Results 91 to 100 of about 212,177 (318)

A diffusion generative model on atom distances for de novo molecular design [PDF]

openThe use of artificial intelligence is revolutionising almost every aspect of our lives; among the most impactful advances in AI, generative models are getting days by days more important thanks to their abilities in generating text, video, images and
BALLARINI, MARCO
core  

Multimodal Image Translation Algorithm Based on Singular Squeeze-and-Excitation Network

Mathematics
Image-to-image translation methods have advanced from focusing on image-level info to incorporating pixel-level and instance-level details. However, with feature-level constraint, deviation occurs when the network overemphasizes convolutional features ...
Hangyao Tu, Zheng Wang, Yanwei Zhao
doaj   +1 more source

Generative Modeling: A Review

CoRR
Generative methods (Gen-AI) are reviewed with a particular goal of solving tasks in machine learning and Bayesian inference. Generative models require one to simulate a large training dataset and to use deep neural networks to solve a supervised learning problem.
Nareklishvili, Maria, Polson, Nick, Sokolov, Vadim   +2 more
openaire   +2 more sources

ASSIST: Refinement of a Benefits Navigator Intervention Among Low‐Income Pediatric Oncology Families

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background/Objectives Children with cancer living in poverty experience worse survival and quality of life. Interventions connecting low‐income families to benefits (e.g., Supplemental Nutrition Assistance Program [SNAP] improve health outcomes; yet nearly 50% of SNAP‐eligible pediatric oncology families are unenrolled.
Puja J. Umaretiya, Selena Murcia Mendez, Jennifer Kellett, Sarah Wright, Marisel Ponton, Robin T. Higashi, Rahela Aziz‐Bose, Colleen A. Kelly, Ayesha Zia, Joanne Wolfe, Sandi L. Pruitt, Kira Bona   +11 more
wiley   +1 more source

Scalable Text Mining with Sparse Generative Models [PDF]

, 2015
The information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers.
Puurula, Antti
core  

Fault Detection for Power Batteries Using a Generative Adversarial Network with a Convolutional Long Short-Term Memory (GAN-CNN-LSTM) Hybrid Model

Applied Sciences
With the rapid proliferation of new energy vehicles, the safety of power batteries has attracted increasing attention. As a crucial approach to ensuring system stability, fault detection has become a research focus.
Shaofan Liu, Tianbao Xie, Yanxin Li, Siyu Liu   +3 more
doaj   +1 more source

Misperception of Body Weight After Childhood Cancer

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Misperception of body weight can negatively impact the weight management efforts of childhood cancer survivors (CCSs). Both being overweight or underweight are associated with chronic health conditions commonly observed in CCS; therefore, accurate weight perception is critical for reducing long‐term health risks.
Fabiën N. Belle, Tomáš Sláma, Eva Haegler‐Laube, Reto D. Kurmann, Gabriela M. Kuster, Christoph Saner, Eva Scheler, Yara Shoman, Christina Schindera   +8 more
wiley   +1 more source

Emergence of Object Segmentation in Perturbed Generative Models [PDF]

, 2019
We introduce a framework to learn object segmentation from a collection of images without any manual annotation. We build on the observation that the location of object segments can be perturbed locally relative to a given background without affecting ...
Favaro, Paolo, Bielski, Adam
core  

Rewriting a Deep Generative Model [PDF]

, 2020
© 2020, Springer Nature Switzerland AG. A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are encoded in the network, or how a ...
Antonio Torralba, Torralba, A, Jun-Yan Zhu, Zhu, JY, David Bau, Bau, D, Tongzhou Wang, Liu, S, Steven Liu, Wang, T   +9 more
core   +1 more source

A generative model of electrophysiological brain responses to stimulation

eLife
Each brain response to a stimulus is, to a large extent, unique. However this variability, our perceptual experience feels stable. Standard decoding models, which utilise information across several areas to tap into stimuli representation and processing,
Diego Vidaurre
doaj   +1 more source