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GloNets: Globally Connected Neural Networks
Deep learning architectures suffer from depth-related performance degradation, limiting the effective depth of neural networks. Approaches like...
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Data Quality in NLP: Metrics and a Comprehensive Taxonomy
Data quality is a crucial factor for the success of natural language processing (NLP) models. However, there is a lack of a standard taxonomy for...
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An Interpretable Human-in-the-Loop Process to Improve Medical Image Classification
Medical imaging classification improves patient prognoses by providing information on disease assessment, staging, and treatment response. The high...
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Node Classification in Random Trees
We propose a method for the classification of objects that are structured as random trees. Our aim is to model a distribution over the node label...
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\(\lambda \) -DBSCAN: Augmenting DBSCAN with Prior Knowledge
State-of-the-art density based cluster algorithms offer remarkable speed and robustness. However, they do not allow the user to make local changes...
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Elimination of Optical Distortions Arising from In Vivo Investigation of the Mouse Brain
An algorithm is proposed for eliminating refractive distortions caused by the oscillating surface of a liquid when studying the brain of a live...
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Comparative Analysis of Fuzzy Controllers in a Truck Cruise Control System
The paper considers the problem of improving the cruise control system used to control the speed of a truck. It is proposed to supplement the...
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Identifying Player Roles in Ice Hockey
Understanding the role of a particular player, or set of players, in a team is an important tool for players, scouts, and managers, as it can improve...
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ETSY: A Rule-Based Approach to Event and Tracking Data SYnchronization
Event data, which records high-level semantic events (e.g., passes), and tracking data, which records positional information for all players, are the...
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Mind the Data, Measuring the Performance Gap Between Tree Ensembles and Deep Learning on Tabular Data
Recent machine learning studies on tabular data show that ensembles of decision tree models are more efficient and performant than deep learning...
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Efficient Lookahead Decision Trees
Conventionally, decision trees are learned using a greedy approach, beginning at the root and moving toward the leaves. At each internal node, the...
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Unsupervised Representation Learning for Smart Transportation
In the automotive industry, sensors collect data that contain valuable driving information. The collected datasets are in multivariate time series...
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Predicting the Failure of Component X in the Scania Dataset with Graph Neural Networks
We use Graph Neural Networks on signature-augmented graphs derived from time series for Predictive Maintenance. With this technique, we propose a...
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Empirical Comparison Between Cross-Validation and Mutation-Validation in Model Selection
Mutation validation (MV) is a recently proposed approach for model selection, garnering significant interest due to its unique characteristics and...
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Machine Learning and Data Mining
The article discusses the main tasks of machine learning. The functional structure of a computer algorithm for solving machine learning problems and...
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Implementing a Jenkins Plugin to Visualize Continuous Integration Pipelines
The paper is devoted to visualization of continuous integration pipelines of the Jenkins system. When working with the Jenkins system additional...
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Diagnostics of Animals Diseases Based on the Principles of Neutrosophic Sets and Sugeno Fuzzy Inference
This study is devoted to the development of improved methods for diagnosing cattle diseases based on the principles of neutrosophic sets and Sugeno...
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Development of Automation and Control System of Waste Gas Production Process Based on Information Technology
Achieving energy and resource efficiency through the application of modern information technologies, calculation algorithms, automatic management and...
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A Deep Learning Approach for Selective Relevance Feedback
Pseudo-relevance feedback (PRF) can enhance average retrieval effectiveness over a sufficiently large number of queries. However, PRF often...
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Large Language Models are Zero-Shot Rankers for Recommender Systems
Recently, large language models (LLMs) (e.g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to...