Neural Embedding Space

A Neural Embedding Space is an embedding space created by a neural network training system.

Context:
- It can (typically) be generated by deep learning models which learn to encode data into a more compact and meaningful format.
- It can (often) serve as the foundational structure for Neural Embedding Vectors which represent individual data points within this space.
- It can range from being a Low-Dimensional Neural Embedding Space (<50) to being a Medium-Dimensional Neural Embedding Space [50-500] to being a High-Dimensional Neural Embedding Space (500-5000).
- It can preserve semantic relationships within the data, allowing for effective similarity measurements and clustering.
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Example(s):
- a Generational Method-Specific Neural Embedding Space, such as:
  - an Autoencoder-Based Neural Embedding Space: Uses autoencoders to reduce dimensionality and encode data.
  - a CNN-Based Neural Embedding Space: Utilizes convolutional neural networks to embed image and video data.
  - a RNN-Based Neural Embedding Space: Employs recurrent neural networks for embedding sequential data like text and time-series.
  - a Transformer-Based Neural Embedding Space: Utilizes transformer models to create embeddings that capture contextual relationships in data.
  - a LLM-Based Neural Embedding Space: Uses large language models to generate embeddings that reflect deep semantic understanding.
  - ...
- a Data Type-Specific Neural Embedding Space, such as:
  - a Text-Item Neural Embedding Space designed for embedding text items using models like BERT or GloVe.
  - an Image Neural Embedding Space created using convolutional neural networks to embed images.
  - an Audio Neural Embedding Space for sound data, generated by models trained to capture the temporal dynamics of audio.
  - a User Behavior Neural Embedding Space in recommender systems, where user actions and preferences are embedded.
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Counter-Example(s):
- Sparse Vector Space, where data representation is not dense or primarily consists of zeros.
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See: Neural Network, Embedding Vector, Dimensionality Reduction, Semantic Similarity.

References

2019

(Cao et al., 2019) ⇒ Shengcao Cao, Xiaofang Wang, and Kris M. Kitani. (2019). “Learnable Embedding Space for Efficient Neural Architecture Compression." In: arXiv preprint arXiv:1902.00383.
- QUOTE: ... (x) over X, since the neural architecture domain X is discrete ... embedding space for the neural architecture domain and define the kernel function based on the learned embedding space...