FeUdal networks (hierarchical RL)
FeUdal networks are a model in hierarchical reinforcement learning that enables efficient learning by structuring tasks into layers of subgoals.
machine learning
Batch normalization
Batch normalization is a technique used in deep learning to improve the training speed and stability of artificial neural networks by normalizing layer inputs. It helps reduce internal covariate shift, allowing higher learning rates and reducing the sensitivity to initialization.
Adversarial example
An adversarial example is a specially crafted input designed to deceive machine learning models, causing them to make incorrect predictions or classifications. These examples exploit vulnerabilities in models, often with minimal perturbations imperceptible to humans.
Precision and recall
Precision and recall are fundamental metrics used to evaluate the performance of classification and information retrieval systems. Precision measures the accuracy of positive predictions, while recall measures the ability to identify all relevant instances.
Charades (human activity dataset)
The Charades dataset is a large-scale dataset for human activity recognition in video, widely used for training and evaluating machine learning models.
Generative adversarial network (GAN)
Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed to generate new data instances that resemble a given training dataset.
Dimensionality reduction
Dimensionality reduction is a process in data analysis and machine learning that transforms data from a high-dimensional space into a lower-dimensional space while preserving essential properties. It facilitates visualization, reduces storage requirements, and helps improve the performance of algorithms by eliminating redundant or irrelevant features.
T5 (text-to-text transfer transformer)
T5 (text-to-text transfer transformer) is a neural network model developed by Google that frames all natural language processing tasks as a unified text-to-text problem. It leverages a transformer architecture to achieve state-of-the-art results across a wide range of language tasks by converting inputs and outputs into text sequences.
Prompt engineering
Prompt engineering is the practice of designing and refining input prompts for artificial intelligence models, especially large language models, to achieve desired outputs. It involves crafting queries or instructions that guide AI systems to generate more accurate, relevant, or contextually appropriate responses.
Energy-based model
An energy-based model is a type of probabilistic model in machine learning that associates a scalar energy value to each configuration of variables. These models learn to represent data by minimizing the energy of observed data points and assigning higher energy to other configurations.