MIT researchers developed SEAL, a framework that lets language models continuously learn new knowledge and tasks.

The dataset used was a widely known credit card fraud detection benchmark obtained from Kaggle. It is notoriously imbalanced, ...

In view of this, the paper proposes a deep learning model based on stacked autoencoder (SAE) to predict electricity price. This paper analyzes the factors affecting electricity price, proposes an ...

Autoencoder basics Autoencoders are based on neural networks, and the network consists of two parts: an encoder and a decoder. Encoder compresses the N-dimensional input (e.g. a frame of sensor data) ...

Spectral Unmixing is an important technique in remote sensing for analyzing hyperspectral images to identify endmembers and estimate fractional abundance maps. Over the past few decades, significant ...

What if one model could dismantle the dominance of AI giants like OpenAI and Google? That’s exactly what some are saying about Deepseek R1-0528, the open ...

This project implements a system for detecting anomalies in time series data collected from Prometheus. It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn ...

The model was trained using a combination of reconstruction loss, Kullback–Leibler (KL) divergence, and classification loss, optimized for balanced performance. For peptide generation, latent vectors ...

Our self-supervised digital model based on routine clinical head MRI discriminated PD vs PPS with good accuracy and ... Deep learning-based algorithms outperform traditional feature engineering-based ...