AI 용어집
인공지능 완전 사전
Memory Augmented Autoencoder (MAE)
Autoencoder architecture integrating an externally addressable memory to store data prototypes, improving reconstruction and generalization on complex distributions.
Sparse Memory Network
Sparsely addressable memory where only a small portion of locations is activated for each query, reducing interference and improving storage capacity for distinct patterns.
Key-Value Memory Matrix
Two-dimensional memory structure where keys (addressing vectors) are associated with values (stored content), enabling efficient retrieval of complex patterns.
Hopfield Network-based Memory
Associative memory inspired by Hopfield networks used in autoencoders to store and retrieve patterns through dynamic convergence to stable states.
Memory Consolidation Strategy
Mechanism regulating memory updates to preserve important information while allowing selective forgetting of redundant or obsolete patterns.
Memory-augmented Variational Autoencoder (M-VAE)
Variant of VAE integrating memory to improve latent space sampling by storing learned probability distributions for different patterns.
Memory Interference Mitigation
Set of techniques aimed at reducing catastrophic interference in memory when learning new patterns, thus preserving previously acquired knowledge.
Neural Turing Machine (NTM) Memory
Memory system controllable by a neural network, enabling complex sequential read/write operations in the context of autoencoders.
Memory Capacity Optimization
Process of adjusting memory size and structure to maximize the number of distinct patterns storable while minimizing computational complexity.
Associative Memory Retrieval
Mechanism allowing information retrieval from memory based on partial or noisy associations, crucial for robust reconstruction in autoencoders.
Memory-augmented Generative Adversarial Network (MemGAN)
GAN architecture integrating a shared memory between generator and discriminator to improve diversity and quality of generated samples.
Prototype-based Memory
Type of memory storing prototypical representations (centroids) of data clusters rather than individual examples, optimizing storage space.
Memory-augmented Denoising Autoencoder
Denoising autoencoder using memory to store clean versions of patterns, significantly improving reconstruction capabilities on corrupted data.
Temporal Memory Integration
Incorporation of time-sensitive memory mechanisms to capture and reconstruct complex temporal dependencies in sequential data.