Latest Articles for Machine Learning

Learn how existing optimization methods can handle approximate data effectively.

A new approach fine-tunes diffusion models for better user preference alignment.

A new method enhances prediction certainty in language models for yes/no questions.

KANs enhance image analysis and classification while using fewer resources.

M2QA enhances machine learning for questions in various languages and topics.

This article examines the limitations of in-context learning in large language models.

This article discusses optimization in Wasserstein space with a focus on complex probability measures.

Combining pruning and quantization streamlines DNN efficiency for smaller devices.

This study examines methods to enhance text classification using SVM and kernel functions.

This study examines how large language models handle fuzzy reasoning tasks.

Fine-tuning large language models directly on smartphones while protecting user data.

LatentDEM effectively tackles blind inverse problems in computer vision and graphics.

A new method enhances document-level relation extraction using efficient data selection.

EMDiffusion improves training of diffusion models using corrupted images.

Swish-T enhances model performance in deep learning through innovative activation techniques.

Improving quantum methods to solve the Poisson equation in fluid dynamics.

A new method enhances speed and accuracy in logistic regression predictions.

This article examines how small language models learn to handle noise in data.

Researchers enhance neutrino energy estimates using deep learning techniques.

New dataset improves audio generation from detailed text descriptions.

Study reveals machine learning models can enhance hospital discharge predictions.

Investigating how overparameterized models outperform underparameterized ones in learning features.

New TOKEN approach improves handling of rare driving events in autonomous vehicles.

New methods enhance image generation by aligning outputs with specific text descriptions.

An overview of how language models like Transformers operate and their significance.

Two new methods for efficient solutions in equations and inclusions using variance reduction.

Research reveals how neural networks struggle with generalization in quantum physics applications.

An analysis of RNN-TPPs and their impact on event prediction accuracy.

Examining the challenges in constructing data centers for training large language models.

This article discusses the strengths and weaknesses of Large Language Models.

We propose a method to enhance vision transformers' efficiency on edge devices.

A new method improves language model performance and efficiency.

Exploring differential encoding and its impact on graph learning models.

BrainMAE approach enhances analysis of brain data using fMRI.

EAGERx enhances robot learning by connecting simulations to real-world tasks.

A new method improves how LLMs handle structured data.

PWTO improves pathfinding for robots in complex environments.

STRIDE predicts lost variable names and types in decompiled software efficiently.

Mutual learning boosts performance and uncertainty estimation in Bayesian Neural Networks.

Enhancing speech synthesis for more natural and expressive voice generation.