Neural Radiance Fields, or NeRF, is a method in computer graphics that uses artificial intelligence to create detailed 3D scenes from a collection of 2D photographs. It works by learning how light behaves at every point in a scene, allowing it to predict what the scene looks like from any viewpoint. This technique makes it…
Category: Deep Learning
Meta-Learning
Meta-learning is a method in machine learning where algorithms are designed to learn how to learn. Instead of focusing on solving a single task, meta-learning systems aim to improve their ability to adapt to new tasks quickly by using prior experience. This approach helps machines become more flexible, allowing them to handle new problems with…
Bayesian Neural Networks
Bayesian Neural Networks are a type of artificial neural network that use probability to handle uncertainty in their predictions. Instead of having fixed values for their weights, they represent these weights as probability distributions. This approach helps the model estimate not just an answer, but also how confident it is in that answer, which can…
Variational Autoencoders (VAEs)
Variational Autoencoders, or VAEs, are a type of machine learning model that learns to compress data, like images or text, into a simpler form and then reconstructs it back to the original format. They are designed to not only recreate the data but also understand its underlying patterns. VAEs use probability to make their compressed…
Generative Adversarial Networks (GANs)
Generative Adversarial Networks, or GANs, are a type of artificial intelligence where two neural networks compete to improve each other’s performance. One network creates new data, such as images or sounds, while the other tries to detect if the data is real or fake. This competition helps both networks get better, resulting in highly realistic…
Subsymbolic Feedback Tuning
Subsymbolic feedback tuning is a process used in artificial intelligence and machine learning where systems adjust their internal parameters based on feedback, without relying on explicit symbols or rules. This approach is common in neural networks, where learning happens through changing connections between units rather than following step-by-step instructions. By tuning these connections in response…
Zero Resource Learning
Zero Resource Learning is a method in artificial intelligence where systems learn from raw data without needing labelled examples or pre-existing resources like dictionaries. Instead of relying on human-annotated data, these systems discover patterns and structure by themselves. This approach is especially useful for languages or domains where labelled data is scarce or unavailable.
Model Pruning
Model pruning is a technique used in machine learning where unnecessary or less important parts of a neural network are removed. This helps reduce the size and complexity of the model without significantly affecting its accuracy. By cutting out these parts, models can run faster and require less memory, making them easier to use on…
Gradient Clipping
Gradient clipping is a technique used in training machine learning models to prevent the gradients from becoming too large during backpropagation. Large gradients can cause unstable training and make the model’s learning process unreliable. By setting a maximum threshold, any gradients exceeding this value are scaled down, helping to keep the learning process steady and…
Contrastive Pretraining
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…