Results 81 to 90 of about 4,829,196 (302)

A De Novo Molecular Generation Method Using Latent Vector Based Generative Adversarial Network [PDF]

, 2019
Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network for de novo ...
Oleksii, Prykhodko, Esben Jannik, Bjerrum, Simon Viet, Johansson, Panagiotis-Christos, Kotsias, Ola, Engkvist, Josep, Arús-Pous, Hongming, Chen   +6 more
core   +1 more source

TVA-GAN: Attention Guided Generative Adversarial Network For Thermal To Visible Image Transformations

, 2021
In the recent advancement of machine learning methods for realistic image generation and image translation, Generative Adversarial Networks (GANs) play a vital role. GAN generates novel samples that look indistinguishable from the real images.
NAND YADAV (10577093), Shiv Ram Dubey (11505067), Satish Kumar Singh (11505064)   +2 more
core   +1 more source

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

Adversarial Learning in Accelerometer Based Transportation and Locomotion Mode Recognition

, 2022
This chapter demonstrates how adversarial learning can be used in the mobile computing domain. Specifically, we address the problem of improving the recognition of human activities from smartphone sensors, when limited training data is available ...
Wang, L, Günthermann, L, Philippides, A, Simpson, I, Roggen, D   +4 more
core   +1 more source

HGAN: Hyperbolic Generative Adversarial Network

IEEE Access, 2021
Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning have gained popularity because of their ability to represent hierarchical data.
Diego Lazcano, Nicolas Fredes Franco, Werner Creixell   +2 more
doaj   +1 more source

Generative Adversarial Networks Unlearning

IEEE Transactions on Dependable and Secure Computing
As machine learning continues to develop, and data misuse scandals become more prevalent, individuals are becoming increasingly concerned about their personal information and are advocating for the right to remove their data. Machine unlearning has emerged as a solution to erase training data from trained machine learning models. Despite its success in
Hui Sun, Tianqing Zhu, Wenhan Chang, Wanlei Zhou 0001   +3 more
openaire   +4 more sources

Solid Harmonic Wavelet Bispectrum for Image Analysis

Advanced Science, EarlyView.
The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown, Mathilda Avirett‐Mackenzie, Carolin Villforth, Georgios Exarchakis   +3 more
wiley   +1 more source

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

Advanced Science, EarlyView.
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang   +6 more
wiley   +1 more source

Overcoming Artificial Structures in Resolution‐Enhanced Hi‐C Data by Signal Decomposition and Multi‐Scale Attention

Advanced Science, EarlyView.
Deep‐learning‐based signal enhancement is an effective way to recover high‐resolution details from a low‐resolution chromatin contact map. However, due to computational challenges, existing methods commonly divide up the contact map into small patches and create artificial discontinuities at patch boundaries.
Qinyao Li, Kelly Yichen Li, Chiara Nicoletti, Stephen Kwok‐Wing Tsui, Pier Lorenzo Puri, Qin Cao, Kevin Y. Yip   +6 more
wiley   +1 more source

oswaldoludwig/Adversarial-Learning-for-Generative-Conversational-Agents: Adversarial Learning for Generative Conversational Agents

, 2017
<p>This repository presents a new adversarial learning method for generative conversational agents (GCA) besides a new model of GCA. Our method assumes the GCA as a generator that aims at fooling a discriminator that labels dialogues as human ...
Oswaldo Ludwig
core   +1 more source