Results 91 to 100 of about 119,888 (330)

AutomataGPT: Transformer‐Based Forecasting and Ruleset Inference for Two‐Dimensional Cellular Automata

Advanced Science, EarlyView.
We introduce AutomataGPT, a generative pretrained transformer (GPT) trained on synthetic spatiotemporal data from 2D cellular automata to learn symbolic rules. Demonstrating strong performance on both forward and inverse tasks, AutomataGPT establishes a scalable, domain‐agnostic framework for interpretable modeling, paving the way for future ...
Jaime A. Berkovich, Noah S. David, Markus J. Buehler   +2 more
wiley   +1 more source

Spatially Aware Fusion in 3D Convolutional Autoencoders for Video Anomaly Detection

IEEE Access
Surveillance videos are crucial for crime prevention and public safety, yet the challenge of defining abnormal events hinders their effectiveness, limiting the applicability of supervised methods.
Asim Niaz, Sareer Ul Amin, Shafiullah Soomro, Hamza Zia, Kwang Nam Choi   +4 more
doaj   +1 more source

DEEP NON-NEGATIVE MATRIX FACTORIZATION MODEL FOR CLUSTERING-BASED IMAGE DENOISING [PDF]

Proceedings on Engineering Sciences
Technologies like self-driving cars and cleaning robots are emerging as mainstream technologies. These technologies make use of cognitive recognition.
Shaily Malik , Nishtha Jatana , Divya Jatain , Geetanjali Sharma , Vaani Garg, Amita Yadav   +5 more
doaj   +1 more source

Quantum autoencoders via quantum adders with genetic algorithms

, 2018
The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies.
Alvarez-Rodriguez, U., Lamata, L., Martín-Guerrero, J. D., Sanz, M., Solano, E.   +4 more
core   +1 more source

Autoencoders and their applications in machine learning: a survey

Artificial Intelligence Review
Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction method.
Kamal Berahmand, Fatemeh Daneshfar, Elaheh Sadat Salehi, Yuefeng Li, Yue Xu   +4 more
semanticscholar   +1 more source

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders [PDF]

VISIGRAPP, 2018
Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data. Most commonly, this task is performed by thresholding a pixel-wise reconstruction error based on an $\ell^p$ distance. This procedure, however,
Paul Bergmann, Sindy Löwe, Michael Fauser, David Sattlegger, C. Steger   +4 more
semanticscholar   +1 more source

De Novo Multi‐Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning

Advanced Science, EarlyView.
Current AI‐driven peptide discovery often overlooks complex structural data. This study presents M3‐CAD, a generative pipeline that leverages 3D voxel coloring and a massive database of over 12 000 peptides to capture nuanced physicochemical contexts.
Xiaojuan Li, Haifan Gong, Yue Wang, Yinuo Zhao, Lixiang Li, Peijing Bao, Qingzhou Kong, Jialu Fu, Boyao Wan, Yumeng Zhang, Jinghui Zhang, Jiekun Ni, Zhongxue Han, Xueping Nan, Kunping Ju, Longfei Sun, Yuerui Ma, Huijun Chang, Mengqi Zheng, Yanbo Yu, Xiaoyun Yang, Xiuli Zuo, Haina Wang, Yanqing Li   +23 more
wiley   +1 more source

Scalable and Efficient Protein Secondary Structure Prediction Using Autoencoder-Reduced ProtBERT Embeddings

Applied Sciences
This study proposes a deep learning framework for Protein Secondary Structure Prediction (PSSP) that prioritizes computational efficiency while preserving classification accuracy.
Yahya Najib Hamood Al-Shameri, İrfan Kösesoy, Hakan Gündüz, Ömer Faruk Yılmaz   +3 more
doaj   +1 more source

Learning Disentangled Representations with Reference-Based Variational Autoencoders [PDF]

, 2019
Learning disentangled representations from visual data, where different high-level generative factors are independently encoded, is of importance for many computer vision tasks.
Binefa, Xavier, Martinez, Oriol, Ruiz, Adria, Verbeek, Jakob   +3 more
core   +2 more sources

Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos

, 2018
Identifying kinship relations has garnered interest due to several applications such as organizing and tagging the enormous amount of videos being uploaded on the Internet. Existing research in kinship verification primarily focuses on kinship prediction
Kohli, Naman, Noore, Afzel, Singh, Richa, Vatsa, Mayank, Yadav, Daksha   +4 more
core   +1 more source