Deep learning has significantly advanced molecular modelling and design, enabling an efficient understanding and discovery of novel molecules. In particular, large language models introduce a fresh research paradigm to tackle scientific problems from a natural language processing perspective. Large language mo...
content typepaper|research areaComputer Vision,research areaData Science and Annotation|Published year2025 AuthorsTzu-Heng Huang, Manjot Bilkhu, Frederic Sala, Javier Movellan Grounding Multimodal Large Language Models in Actions content typepaper|research areaComputer Vision,research areaMethods and Algorithm...
Deep learning has transformed the use of machine learning technologies for the analysis of large experimental datasets. In science, such datasets are typically generated by large-scale experimental facilities, and machine learning focuses on the identifi
期刊名称:Computer Methods and Programs in Biomedicine 第一作者:Wenge Que 通讯作者:Wenge Que,Chuang Han,Xiliang Zhao,Li Shi 通讯单位:Tsinghua university,Zhengzhou University of Light Industry,Capital Medical University,Beijing National Research Center For Information Science And Technology DOI:10.1016/j....
(2008). Model based learning and instruction in science. Springer: USA. . DiSessa, A. (2013). A Bird's-Eye View of the "Pieces" vs. "Coherence" Controversy (from the "Pieces" Side of the Fence). In: Vosniadou, S. (Ed.). International Handbook of Research on Conceptual Change. ...
efficacy of S3GM has been verified on multiple dynamical systems with various synthetic, real-world and laboratory-test datasets (ranging from turbulent flow modelling to weather/climate forecasting). The results demonstrate the sound performance of S3GM in zero-shot reconstruction and prediction of ...
This research focuses on BioScientist, a digital game-based, inquiry-based learning program embedded in the biology curriculum that develops inquiry skills
This study was supported by the National Natural Science Foundation of China (Grant Nos. 41807192, 41790441), Innovation Capability Support Program of Shaanxi (Grant No. 2020KJXX-005), and Natural Science Basic Research Program of Shaanxi (Grant Nos. 2019JLM-7, 2019JQ-094).More...
Neural generative models can be used to learn complex probability distributions from data, to sample from them, and to produce probability density estimates. We propose a computational framework for developing neural generative models inspired by the the
utilization to those people or groups that combine expertise in medicine and data science. Automated machine learning (AutoML) is an established discipline that aims to make ML accessible to non-technical experts. In medicine, the principle feasibility and use of AutoML platforms, such as the ...