Out-of-Distribution Detection is a technique used to identify when a machine learning model encounters data that is significantly different from the data it was trained on. This helps to prevent the model from making unreliable or incorrect predictions on unfamiliar inputs. Detecting these cases is important for maintaining the safety and reliability of AI systems…
Category: Deep Learning
Continual Learning
Continual learning is a method in artificial intelligence where systems are designed to keep learning and updating their knowledge over time, instead of only learning once from a fixed set of data. This approach helps machines adapt to new information or tasks without forgetting what they have already learned. It aims to make AI more…
Memory-Augmented Neural Networks
Memory-Augmented Neural Networks are artificial intelligence systems that combine traditional neural networks with an external memory component. This memory allows the network to store and retrieve information over long periods, making it better at tasks that require remembering past events or facts. By accessing this memory, the network can solve problems that normal neural networks…
Dynamic Neural Networks
Dynamic Neural Networks are artificial intelligence models that can change their structure or operation as they process data. Unlike traditional neural networks, which have a fixed sequence of layers and operations, dynamic neural networks can adapt in real time based on the input or the task at hand. This flexibility allows them to handle a…
Neural Module Networks
Neural Module Networks are a type of artificial intelligence model that break down complex problems into smaller tasks, each handled by a separate neural network module. These modules can be combined in different ways, depending on the question or task, to produce a final answer or result. This approach is especially useful for tasks like…
Pruning-Aware Training
Pruning-aware training is a machine learning technique where a model is trained with the knowledge that parts of it will be removed, or pruned, later. This helps the model maintain good performance even after some connections or neurons are taken out to make it smaller or faster. By planning for pruning during training, the final…
Model Compression
Model compression is the process of making machine learning models smaller and faster without losing too much accuracy. This is done by reducing the number of parameters or simplifying the model’s structure. The goal is to make models easier to use on devices with limited memory or processing power, such as smartphones or embedded systems.
Teacher-Student Models
Teacher-Student Models are a technique in machine learning where a larger, more powerful model (the teacher) is used to train a smaller, simpler model (the student). The teacher model first learns a task using lots of data and computational resources. Then, the student model learns by imitating the teacher, allowing it to achieve similar performance…
Cross-Modal Learning
Cross-modal learning is a process where information from different senses or types of data, such as images, sounds, and text, is combined to improve understanding or performance. This approach helps machines or people connect and interpret signals from various sources in a more meaningful way. By using multiple modes of data, cross-modal learning can make…
Self-Attention Mechanisms
Self-attention mechanisms are a method used in artificial intelligence to help a model focus on different parts of an input sequence when making decisions. Instead of treating each word or element as equally important, the mechanism learns which parts of the sequence are most relevant to each other. This allows for better understanding of context…