Scientific Research Publishing

A Hybrid CNN-LSTM Variational Autoencoder for Treatment Response Prediction in Synthetic Psychiatric Data ()

Treatment response prediction remains one of the most pressing challenges in precision psychiatry, where patient heterogeneity and complex biomarker interactions limit the reliability of conventional ...
IEEE

Temporal Autoencoder for Identification and Predictive Control of Nonlinear Dynamics based on Koopman Operator Theory

Abstract: This paper presents an approach to Model Predictive Control (MPC) for nonlinear systems by combining Koopman operator theory with autoencoder networks enhanced by temporal layers for ...
Phys.org

Battle to eradicate invasive pythons in Florida achieves stunning milestone

A startling milestone has been reached in Florida's war against the invasive Burmese pythons eating their way across the Everglades. The Conservancy of Southwest Florida reports it has captured and ...
ascopubs.org

A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides.

Recent advances in feature selection methods for breast cancer recurrence prediction: A systematic review. This is an ASCO Meeting Abstract from the 2025 ASCO Annual Meeting I. This abstract does not ...
GitHub

Multimodal Masked Autoencoder Pre-training for 3D MRI-based Brain Tumor Analysis with Missing Modalities

This is a Python repository for recovering weights or re-training a multimodal masked autoencoder on anatomical brain MRIs. It naturally handles missing modalities and processes any combination of ...
IEEE

Autoencoder-Lstm Framework for Accurate Foreign Exchange Volatility Prediction

Abstract: Accurately predicting foreign exchange volatility is crucial for financial institutions, traders, and policymakers. This makes it an extremely complex and dynamic market, hence failing ...
marktechpost

This AI Paper Introduces a Short KL+MSE Fine-Tuning Strategy: A Low-Cost Alternative to End-to-End Sparse Autoencoder Training for Interpretability

Sparse autoencoders are central tools in analyzing how large language models function internally. Translating complex internal states into interpretable components allows researchers to break down ...
C&EN

Interpolation of Imaging Mass Spectrometry Data by a Window-Based Adversarial Autoencoder Method

Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka 431-3192, Japan Department of Cellular and Molecular Anatomy, Hamamatsu University School of ...
PNAS

ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design

The sequence of amino acids within a protein dictates its structure and function. Protein engineering campaigns seek to discover protein sequences with desired functions. Data-driven models of the ...