Results 81 to 90 of about 236,656 (279)

Probabilistic Safety for Bayesian Neural Networks

, 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.
Wicker, M, Laurenti, L, Patane, A, Kwiatkowska, M   +3 more
openaire   +3 more sources

SKOOTS: Skeleton‐Oriented Object Segmentation for Mitochondria in High‐Resolution Cochlear EM Datasets

Advanced Science, EarlyView.
Skeleton‐oriented object segmentation (SKOOTS) introduces a new strategy for 3D mitochondrial instance segmentation by predicting explicit skeletons rather than relying on boundary cues. This approach enables robust analysis of densely packed organelles in large FIB‐SEM datasets.
Christopher J. Buswinka, Richard T. Osgood, Hidetomi Nitta, Artur A. Indzhykulian   +3 more
wiley   +1 more source

Spintronic Bayesian Hardware Driven by Stochastic Magnetic Domain Wall Dynamics

Advanced Science, EarlyView.
Magnetic Probabilistic Computing (MPC) utilizes intrinsic stochastic dynamics in domain walls to establish a hardware foundation for uncertainty‐aware artificial intelligence. Thermally driven domain‐wall fluctuations, voltage‐controlled magnetic anisotropy, and TMR readout enable fully electrical, tunable probabilistic inference.
Tianyi Wang, Bingqian Dai, Shijie Xu, Kin Wong, Yaochen Li, Yang Cheng, Qingyuan Shu, Haoran He, Puyang Huang, Hanshen Huang, Xixiang Zhang, Kang L. Wang   +11 more
wiley   +1 more source

Applying Bayesian Regularization for Acceleration of Levenberg-Marquardt based Neural Network Training

International Journal of Interactive Multimedia and Artificial Intelligence, 2018
Neural network is widely used for image classification problems, and is proven to be effective with high successful rate. However one of its main challenges is the significant amount of time it takes to train the network.
Azizah Suliman, Batyrkhan Omarov
doaj   +1 more source

Flat Seeking Bayesian Neural Networks

, 2023
Bayesian Neural Networks (BNNs) provide a probabilistic interpretation for deep learning models by imposing a prior distribution over model parameters and inferring a posterior distribution based on observed data. The model sampled from the posterior distribution can be used for providing ensemble predictions and quantifying prediction uncertainty.
Nguyen, Van-Anh, Vuong, Tung-Long, Phan, Hoang, Do, Thanh-Toan, Phung, Dinh, Le, Trung   +5 more
openaire   +2 more sources

SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics

Advanced Science, EarlyView.
ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao, Gaiqin Liu, Jingyi Xia, Runle Chen, Tong Wang, Xiaoling Yang, Ruiling Fang, Yanhong Luo, Ping Zeng, Hongmei Yu, Yanbo Zhang, Yuehua Cui   +11 more
wiley   +1 more source

BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA

Barekeng
Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form.
Suci Astutik, Nur Silviyah Rahmi, Diego Irsandy, Fang You Dwi Ayu Shalu Saniyawati, Fidia Raaihatul Mashfia, Evelin Dewi Lusiana, Intan Fadhila Risda, Mohammad Hilmi Susanto   +7 more
doaj   +1 more source

Financial Computational Intelligence [PDF]

Artificial intelligence decision support system is always a popular topic in providing the human with an optimized decision recommendation when operating under uncertainty in complex environments.
Chiu-Che Tseng, Yu-Chieh Lin
core  

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods

, 2019
We formulate the problem of neural network optimization as Bayesian filtering, where the observations are the backpropagated gradients. While neural network optimization has previously been studied using natural gradient methods which are closely related
Aitchison, Laurence
core