A Deep Learning Alternative Can Help AI Agents Gameplay the Real World A new machine learning approach tries to better emulate the human brain, in hopes of creating more capable agentic AI.

Pratama, M. and Wang, D. (2019) Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams. Information Sciences, 495, 150-174.

The separation of encoder and decoder components represents a promising future direction for wearable AI devices, efficiently balancing response quality, privacy protection, latency and power ...

This study presents a deep learning (DL)-based approach to the seismic velocity inversion problem, focusing on both noisy and noiseless training datasets of varying sizes. Our seismic velocity ...

The brain encoder then learns to align MEG signals to these image embeddings. Finally, the image decoder generates a plausible image based on these brain representations.

The Mathematics Behind Diffusion Models To truly understand diffusion models, it's crucial to dig deeper into the mathematics that underpin them. Let's explore some key concepts in more detail: Markov ...

Additionally, deepSPoC, which combines SPoC with deep learning, is proposed and tested on various mean-field equations. Check out the Paper. All credit for this research goes to the researchers of ...

Deep learning is a branch of AI—specifically, a subset of machine learning (ML) —that involves the use of artificial neural networks to autonomously learn complex patterns and make intelligent ...

This article explores some of the most influential deep learning architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), ...

An encoder-decoder architecture is a powerful tool used in machine learning, specifically for tasks involving sequences like text or speech. It’s like a two-part machine that translates one form ...