arXiv research

Study shows non-systematic bias in customer satisfaction surveys limits data value.

This paper surveys enterprise financial risk analysis from Big Data and LLMs perspectives.

Survey of diffusion models for time series forecasting.

Systematic review finds reinforcement learning enhances financial tech performance.

Survey of integrating planning and learning in model-based reinforcement learning.

Survey on geometric properties of special minimal surfaces.

Survey on rational curves on complex surfaces, highlighting different approaches.

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computa…

We present a Bayesian framework for estimating the customer lifetime value (CLV) and the customer equity (CE) based on the purchasing behavior deducible from the market surveys on customer purchasing behavior. The proposed framework systematically addresses the challenges faced when the future value of customers is est…

Survey of methods to incorporate external knowledge into stock price prediction.

Explanations in Machine Learning come in many forms, but a consensus regarding their desired properties is yet to emerge. In this paper we introduce a taxonomy and a set of descriptors that can be used to characterise and systematically assess explainable systems along five key dimensions: functional, operational, usab…

Survey of Willmore surfaces in spheres using DPW method.

Survey on modeling event sequences through temporal processes.

Survey of deep learning methods for medical anomaly detection.

Discrete differential geometry aims to develop discrete equivalents of the geometric notions and methods of classical differential geometry. In this survey we discuss the following two fundamental Discretization Principles: the transformation group principle (smooth geometric objects and their discretizations are invar…

Survey on automating geometry problem solving with large models.

Survey of AI in finance covering models, strategies, and knowledge systems.

Survey examines agentic AI in finance, highlighting its autonomy and challenges.

This paper reviews deep time-series forecasting focusing on autocorrelation modeling.

Interactive Machine Learning (IML) is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to effectively solve a wide variety of real-world application problems. Although recent years have witnessed the proliferation of IML in the fiel…

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prosp…

Survey tackles challenges in neural architecture design.

Survey explores how transfer learning improves deep reinforcement learning.

Survey classifies Clustered Federated Learning into three types of approaches.

This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack (PHY, MAC and network). First, the related work and paper contributions are discussed…

Survey on negative transfer in machine learning.

The present volume is the written version of the series of lectures the author delivered at the Catholic University of Leuven, Belgium during the period of June-July, 1990. The main purpose of these talks is to present some of author's work and also his joint works with Professor T. Nagano and Professor Y. Tazawa of Ja…

Survey of yield farming protocols in DeFi.

The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning…

Transformers improve time series modeling by capturing long-range dependencies.

Survey on computational models in dynamical systems, including new universality concepts.

Survey of reinforcement learning for sustainable energy challenges.

Survey of GANs challenges and solutions for better model design and optimization.

Survey on reproducibility and distortion issues in text clustering and topic modeling.

This paper surveys large-scale machine learning methods for efficient data analysis.

Spatiotemporal systems are common in the real-world. Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem. Although lots of real-world problems can be viewed as STSF and many research wo…

This paper surveys various data balancing methods for imbalanced datasets.

Survey compares methods for generating artificial outliers.

Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been devoted to a…

Survey on deep learning robust training methods for noisy labels.

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as …

Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible. Not only does this dramatically speed up and …

Survey of RL methods for control systems with time delays.

This paper surveys gradient-based multi-objective deep learning methods.

This paper surveys UQ methods for deep learning.

A new era in radioastronomy will begin with the upcoming large-scale surveys planned at the Australian Square Kilometre Array Pathfinder (ASKAP). ASKAP started its Early Science program in October 2017 and several target fields were observed during the array commissioning phase. The SCORPIO field was the first observed…

This paper surveys scalable automated alignment methods for LLMs.

Artificial neural networks (ANNs) have achieved significant success in tackling classical and modern machine learning problems. As learning problems grow in scale and complexity, and expand into multi-disciplinary territory, a more modular approach for scaling ANNs will be needed. Modular neural networks (MNNs) are neu…