Hypernetwork

What is Hypernetwork?

A neural network architecture where one network is used to generate the weights or parameters for another neural network.

In other languages

한국어하이퍼네트워크

다른 신경망의 가중치를 생성하거나 업데이트하기 위해 설계된 별도의 보조 신경망 구조이다.

日本語ハイパーネットワーク

他のニューラルネットワークの重みを生成するために設計された、二次的なニューラルネットワーク。

Related Terms

• SuperpositionA phenomenon in neural networks where a single neuron participates in representing many unrelated concepts simultaneously
• Sparse artificial neural networkNeural network whose connections or weights are intentionally limited, reducing the number of active parameters used for computation
• Generative Adversarial NetworkA class of machine learning frameworks where two neural networks, a generator and a discriminator, compete to create realistic data.
• Operator FusionAn optimization technique that combines multiple consecutive neural network operations into a single kernel to reduce memory overhead and improve speed.
• Multilayer perceptronA class of feedforward artificial neural network consisting of an input layer, one or more hidden layers, and an output layer
• Graph Neural NetworkA type of deep learning architecture specifically designed to process data represented as graphs, enabling the analysis of complex relationships and dependencies between discrete entities.
• Activation FunctionA function in a neural network that determines the output of a node based on its inputs, introducing non-linear properties to the network.
• Transformer attentionA neural network mechanism that weighs relationships among elements in a sequence to determine which ones matter most.
• MLPMulti-Layer Perceptron; a class of feedforward artificial neural network consisting of at least three layers of nodes
• Self-attentionA mechanism in neural networks that allows the model to weigh the importance of different parts of the input data relative to each other.
• Neural operatorA machine learning model that learns mappings between functions, often to approximate solutions to physical systems.
• Pipeline ParallelismA technique that splits an AI model's layers across multiple processors, allowing different parts of a sequence to be processed simultaneously.