Latest Articles for Machine Learning

CRACKS dataset leverages crowdsourced data for improved fault analysis.

Assessing deep learning models for practical use is crucial for safety and reliability.

Researchers combine machine learning and DFT to discover efficient thermoelectric materials.

A new method enhances GAN performance with limited data.

A study on the effectiveness of clustering methods in various scenarios.

Combining traditional and modern methods for improved search accuracy.

A new benchmark assesses LLMs’ ability to generate Verilog code.

FLAME improves navigation by combining language and visual input in urban settings.

A new method improves the analysis of single-cell RNA sequencing data.

GraphFSA enhances machine learning by applying finite state automata to graph structures.

Blending text and images for efficient AI training.

This study examines how stock networks can improve price predictions.

A method to speed up Bayesian inference in huge datasets using subsampling.

A new method improves accuracy in recognizing Bengali handwritten text.

Two new methods enhance data augmentation for time series forecasting.

Analyzing the role of Reinforcement Learning in transforming financial decision-making.

An in-depth look at Gated Recurrent Units in sequence learning.

ShapeSplat dataset enhances 3D understanding through labeled Gaussian objects.

ARIO standardizes data to improve robot training and adaptability.

Research shows people prefer larger bounding boxes in object detection.

A new model improves image quality in low-dose CT scans.

Explore how randomization enhances algorithm design and performance.

Exploring how language models handle multiple languages and cultural contexts.

New methods enhance small models' accuracy in telecommunications question answering.

This method improves 3D pose accuracy from 2D images using a transformer network.

iDLR predicts heliostat shapes using sunlight images, enhancing solar power efficiency.

AutoAugOOD improves novelty detection methods for various data scenarios.

This paper examines the depth and noise effects in poly-GNNs for semi-supervised learning.

This study examines the reliability of rationalization models under adversarial attacks.

Simplifying object detection while increasing accuracy and efficiency.

Otter improves LLMs with efficient parameter insertion, enhancing performance while saving resources.

A study on discovering galaxy-scale lenses using telescope data.

Explore methods to effectively fill in missing data in large matrices.

A new method to generate unbiased synthetic data for AI applications.

Exploring security measures in hierarchical federated learning against various attacks.

A new method improves gas measurement accuracy using machine learning and physics.

Discover the new Morse Model-based RL approach for effective decision-making.

A study on identifying causal relationships amidst hidden variables and measurement errors.

LFIT2 offers a refined approach to learning logic from dynamic systems.

A method to improve how confident language models are in their text generation.