Results 81 to 90 of about 3,905,952 (289)

Bayesian Neural Networks: An Introduction and Survey [PDF]

, 2020
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of computer vision, speech recognition and natural language processing. Despite their success, they are often implemented in a frequentist scheme, meaning they are unable to reason ...
Goan, Ethan, Fookes, Clinton
openaire   +3 more sources

Forecasting cyanobacteria with Bayesian and deterministic artificial neural networks

, 2006
Copyright © 2006 IEEECyanobacteria blooms are a major water quality problem in the River Murray and models are needed In provide warnings of such blooms and to investigate the response of cyanobacteria to different management strategies.
G.B. Kingston, Kingston, G., Maier, H., Lambert, M., H.R. Maier, M.F. Lambert   +5 more
core   +1 more source

Analyzing the Sensitivity of Deep Neural Networks for Sentiment Analysis: A Scoring Approach

, 2020
Part of IEEE WCCI 2020 is the world’s largest technical event on computational intelligence, featuring the three flagship conferences of the IEEE Computational Intelligence Society (CIS) under one roof: The 2020 International Joint Conference on Neural ...
Wei Emma Zhang, Sheng, Q.Z., Abdulwahab Aljubairy, Alhazmi, A., Ahoud Alhazmi, Quan Z Sheng, Zhang, W.E., Aljubairy, A.   +7 more
core   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Stochastic Control for Bayesian Neural Network Training

Entropy, 2022
In this paper, we propose to leverage the Bayesian uncertainty information encoded in parameter distributions to inform the learning procedure for Bayesian models.
Ludwig Winkler, César Ojeda, Manfred Opper   +2 more
doaj   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Probabilistic Safety for Bayesian Neural Networks

CoRR, 2020
We study probabilistic safety for BayesianNeural Networks (BNNs) under adversarial in-put perturbations. Given a compact set of input points,T⊆Rm, we study the probability w.r.t. the BNN posterior that all the pointsinTare mapped to the same region S in theoutput space.
Matthew Wicker, Luca Laurenti, Andrea Patane, Marta Kwiatkowska   +3 more
openaire   +5 more sources

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Causal pattern inference from neural spike train data [PDF]

, 2010
Electrophysiological recordings are a valuable tool for neuroscience in order to monitor the activity of multiple or even single neurons. Significant insights into the nervous system have been gained by analyses of resulting data; in particular, many ...
Echtermeyer, Christoph
core   +2 more sources

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source