Interpretable machine learning as a tool for scientific discovery in chemistry. New J. Chem. 44, 20914–20920 (2020). Article CAS Google Scholar Rothenberg, G. Data mining in catalysis: separating knowledge from garbage. Catal. Today 137, 2–10 (2008). Article CAS Google Scholar Janet, ...
Machine learning techniques as a tool for predicting overtourism : The case of Spainearly warning systemhypothesis testingmachine learningovertourismpredictionOne of the most challenging tasks for tourism scientists is the prediction of potential overtourism situations in the tourist destinations. Until now,...
Model: Also known as “hypothesis”, a machine learning model is the mathematical representation of a real-world process. A machine learning algorithm along with the training data builds a machine learning model. Feature: A feature is a measurable property or parameter of the data-set. Feature ...
CNTK - The Computational Network Toolkit (CNTK) by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. CUDA - This is a fast C++/CUDA implementation of convolutional [DEEP LEARNING] DeepDetect - A machine...
learning to reweight examples for robust deep learning [Paper] robust learning under uncertain test distributions: relating ... [Paper] robust reinforcement learning: a constrained game ... [Paper] overfitting in adversarially robust deep learning [Paper] robust deep learning as optimal control...
Machine Learning All research Accelerating LLM Inference on NVIDIA GPUs with ReDrafter content typehighlight|Published year2024 ARMADA: Augmented Reality for Robot Manipulation and Robot-Free Data Acquisition content typepaper|research areaData Science and Annotation,research areaHuman-Computer Interaction|Pub...
deep learning pipeline for accurate and rapid de novo RNA 3D structure prediction, demonstrating strong accuracy in modeling single-stranded RNAs and excellent generalization across RNA families and types while also being capable of capturing local features such as interhelical angles and secondary ...
a central particle with the potential application as ultra-high density memory elements for information storage) has been studied by applying nonlinear machine learning to Brownian dynamics simulations [276]. By integrating experimental particle tracking technology with sophisticated machine learning tools, ...
(BAMs) with machine learning for predictive analysis. This platform enables evaluation of compounds’ mechanisms of action and potential therapeutic uses based on information-rich BAMs derived from drug-treated zebrafish larvae. From a screen of clinically used drugs, we found intrinsically coherent ...
people have said it’s just another way of saying machine learning, but you characterize it as, basically, the next new field for both programming and machine learning. So, tell us, how is machine teaching a new paradigm and what’s so different about it that it qualifies as a new ...