Paper proposes FOFE for efficient WSD.
problem Word sense disambiguation (WSD) problem.
method Fixed-size ordinally forgetting encoding (FOFE) combined with FFNN.
result FOFE-based FFNN achieves comparable performance to state-of-the-art at lower cost.
Investment decisions can benefit from incorporating an accumulated knowledge of the past to drive future decision making. We introduce Continual Learning Augmentation (CLA) which is based on an explicit memory structure and a feed forward neural network (FFNN) base model and used to drive long term financial investment…
This study examines how randomness affects machine learning model performance.
problem The impact of randomness on machine learning model performance.
method Empirical study comparing randomness in model training and dataset partitioning.
result Randomness in model training causes more variation in FFNNs than tree-based methods.
This study compares machine learning algorithms for predictive performance and interpretability.
problem Comparing supervised machine learning algorithms for predictive ability and model interpretation.
method Simulation studies of extreme gradient boosting machines, random forests, and feedforward neural networks on structured data.
result Feedforward neural networks generally outperform tree-based boosting algorithms in smooth models, while tree-based models perform better in non-smooth models.
LSTMs fail in financial tasks, CL improves performance.
problem Financial time-series analysis and inference with LSTMs.
method Continual Learning (CL) approach for financial decision making.
result CL outperforms LSTMs and FFNN in financial decision making.
Study compares machine learning models for insurance pricing, including neural networks and GLMs.
problem Improving insurance pricing models using machine learning techniques.
method Benchmark study using four insurance datasets, comparing GLMs, GBM, FFNN, and CANN.
result CANNs provide better performance than GLMs and GBM, especially for frequency and severity modeling.
We propose an autoencoding sequence-based transceiver for communication over dispersive channels with intensity modulation and direct detection (IM/DD), designed as a bidirectional deep recurrent neural network (BRNN). The receiver uses a sliding window technique to allow for efficient data stream estimation. We find t…
FourNet approximates financial transition densities using Fourier transforms.
problem Approximating transition densities in finance with high accuracy.
method FourNet is a novel FFNN with Gaussian activation, learning from characteristic functions.
result FourNet can approximate transition densities arbitrarily well with finite neurons.
Measures neural network complexity via effective degrees of freedom.
problem Challenges in quantifying neural network complexity.
method Adapts generalized degrees of freedom (GDF) for binary outcomes and compares with cross-validation and null degrees of freedom.
result GDF provides a robust measure of model complexity for neural networks.
This paper presents a novel Inter Catchment Wastewater Transfer (ICWT) method for mitigating sewer overflow. The ICWT aims at balancing the spatial mismatch of sewer flow and treatment capacity of Wastewater Treatment Plant (WWTP), through collaborative operation of sewer system facilities. Using a hydraulic model, the…
LIFE framework improves model accuracy and interpretability.
problem Achieving high prediction accuracy and interpretability in neural networks.
method Three-step process: subset definition, feature creation, and linear model combination.
result LIFE consistently outperforms other models in prediction accuracy and interpretability.
Deep learning models predict solar irradiance for short-term forecasts.
problem Accurate prediction of solar irradiance for renewable energy integration.
method Sequence-to-sequence LSTM models for GHI forecasting, incorporating spatial-temporal features.
result LSTM models outperform traditional techniques in short-term GHI forecasting.
Paper predicts M&A deal success using ML and DL techniques.
problem Predicting the success of M&A deals to avoid costly mistakes.
method Data preprocessing with ML techniques, feedforward neural networks, and sentiment scores integration.
result Methodology outperforms benchmark models in preliminary tests.
Echo state network (ESN) is viewed as a temporal non-orthogonal expansion with pseudo-random parameters. Such expansions naturally give rise to regressors of various relevance to a teacher output. We illustrate that often only a certain amount of the generated echo-regressors effectively explain the variance of the tea…
MTL improves multi-dimensional regression in luminescence sensing.
problem Challenges in modeling multi-dimensional regression problems with classical methods.
method Multi-task learning (MTL) with feed-forward neural networks (FFNNs).
result MTL allows predicting multiple parameters from a single set of measurements.
Develops AMITE for analyzing neural network nonlinearities.
problem Addressing difficulties in verification, explainability, and security in neural network analysis.
method Analytically modified integral transform expansion (AMITE) for neural network nonlinearities.
result First to provide six mutually exclusive desired expansion properties.
Activities in reinforcement learning (RL) revolve around learning the Markov decision process (MDP) model, in particular, the following parameters: state values, V; state-action values, Q; and policy, pi. These parameters are commonly implemented as an array. Scaling up the problem means scaling up the size of the arra…