Contrastive pretraining is a method in machine learning where a model learns to tell how similar or different two pieces of data are. It does this by being shown pairs of data and trying to pull similar pairs closer together in its understanding, while pushing dissimilar pairs further apart. This helps the model build useful…
Loss landscape analysis is the study of how the values of a machine learning model’s loss function change as its parameters are adjusted. It helps researchers and engineers understand how easy or difficult it is to train a model by visualising or measuring the shape of the loss surface. A smoother or flatter loss landscape…
Evaluation benchmarks are standard tests or sets of criteria used to measure how well a system, tool, or model performs. They provide a way to compare different approaches fairly by using the same tasks or datasets. In technology and research, benchmarks help ensure that results are reliable and consistent across different methods or products.
Batch normalisation is a technique used in training deep neural networks to make learning faster and more stable. It works by adjusting and scaling the activations of each layer so they have a consistent mean and variance. This helps prevent problems where some parts of the network learn faster or slower than others, making the…
A Mixture of Experts is a machine learning model that combines several specialised smaller models, called experts, to solve complex problems. Each expert focuses on a specific part of the problem, and a gating system decides which experts to use for each input. This approach helps the overall system make better decisions by using the…
The Neural Tangent Kernel (NTK) is a mathematical tool used to study and predict how very large neural networks learn. It simplifies the behaviour of neural networks by treating them like a type of kernel method, which is a well-understood class of machine learning models. Using the NTK, researchers can analyse training dynamics and generalisation…
LoRA Fine-Tuning is a method used to adjust large pre-trained artificial intelligence models, such as language models, with less computing power and memory. Instead of changing all the model’s weights, LoRA adds small, trainable layers that adapt the model for new tasks. This approach makes it faster and cheaper to customise models for specific needs…
Gradient accumulation is a technique used in training neural networks where gradients from several smaller batches are summed before updating the model’s weights. This allows the effective batch size to be larger than what would normally fit in memory. It is especially useful when hardware limitations prevent the use of large batch sizes during training.
Dynamic prompt tuning is a technique used to improve the responses of artificial intelligence language models by adjusting the instructions or prompts given to them. Instead of using a fixed prompt, the system can automatically modify or optimise the prompt based on context, user feedback, or previous interactions. This helps the AI generate more accurate…
Parameter-efficient fine-tuning is a machine learning technique that adapts large pre-trained models to new tasks or data by modifying only a small portion of their internal parameters. Instead of retraining the entire model, this approach updates selected components, which makes the process faster and less resource-intensive. This method is especially useful when working with very…