Generative Adversarial Network
A class of machine learning frameworks where two neural networks, a generator and a discriminator, compete against each other to produce realistic data samples.
Tag: Architecture
Variational Autoencoder
A generative model that learns to encode input data into a latent space and then decode from that space to reconstruct the original data, often used for generating new data samples.
TabPFN
A surrogate model designed for accurate and efficient prediction in optimization tasks, with capabilities for uncertainty estimation.
Dual-Control Bi-Space Surrogate-Assisted Evolutionary Algorithm
A framework that combines dual control mechanisms for candidate generation and infill criterion selection in surrogate-assisted evolutionary algorithms.
Dynamic Segmented Bus
A communication architecture that allows for efficient data transfer in neuromorphic systems by dynamically managing segments of the bus based on activity levels.
Spiking Neural Network
A type of artificial neural network that uses spikes, or discrete events, to represent information, mimicking the way biological neurons communicate.
Neural Engineering Framework (NEF)
A framework for designing and implementing neural networks that can be executed on neuromorphic hardware.
Bellman Memory Units (BMUs)
A neuromorphic architecture that integrates Bellman equations for reinforcement learning, enabling dynamic network topology evolution.
Video Diffusion Model
A generative model that creates videos by diffusing information over time, often conditioned on textual or visual inputs.
Visual Language Model
A model that integrates visual and textual information to perform tasks that require understanding both modalities.