Results 51 to 60 of about 2,611,781 (277)
SSRN Electronic JournalSocial science theories often postulate systems of causal relationships among variables, which are commonly represented using directed acyclic graphs (DAGs). As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships.
Sourabh Balgi +4 more
openaire +2 more sources
Holography as deep learning [PDF]
International Journal of Modern Physics D, 2017 Quantum many-body problem with exponentially large degrees of freedom can be reduced to a tractable computational form by neural network method [G. Carleo and M. Troyer, Science 355 (2017) 602, arXiv:1606.02318.] The power of deep neural network (DNN) based on deep learning is clarified by mapping it to renormalization group (RG), which may shed ...
Wen-Cong Gan, Fu-Wen Shu
openaire +3 more sources
FEBS Open Bio, EarlyView.This study presents a novel approach to teaching Python and bioinformatics using team‐based learning and cloud‐hosted notebooks. By integrating interactive coding into biomedical education, the method improves accessibility, student engagement, and confidence—especially for those without a computing background.
Nuno S. Osório, Leonardo D. Garma
wiley +1 more source
Deep Learning in Robotics: A Review of Recent Research
, 2017 Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least thirty papers published on the subject between 2014 and the present.
Gashler, Michael S., Pierson, Harry A.
core +1 more source
Deep Learning Volatility [PDF]
SSRN Electronic Journal, 2019 We present a neural network based calibration method that performs the calibration task within a few milliseconds for the full implied volatility surface. The framework is consistently applicable throughout a range of volatility models -including the rough volatility family- and a range of derivative contracts.
Mehdi Tomas +2 more
openaire +3 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT C‐truncating variants in the charged multivesicular body protein 2B (CHMP2B) gene are a rare cause of frontotemporal lobar degeneration (FTLD), previously identified only in Denmark, Belgium, and China. We report a novel CHMP2B splice‐site variant (c.35‐1G>A) associated with familial FTLD in Spain. The cases were two monozygotic male twins who
Sara Rubio‐Guerra +17 more
wiley +1 more source
Efficient Deep Feature Learning and Extraction via StochasticNets
, 2015 Deep neural networks are a powerful tool for feature learning and extraction given their ability to model high-level abstractions in highly complex data.
Fieguth, Paul +3 more
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Reliable biomarkers are essential for tracking disease progression and advancing treatments for multiple system atrophy (MSA). In this study, we propose the MSA Atrophy Index (MSA‐AI), a novel composite volumetric measure to distinguish MSA from related disorders and monitor disease progression. Methods Seventeen participants with an
Paula Trujillo +11 more
wiley +1 more source
Deep Ordinal Reinforcement Learning
, 2019 Reinforcement learning usually makes use of numerical rewards, which have nice properties but also come with drawbacks and difficulties. Using rewards on an ordinal scale (ordinal rewards) is an alternative to numerical rewards that has received more ...
C Wirth, CJ Watkins, RS Sutton, V Mnih
core +1 more source
2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC), 2020 Deep learning and other machine learning approaches are deployed to many systems related to Internet of Things or IoT. However, it faces challenges that adversaries can take loopholes to hack these systems through tampering history data. This paper first presents overall points of adversarial machine learning.
openaire +3 more sources