Results 131 to 140 of about 2,291,078 (312)
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering [PDF]
arXiv, 2021 Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition. However, we uncover a striking contrast to this promise: across 5 models and 4 datasets on the task of visual question
arxiv
Active Learning of Nondeterministic Finite State Machines [PDF]
Mathematical Problems in Engineering, 2013 We consider the problem of learning nondeterministic finite state machines (NFSMs) from systems where their internal structures are implicit and nondeterministic. Recently, an algorithm for inferring observable NFSMs (ONFSMs), which are the potentially learnable subclass of NFSMs, has been proposed based on the hypothesis that the complete testing ...
Warawoot Pacharoen +3 more
openaire +1 more source
Underwater Acoustic Research Trends with Machine Learning: Active SONAR Applications
한국해양공학회지, 2020 : Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources ...
Haesang Yang +4 more
semanticscholar +1 more source
FEBS Open Bio, EarlyView.The tumor microenvironment is a dynamic, multifaceted complex system of interdependent cellular, biochemical, and biophysical components. Three‐dimensional in vitro models of the tumor microenvironment enable a better understanding of these interactions and their impact on cancer progression and therapeutic resistance.
Salma T. Rafik +3 more
wiley +1 more source
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
, 2014 Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest.
Aparna Lakshmiratan +10 more
core
Modeling PROTAC degradation activity with machine learning
Artificial Intelligence in the Life Sciences13 pages, 10 ...
Stefano Ribes +3 more
openaire +3 more sources
Classifying Human Activities Using Machine Learning and Deep Learning Techniques
, 2023 Human Activity Recognition (HAR) describes the machines ability to recognize human actions. Nowadays, most people on earth are health conscious, so people are more interested in tracking their daily activities using Smartphones or Smart Watches, which can help them manage their daily routines in a healthy way.
Uday, Sanku Satya +3 more
openaire +2 more sources
FEBS Open Bio, EarlyView.We obtained potential bacterial laccase‐like multicopper oxidase (LMCO) sequences through metagenomic sequencing. All sequences exhibited significant differences from known LMCOs in databases. To select the most promising candidates, we performed structure prediction and molecular docking using alphafold2, metal3d and rosetta.
Ting Cui +5 more
wiley +1 more source
Active Learning: Problem Settings and Recent Developments [PDF]
arXiv, 2020 In supervised learning, acquiring labeled training data for a predictive model can be very costly, but acquiring a large amount of unlabeled data is often quite easy. Active learning is a method of obtaining predictive models with high precision at a limited cost through the adaptive selection of samples for labeling.
arxiv
A review of artificial intelligence in brachytherapy
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in various aspects of brachytherapy.
Jingchu Chen +4 more
wiley +1 more source