Browsing: ML News
Advances in vision-language models (VLMs) have shown impressive common sense, reasoning, and generalization abilities. This means that developing a fully independent digital AI assistant, that can…
In the era of vast data, information retrieval is crucial for search engines, recommender systems, and any application that needs to find documents based on their…
Neural networks, despite their theoretical capability to fit training sets with as many samples as they have parameters, often fall short in practice due to limitations…
LLMs like ChatGPT and Gemini demonstrate impressive reasoning and answering capabilities but often produce “hallucinations,” meaning they generate false or unsupported information. This problem hampers their…
In recent years, ML algorithms have increasingly been recognized in ecological modeling, including predicting soil organic carbon (SOC). However, their application on smaller datasets typical of…
Meta’s Fundamental AI Research (FAIR) team has announced several significant advancements in artificial intelligence research, models, and datasets. These contributions, grounded in openness, collaboration, excellence, and…
Modern bioprocess development, driven by advanced analytical techniques, digitalization, and automation, generates extensive experimental data valuable for process optimization—ML methods to analyze these large datasets, enabling…
Machine learning methods, particularly deep neural networks (DNNs), are widely considered vulnerable to adversarial attacks. In image classification tasks, even tiny additive perturbations in the input…
Data curation is essential for developing high-quality training datasets for language models. This process includes techniques such as deduplication, filtering, and data mixing, which enhance the…
Transformer-based Large Language Models (LLMs) have emerged as the backbone of Natural Language Processing (NLP). These models have shown remarkable performance over a variety of NLP…