Browsing: ML News
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…
Machine learning has seen significant advancements in integrating Bayesian approaches and active learning methods. Two notable research papers contribute to this development: “Bayesian vs. PAC-Bayesian Deep…
Data-driven methods that convert offline datasets of prior experiences into policies are a key way to solve control problems in various fields. There are mainly two…
Topological Deep Learning (TDL) advances beyond traditional GNNs by modeling complex multi-way relationships, unlike GNNs that only capture pairwise interactions. This capability is critical for understanding…
Navigating the Challenges of Selective Classification Under Differential Privacy: An Empirical Study
In machine learning, differential privacy (DP) and selective classification (SC) are essential for safeguarding sensitive data. DP adds noise to preserve individual privacy while maintaining data…