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Found 21 papers, 11 papers with code
Cryptocurrency Portfolio Optimization by Neural Networks
Many cryptocurrency brokers nowadays offer a variety of derivative assets that allow traders to perform hedging or speculation.
Recognition of Defective Mineral Wool Using Pruned ResNet Models
Considering the low quantity of the dataset, cross-validation and augmentation methods are used during the training.
Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification
In addition, as the market evolves through time, it is necessary to update the existing models or train new ones when new data is made available.
Variational Neural Networks
Bayesian Neural Networks (BNNs) provide a tool to estimate the uncertainty of a neural network by considering a distribution over weights and sampling different models for each input.
Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction
Financial time-series forecasting is one of the most challenging domains in the field of time-series analysis.
Remote Multilinear Compressive Learning with Adaptive Compression
By developing compressive sensing and learning models that can operate with an adaptive compression rate, we can maximize the informational content throughput of the whole application.
Bilinear Input Normalization for Neural Networks in Financial Forecasting
Data normalization is one of the most important preprocessing steps when building a machine learning model, especially when the model of interest is a deep neural network.
Knowledge Distillation By Sparse Representation Matching
Knowledge Distillation refers to a class of methods that transfers the knowledge from a teacher network to a student network.
Performance Indicator in Multilinear Compressive Learning
Recently, the Multilinear Compressive Learning (MCL) framework was proposed to efficiently optimize the sensing and learning steps when working with multidimensional signals, i. e. tensors.
Attention-based Neural Bag-of-Features Learning for Sequence Data
In this paper, we propose 2D-Attention (2DA), a generic attention formulation for sequence data, which acts as a complementary computation block that can detect and focus on relevant sources of information for the given learning objective.
Subset Sampling For Progressive Neural Network Learning
Progressive Neural Network Learning is a class of algorithms that incrementally construct the network's topology and optimize its parameters based on the training data.
Multilinear Compressive Learning with Prior Knowledge
Extensive experiments demonstrate that the proposed knowledge transfer method can effectively train MCL models to compressively sense and synthesize better features for the learning tasks with improved performances, especially when the complexity of the learning task increases.
Multilinear Compressive Learning
Compressive Learning is an emerging topic that combines signal acquisition via compressive sensing and machine learning to perform inference tasks directly on a small number of measurements.
Temporal Attention Augmented Bilinear Network for Financial Time Series Data Analysis
Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market.
Data-driven Neural Architecture Learning For Financial Time-series Forecasting
Forecasting based on financial time-series is a challenging task since most real-world data exhibits nonstationary property and nonlinear dependencies.
Progressive Operational Perceptron with Memory
Generalized Operational Perceptron (GOP) was proposed to generalize the linear neuron model in the traditional Multilayer Perceptron (MLP) and this model can mimic the synaptic connections of the biological neurons that have nonlinear neurochemical behaviours.
Heterogeneous Multilayer Generalized Operational Perceptron
Previously, Generalized Operational Perceptron (GOP) was proposed to extend conventional perceptron model by defining a diverse set of neuronal activities to imitate a generalized model of biological neurons.
Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis
Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market.
Multilinear Class-Specific Discriminant Analysis
There has been a great effort to transfer linear discriminant techniques that operate on vector data to high-order data, generally referred to as Multilinear Discriminant Analysis (MDA) techniques.
Improving Efficiency in Convolutional Neural Network with Multilinear Filters
The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices.
Tensor Representation in High-Frequency Financial Data for Price Change Prediction
Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders.
