Contemporary machine learning requires training large neural networks on massive datasets and thus faces the challenges of high computational demands. Dataset distillation, as a recent emerging strategy, aims to compress real-world datasets for efficient training. However, this line of research currently ...
Machine learning & AI New technique reduces bias in AI models while preserving or improving accuracy Machine-learning models can fail when they try to make predictions for individuals who were underrepresented in the datasets they were trained on. Dec 11, 2024 0 18 Computer Sciences Enabling...
machine-learning model Date Rank LiveRank Popular Machine learning & AI New technique reduces bias in AI models while preserving or improving accuracy Machine-learning models can fail when they try to make predictions for individuals who were underrepresented in the datasets they were trained on....
Processor:Minimum 1 GHz Processor (2.4 GHz recommended) RAM:4GB (8GB or more recommended) Free Hard Disk Space:2GB or more is recommended Conclusion EZ Machine Learning is an excellent choice for anyone looking to delve into machine learning without the complexity of traditional tools. It offers...
As machine learning models are becoming mainstream tools for molecular and materials research, there is an urgent need to improve the nature, quality, and accessibility of atomistic data. In turn, there are opportunities for a new generation of generally applicable datasets and distillable models. ...
Machine Learning Engineering for Production (MLOps) Specialization course on MLOPs by Andrew Ng. 🔗 Link to Lectures MIT Introduction to Data-Centric AI Covers the emerging science of Data-Centric AI (DCAI) that studies techniques to improve datasets, which is often the best way to improve ...
In this paper, we introduce Linked Papers With Code (LPWC), an RDF knowledge graph that provides comprehensive, current information about almost 400,000 machine learning publications. This includes the tasks addressed, the datasets utilized, the methods implemented, and the evaluations conducted, ...
SDG refers to the process of creating datasets that can be used for a variety of model customizations, from SFT, PEFT includingLow-Rank Adaptation (LoRA), and model alignment (using methods likeRLAIF,DPO, and so on). Use cases for SDG are not limited to model alignment but can apply to...
Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have multiple applications, for example, in the improvement of data mining algorithms. ...
:sparkles::sparkles:Latest Papers and Datasets on Multimodal Large Language Models, and Their Evaluation. - mbrukman/Awesome-Multimodal-Large-Language-Models