This repository contains the implementation of the paper "Maximum Entropy Deep Inverse Reinforcement Learning" by Wulfmeier et al. [1] in PyTorch. You will also find in the notebooks directory a ...

Introduction: As the core equipment in industrial production, rotating machinery bearings play a critical role. However, traditional feature extraction algorithms for vibration signals are susceptible ...

Multielement high-entropy carbides (HECs) provide many opportunities for HECs to obtain optimal combinations of various properties, e.g., high strength and high flexibility, leading to high toughness.

This study evaluates the predictive performance of traditional and machine learning-based models in forecasting NFL team winning percentages over a 21-season dataset (2003–2023). Specifically, we ...

Large Language Models (LLMs) generate step-by-step responses known as Chain-of-Thoughts (CoTs), where each token contributes to a coherent and logical narrative. To improve the quality of reasoning, ...

Abstract: Bipolar disorder (BD) is a common psychiatric condition with varying severity, characterized by alternating episodes of mania or hypomania and depression, making accurate and timely ...

Advancements in large language models (LLMs) have revolutionized natural language processing, with applications spanning text generation, translation, and summarization. These models rely on large ...

ABSTRACT: Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach ...

ABSTRACT: This study evaluates the performance and reliability of a vision transformer (ViT) compared to convolutional neural networks (CNNs) using the ResNet50 model in classifying lung cancer from ...