Andrej Karpathy's new open source 'autoresearch' lets you run hundreds of AI experiments a night — with revolutionary implications

An AI agent reads its own source code, forms a hypothesis for improvement (such as changing a learning rate or an architecture depth), modifies the code, runs the experiment, and evaluates the results ...
IEEE

Sparse Autoencoder-Based Deep Learning Approach for Enhancing End-to-End Communication System Performance

Deep Learning (DL) has emerged as a transformative approach in artificial intelligence, demonstrating remarkable capabilities in solving complex problems once considered unattainable. Its ability to ...
IEEE

Multi-layer Structure Autoencoder for Deep Learning Using Kernel Method

Abstract: Deep learning performs feature extraction through a series of data transformations. Convolutional neural networks (CNNs) are among the most representative methods in deep learning. CNNs ...
Women's Health

What Is A Deep Plane Face Lift, The Plastic Surgery Technique Behind Celebrities' Refreshed Faces

There's something interesting happening with the celebrities we've known, loved, and followed for decades: They don't seem to be aging like the rest of us. Lindsay Lohan, at the height of her ...
Scientific Research Publishing

Traffic Speed Prediction Based on Autoencoder and Deep Learning ()

Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction model ...
GitHub

Enhancing EV Cost Prediction Using AutoEncoder-Based Feature Learning

Electric Vehicle (EV) cost prediction involves analyzing complex, high-dimensional data that often contains noise, multicollinearity, and irrelevant features. Traditional regression models struggle to ...
GitHub

Lesson No. 3: Variational autoencoder code for CelebA dataset takes too long to load

train_data = utils.image_dataset_from_directory( "celeba-dataset/img_align_celeba/img_align_celeba", labels = None, color_mode="rgb", image_size = (64, 64), batch ...