Multi-scale feature learning is a technique in machine learning where a model is designed to understand information at different levels of detail. This means it can recognise both small, fine features and larger, more general patterns within data. It is especially common in areas like image and signal processing, where objects or patterns can appear…
Category: Embeddings & Representations
Transferability of Pretrained Representations
Transferability of pretrained representations refers to the ability to use knowledge learned by a machine learning model on one task for a different, often related, task. Pretrained models are first trained on a large dataset, then their learned features or representations are reused or adapted for new tasks. This approach can save time and resources…
Adaptive Prompt Memory Buffers
Adaptive Prompt Memory Buffers are systems used in artificial intelligence to remember and manage previous interactions or prompts during a conversation. They help the AI keep track of relevant information, adapt responses, and avoid repeating itself. These buffers adjust what information to keep or forget based on the context and the ongoing dialogue to maintain…
Embedding Sanitisation Techniques
Embedding sanitisation techniques are methods used to clean and filter data before it is converted into vector or numerical embeddings for machine learning models. These techniques help remove unwanted content, such as sensitive information, irrelevant text, or harmful language, ensuring that only suitable and useful data is processed. Proper sanitisation improves the quality and safety…
Latent Representation Calibration
Latent representation calibration is the process of adjusting or fine-tuning the hidden features that a machine learning model creates while processing data. These hidden features, or latent representations, are not directly visible but are used by the model to make predictions or decisions. Calibration helps ensure that these internal features accurately reflect the real-world characteristics…
Sparse Decoder Design
Sparse decoder design refers to creating decoder systems, often in artificial intelligence or communications, where only a small number of connections or pathways are used at any one time. This approach helps reduce complexity and resource use by focusing only on the most important or relevant features. Sparse decoders can improve efficiency and speed while…
Intelligent Search Bar
An intelligent search bar is a search tool that uses advanced technologies, such as machine learning or natural language processing, to provide more accurate and relevant results. It can understand user intent, suggest queries, and correct spelling mistakes automatically. This type of search bar helps users find information faster by predicting what they are looking…
Output Anchors
Output anchors are specific points or markers in a process or system where information, results, or data are extracted and made available for use elsewhere. They help organise and direct the flow of outputs so that the right data is accessible at the right time. Output anchors are often used in software, automation, and workflow…
Syntax Coherence
Syntax coherence refers to the logical and consistent arrangement of words and phrases within sentences, so that the meaning is clear and easy to follow. It ensures that the structure of sentences supports the intended message, making communication more effective. Without syntax coherence, writing can become confusing or ambiguous, making it harder for the reader…
Token Usage
Token usage refers to the number of pieces of text, called tokens, that are processed by language models and other AI systems. Tokens can be as short as one character or as long as one word, depending on the language and context. Tracking token usage helps manage costs, performance, and ensures that the input or…