arXiv research

Deep learning transforms time series into images for anomaly detection in industrial assets.

This paper presents a brief introduction to the key points of the Grey Machine Learning (GML) based on the kernels. The general formulation of the grey system models have been firstly summarized, and then the nonlinear extension of the grey models have been developed also with general formulations. The kernel implicit …

Clustering analysis, a classical issue in data mining, is widely used in various research areas. This article aims at proposing a self-adaption grey DBSCAN clustering (SAG-DBSCAN) algorithm. First, the grey relational matrix is used to obtain the grey local density indicator, and then this indicator is applied to make …

Proposes a new model for equity options calibration.

A framework for analyzing regularizers to ensure trustworthy theory-driven model estimation.

Residual generation helps diagnose engine faults using neural networks.

Machine learning (ML) classifiers are vulnerable to adversarial examples. An adversarial example is an input sample which is slightly modified to induce misclassification in an ML classifier. In this work, we investigate white-box and grey-box evasion attacks to an ML-based malware detector and conduct performance eval…

A new kNN imputation method improves classification performance on datasets with missing data.

MOBONS optimizes complex systems with multi-objective Bayesian optimization.

BOIS optimizes complex systems by combining known and unknown functions, improving efficiency.

The main idea of this paper is to explore the possibilities of generating samples from the neural networks, mostly focusing on the colorization of the grey-scale images. I will compare the existing methods for colorization and explore the possibilities of using new generative modeling to the task of colorization. The c…

A tutorial on optimizing complex functions with partial knowledge.

Kernel-based machine learning approaches are gaining increasing interest for exploring and modeling large dataset in recent years. Gaussian process (GP) is one example of such kernel-based approaches, which can provide very good performance for nonlinear modeling problems. In this work, we first propose a grey-box mode…

Hybrid model combines physics and data to handle incomplete systems.

With the widespread engineering applications ranging from artificial intelligence and big data decision-making, originally a lot of tedious financial data processing, processing and analysis have become more and more convenient and effective. This paper aims to improve the accuracy of stock price forecasting. It improv…

Study uses machine learning to detect early COVID-19 from CT images.

We suggest an original physical approach to describe the mechanism of market pricing. The core of our approach is to consider pricing at different time scales separately, using independent equations of motion. Such an approach leads to a pricing model that not only allows estimating the volatility of future market pric…

Improved physics-integrated generative models with noise robustness and fidelity.

Dynamic risk assessment method for WUI fires improves upon static frameworks.

A new UNet variant reduces spectral artifacts in image transformations.

Deep learning methods have shown state of the art performance in a range of tasks from computer vision to natural language processing. However, it is well known that such systems are vulnerable to attackers who craft inputs in order to cause misclassification. The level of perturbation an attacker needs to introduce in…

Algorithm optimizes cascaded functions with known structure.

SFM resolves small-scale physics challenges in weather data.

Hybrid model outperforms benchmarks in financial forecasting.

Researchers disrupt Gaussian model inference to test adversarial attacks.

This study presents a new lossy image compression method that utilizes the multi-scale features of natural images. Our model consists of two networks: multi-scale lossy autoencoder and parallel multi-scale lossless coder. The multi-scale lossy autoencoder extracts the multi-scale image features to quantized variables a…

New methods merge discrete gradient fields from patches to correct errors.

This paper studies the prediction of chord progressions for jazz music by relying on machine learning models. The motivation of our study comes from the recent success of neural networks for performing automatic music composition. Although high accuracies are obtained in single-step prediction scenarios, most models fa…

In this work, we present a method to compute the Kantorovich-Wasserstein distance of order one between a pair of two-dimensional histograms. Recent works in Computer Vision and Machine Learning have shown the benefits of measuring Wasserstein distances of order one between histograms with $n$ bins, by solving a classic…

ETC improves Transformer models for long and structured inputs.

We compute the condition of minimality of a G-structure for the Gray-Hervella class $\mathcal{W}_4$ of almost hermitian manifolds and $\mathcal{C}_5$ class of almost contact metric structures. We also consider $\mathcal{C}_4$ class by comparison with the Grey-Hervella class $\mathcal{W}_4$. The common feature is the ex…

Enhances quantum machine learning models using Fock states.

The condition number predicts efficient information encoding in neural units, aiding model fine-tuning.

New subspace prototype flag median improves clustering on noisy data.

Novel neural network solves PDEs with multi-scale resolution.

CAFLOW uses auto-regressive flows to translate images efficiently.

Space2Vec learns multi-scale spatial representations from grid cell insights.

Object detection in point cloud data is one of the key components in computer vision systems, especially for autonomous driving applications. In this work, we present Voxel-FPN, a novel one-stage 3D object detector that utilizes raw data from LIDAR sensors only. The core framework consists of an encoder network and a c…

CNNs encode data augmentation transformations, especially in early layers.

Recurrent auto-encoder model summarises sequential data through an encoder structure into a fixed-length vector and then reconstructs the original sequence through the decoder structure. The summarised vector can be used to represent time series features. In this paper, we propose relaxing the dimensionality of the dec…

Randomized positional encodings boost transformer performance on longer sequences.

DRFormer uses dynamic tokenization and multi-scale transformer to forecast long time series.

Method analyzes large-scale network data to detect communication pattern shifts.

New estimator for digital options using path splitting and MLMC.

Regularized target encoding beats traditional methods for high cardinality features in ML.

Spectral analysis of neighborhood graphs is one of the most widely used techniques for exploratory data analysis, with applications ranging from machine learning to social sciences. In such applications, it is typical to first encode relationships between the data samples using an appropriate similarity function. Popul…

We propose a probabilistic graphical model realizing a minimal encoding of real variables dependencies based on possibly incomplete observation and an empirical cumulative distribution function per variable. The target application is a large scale partially observed system, like e.g. a traffic network, where a small pr…

We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a finite-dimensional one. Furthermore, rather than directly producing the representation, we learn a neural update rule resembling functional gra…