional Brownian diffusions, and associated functionals which correspond to solutions to high-dimensional parabolic PDEs through the Feynman-Kac formula. In particular, it is proved that the complexi…
We study the stability vis a vis adversarial noise of matrix factorization algorithm for matrix completion. In particular, our results include: (I) we bound the gap between the solution matrix of the factorization method and the ground truth in terms of root mean square error; (II) we treat the matrix factorization as …
JULIA combines multi-linear and nonlinear models for tensor completion.
problem Complex patterns in real-world tensors require a unified model.
method JULIA unifies multi-linear and nonlinear models with flexible component assignment and efficient alternating optimization.
result JULIA outperforms existing methods in large-scale tensor completion.
Transformer model predicts stock prices in Bangladesh's stock market.
problem Predicting volatile stock prices in the Bangladesh stock market.
method Transformer model applied to time series data for stock price prediction.
result Transformer model shows promising results in predicting stock price movements.
A new sparse benchmark metabench identifies key abilities from large benchmarks.
problem Redundancy and compression in existing benchmarks.
method Data from 5000+ LLMs to identify most informative items, distilling a sparse benchmark.
result Sparse benchmark metabench captures underlying abilities with high accuracy.
Optimal stock price prediction model using recurrent neural networks with RMSprop optimizer.
problem Stock price prediction using neural networks.
method Comparison of fully connected, convolutional, and recurrent architectures; inclusion of three optimization techniques.
result Single layer recurrent neural network with RMSprop optimizer produces optimal results with validation and test MAE of 0.0150 and 0.0148 respectively.
Hybrid QML model improves recovery rate prediction accuracy.
problem Complex nonlinear dependencies, high-dimensional feature spaces, and limited sample sizes in recovery rate forecasting.
method Hybrid Quantum Machine Learning (QML) with Amplitude Encoding, leveraging PQC and qubit data compression.
result Significantly lower RMSE (0.228) compared to classical models.
Method detects lithium-ion battery knee onset for early warning.
problem Early warning of lithium-ion battery performance changes.
method Temporal dynamics extraction and Matrix Profile anomaly detection.
result Knee onset detected for categorizing battery health.
Two-step model estimates DLMO using both daily and frequent data.
problem Expensive and time-consuming DLMO measurement.
method Two-step framework combining daily and frequent data.
result Two-step model with two time-scale features has lower errors.
Paper proposes combining GAM and DNN for accurate peak demand estimation from lower-resolution data.
problem Predicting high-resolution peak demand from limited lower-resolution data.
method Combines generalized additive models (GAM) and deep neural networks (DNN) for half-hourly load forecasting.
result Proposed method reduces out-of-sample RMSE by 57.4% compared to benchmark.
Optimizes Gaussian process hyperparameters using Bayesian autoregression.
problem Optimizing hyperparameters for Matérn kernel temporal Gaussian processes.
method Recursive Bayesian estimation for autoregressive parameters.
result Outperforms traditional optimization methods in runtime and accuracy.
Test log-likelihood comparisons can be misleading.
problem Misinterpretation of test log-likelihood in model comparison.
method Simple examples of model comparison and forecast accuracy.
result Test log-likelihood does not always correlate with model accuracy.
The standard Kernel Quadrature method for numerical integration with random point sets (also called Bayesian Monte Carlo) is known to converge in root mean square error at a rate determined by the ratio s/d, where s and d encode the smoothness and dimension of the integrand. However, an empirical investigation re…
A new learning framework reduces PV-Battery system costs by 3.6%.
problem Optimizing PV-Battery systems for cost savings with accurate forecasts.
method Decision-focused learning framework integrating optimization and prediction.
result Decision-focused method reduces average electricity costs by 3.6%.
Study on theoretical limits of ℓ0 sparse-regression algorithms using Fl RDT.
problem Understanding the performance limits of ℓ0 norm based optimization algorithms in compressed sensing and sparse regression. method Utilized Fully lifted random duality theory (Fl RDT) to analyze the maximum-likelihood (ML) decoding performance.
result Uncovered phase-transition (PT) and descending ℓ0 (dℓ0) curves that separate successful and unsuccessful algorithm performance. Unified NICEk metrics improve solar forecasting accuracy.
problem Lack of suitable error metrics for multidimensional solar irradiance forecasting.
method Introducing NICEk framework with Lk norms for evaluating forecasting models.
result NICESigma consistently outperforms traditional metrics in discriminative power and statistical significance.
Enhanced ensemble filters use machine learning to improve accuracy in filtering models.
problem Accuracy limitations of traditional ensemble Kalman filters.
method Introduces a measure neural mapping (MNM) to map joint predicted state and observation to updated state estimates.
result Superior root-mean-square-error performance compared to leading methods in filtering models.
This review assesses deep-learning methods for complex sequential data.
problem Lack of robustness and transparency in deep-learning frameworks for irregular sequential data.
method Systematic literature review of existing algorithms.
result Recurrent neural networks dominate in performance evaluation of deep-learning frameworks.
RMSNorm simplifies LayerNorm, reducing computational cost.
problem Computational overhead of LayerNorm in deep neural networks.
method RMSNorm re-scales inputs using root mean square (RMS) instead of invariance.
result RMSNorm achieves comparable performance to LayerNorm but reduces running time.
Study finds price-based clustering outperforms AI and human methods in stock market analysis.
problem Investigates if AI can improve stock clustering compared to traditional methods.
method Compares price-based, human-informed, and AI-driven clustering methods using synthetic factor models.
result Price-based clustering reduces RMSE by 15.9% relative to GICS and 14.7% relative to LLM embeddings.
Study compares machine learning algorithms for predicting SST in the Great Barrier Reef.
problem Predicting sea surface temperature in the Great Barrier Reef region.
method Ridge regression, LASSO, Random Forest, and Extreme Gradient Boosting (XGBoost) algorithms were evaluated.
result XGBoost significantly outperforms other algorithms in terms of predictive accuracy and Kullback-Leibler Divergence.
Deep neural network predicts black hole merger remnants with high accuracy.
problem Estimating the properties of black hole merger remnants.
method Trained on a dataset of binary black hole simulations, a deep neural network predicts mass and spin with high precision.
result The network predicts remnant black hole mass and spin with errors less than 0.04% and 0.3% respectively, and reduces errors in precessing cases to half.
Paper proposes machine learning models for more accurate road inspection.
problem Traditional road inspection systems have safety, energy, and cost issues.
method Hybrid machine learning models using surface deflection data from FWD tests.
result CMIS model outperforms other models with APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.
A visualization aids in comparing regression models by highlighting errors and correlations.
problem Comparing regression models is difficult due to varying hyper-parameters and metrics.
method Introduces a novel visualization approach using 2D residual space, Mahalanobis distance, and colormaps.
result Enhanced understanding of regression model performance differences and error distributions.
This paper uses ML and EVT to analyze tree ring data, improving accuracy of predictions.
problem Analyzing tree ring data for climate modeling and historical studies.
method Combines machine learning algorithms with extreme value theory for data analysis.
result Random Forest method yields the most accurate results for tree ring data analysis.
Paper uses MLPNN for drought forecasting in Pakistan.
problem Early detection of drought hazards in Pakistan.
method Multilayer Perceptron Artificial Neural Network (MLPNN) model applied to SPEI data.
result MLPNN shows potential for SPEI drought forecasting.
The completion of low rank matrices from few entries is a task with many practical applications. We consider here two aspects of this problem: detectability, i.e. the ability to estimate the rank r reliably from the fewest possible random entries, and performance in achieving small reconstruction error. We propose a …
New loss function improves accuracy of MRI parameter estimation.
problem Systematic errors in parameter estimates at low SNR.
method Developed and implemented negative log Rician likelihood (NLR) loss.
result NLR loss shows higher accuracy in parameter estimation than MSE loss at low SNR.
Study evaluates different mathematical models for three case studies using statistical fitting.
problem Estimating outcomes in population dynamics, temperature variations, and market equilibrium.
method Applied various statistical equations (e.g., fractional exponential, sinusoidal) to three case studies.
result Optimal models differ by case study (fractional exponential for population dynamics, sinusoidal for temperature and market equilibrium).
LSTM outperforms traditional models in forecasting international migration.
problem Precise forecasting of international migration for policymaking.
method Replaced a gravity linear model with an LSTM approach using Google Trends data.
result LSTM approach combined with Google Trends data outperforms existing models.
The study examines generalization bounds for regression and classification tasks on adaptive input domains.
problem Understanding the generalization error in adaptive input domains for regression and classification.
method The analysis considers regression and classification separately, using Lipschitz continuity and 2-norm/0/1 loss for measurement. It also highlights the polynomial relationship between generalization bounds and network parameters.
result Generalization bounds for regression and classification are inversely proportional to a polynomial of the number of parameters, emphasizing the advantages of over-parameterized networks.
Study evaluates 41 ML models for Bitcoin trading performance.
problem Predicting Bitcoin prices for algorithmic trading.
method Examined 21 classifiers and 20 regressors under various market conditions.
result Certain models like Random Forest and Stochastic Gradient Descent outperform others in profit and risk management.
A new tamed stochastic gradient Hamiltonian Monte Carlo algorithm for superlinearly growing stochastic gradients.
problem Sampling and stochastic optimization problems with superlinearly growing stochastic gradients.
method Tamed Stochastic Gradient Hamiltonian Monte Carlo (tSGHMC) algorithm.
result Established a non-asymptotic error bound in Wasserstein-2 distance with a convergence rate of 1/4. We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our methods are simple and have a closed form to optimally rotate, translate, and scale to minimize root mean squared errors or maximize the averag…