Transferable representations are ways of encoding information so that what is learned in one context can be reused in different, but related, tasks. In machine learning, this often means creating features or patterns from data that help a model perform well on new, unseen tasks without starting from scratch. This approach saves time and resources…
Category: Deep Learning
Neural Network Robustness
Neural network robustness is the ability of a neural network to maintain accurate and reliable performance even when faced with unexpected or challenging inputs, such as noisy data or intentional attacks. Robustness helps ensure that the network does not make mistakes when small changes are made to the input. This is important for safety and…
Neural Network Generalization
Neural network generalisation refers to the ability of a neural network to perform well on new, unseen data after being trained on a specific set of examples. It shows how well the network has learned patterns and rules, rather than simply memorising the training data. Good generalisation means the model can make accurate predictions in…
Low-Rank Factorization
Low-Rank Factorisation is a mathematical technique used to simplify complex data sets or matrices by breaking them into smaller, more manageable parts. It expresses a large matrix as the product of two or more smaller matrices with lower rank, meaning they have fewer independent rows or columns. This method is often used to reduce the…
Sparse Activation Maps
Sparse activation maps are patterns in neural networks where only a small number of neurons or units are active at any given time. This means that for a given input, most of the activations are zero or close to zero, and only a few are significantly active. Sparse activation helps make models more efficient by…
Neural Network Compression
Neural network compression refers to techniques used to make large artificial neural networks smaller and more efficient without significantly reducing their performance. This process helps reduce the memory, storage, and computing power required to run these models. By compressing neural networks, it becomes possible to use them on devices with limited resources, such as smartphones…
Efficient Attention Mechanisms
Efficient attention mechanisms are methods used in artificial intelligence to make the attention process faster and use less computer memory. Traditional attention methods can become slow or require too much memory when handling long sequences of data, such as long texts or audio. Efficient attention techniques solve this by simplifying calculations or using clever tricks,…
Weight Sharing Techniques
Weight sharing techniques are methods used in machine learning models where the same set of parameters, or weights, is reused across different parts of the model. This approach reduces the total number of parameters, making models smaller and more efficient. Weight sharing is especially common in convolutional neural networks and models designed for tasks like…
Model Distillation Frameworks
Model distillation frameworks are tools or libraries that help make large, complex machine learning models smaller and more efficient by transferring their knowledge to simpler models. This process keeps much of the original model’s accuracy while reducing the size and computational needs. These frameworks automate and simplify the steps needed to train, evaluate, and deploy…
Neural Network Quantization
Neural network quantisation is a technique that reduces the amount of memory and computing power needed by a neural network. It works by representing the numbers used in the network, such as weights and activations, with lower-precision values instead of the usual 32-bit floating-point numbers. This makes the neural network smaller and faster, while often…