Results 141 to 150 of about 6,131,436 (285)

Sleep staging through an unsupervised learning lens. [PDF]

Patterns (N Y)
Christopoulos A, Tzovara A.
europepmc   +1 more source

VAE+DDPG: An Attention‐Enhanced Variational Autoencoder for Deep Reinforcement Learning‐Based Autonomous Navigation in Low‐Light Environments

Advanced Intelligent Systems, EarlyView.
Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee, Chanhong Hwang, Hyunseok Kang, Yechan Park, Hangwoo Jin, Tuan Anh Nguyen, Eunmi Choi, Dugki Min   +7 more
wiley   +1 more source

Generalizable morphological profiling of cells by interpretable unsupervised learning. [PDF]

Nat Commun
Murthy RS, Stassen SV, Siu DMD, Lo MCK, Yip GGK, Tsia KK.   +5 more
europepmc   +1 more source

gnSPADE: Incorporating Gene Network Structures Enhances Reference‐Free Deconvolution in Spatial Transcriptomics

Advanced Intelligent Systems, EarlyView.
gnSPADE integrates gene‐network structures into a probabilistic topic modeling framework to achieve reference‐free cell‐type deconvolution in spatial transcriptomics. By embedding gene connectivity within the generative process, gnSPADE enhances biological interpretability and accuracy across simulated and real datasets, revealing spatial organization ...
Aoqi Xie, Yuehua Cui
wiley   +1 more source

A Generalizable Transformer Framework for Gene Regulatory Network Inference from Single‐Cell Transcriptomes

Advanced Intelligent Systems, EarlyView.
FTGRN introduces an LLM‐enhanced framework for gene regulatory network inference through a two‐stage workflow. It combines a Transformer‐based model, pretrained on GPT‐4 derived gene embeddings and regulatory knowledge, with a fine‐tuning stage utilizing single‐cell RNA‐seq data.
Guangzheng Weng, Hyobin Kim, Patrick Martin, Junil Kim, Tae‐Hyung Kim, Gi‐Hoon Nam, Dongha Kim, Kyoung Jae Won   +7 more
wiley   +1 more source

Unsupervised learning of spatially varying regularization for diffeomorphic image registration. [PDF]

Med Image Anal
Chen J, Wei S, Liu Y, Bian Z, He Y, Carass A, Bai H, Du Y.   +7 more
europepmc   +1 more source

Neuromorphic Photonic Processing and Memory With Spiking Resonant Tunneling Diode Neurons and Neural Networks

Advanced Intelligent Systems, EarlyView.
This work shows resonant tunneling diode‐based opto‐electronic spiking neurons enabling fast edge detection in time series, a two‐layer photonic spiking neural network for complex classification, and a depth‐tunable photonic spiking memory system. Neuromorphic computing—modeled after the functionality and efficiency of biological neural systems—offers ...
Dafydd Owen‐Newns, Joshua Robertson, Giovanni Donati, Qusay Al‐Taai, José Figueiredo, Edward Wasige, Kathy Lüdge, Bruno Romeira, Antonio Hurtado   +8 more
wiley   +1 more source

Supervised and Unsupervised Learning with Numerical Computation for the Wolfram Cellular Automata. [PDF]

Entropy (Basel)
Tuo K, Deng S, Yang Y, Wang Y, Wang Q, Li W, Zhang W.   +6 more
europepmc   +1 more source

Integrating Artificial Intelligence With Droplet‐Based Microfluidics: Advances, Challenges, and Emerging Opportunities

Advanced Intelligent Systems, EarlyView.
Droplet‐based microfluidics enables precise, high‐throughput microscale reactions but continues to face challenges in scalability, reproducibility, and data complexity. This review examines how artificial intelligence enhances droplet generation, detection, sorting, and adaptive control and discusses emerging opportunities for clinical and industrial ...
Junyan Lai, Sukrit Gaira, Rongfeng Wang, Qingtian Zhang, Ming Li, Liao Wu, Diangeng Li, Yi Li, Chengchen Zhang, Jinhong Guo, Shi‐Yang Tang   +10 more
wiley   +1 more source

Early damage detection in bridges using a variational autoencoder-based hybrid unsupervised learning framework. [PDF]

Sci Rep
Pakzad SS, Masoodi AR.
europepmc   +1 more source