Counterfactual explanations
Counterfactual explanations provide insights into the decisions made by algorithms by illustrating what could have happened under different circumstances.
Artificial Intelligence
VoxCeleb
VoxCeleb is a large-scale speaker recognition dataset designed for research in speaker verification and recognition tasks.
Chatbot
A chatbot is a computer program designed to simulate human conversation through text or speech interactions. It uses artificial intelligence and natural language processing to understand user inputs and provide relevant responses, often employed in customer service, information retrieval, and entertainment.
MRPC (Microsoft Research Paraphrase Corpus)
The MRPC is a dataset developed by Microsoft Research for paraphrase identification tasks, consisting of pairs of sentences labeled for semantic similarity.
A3C (asynchronous advantage actor-critic)
A3C is a reinforcement learning algorithm that utilizes asynchronous training to improve performance and efficiency in decision-making tasks.
Exploration by random network distillation (RND)
Exploration by random network distillation (RND) is a technique in reinforcement learning used to encourage agents to explore unfamiliar states by providing intrinsic rewards based on the novelty of observations. It leverages a fixed random neural network and a trainable network to measure prediction error as a proxy for novelty.
OpenAI Five (Dota 2 bot)
OpenAI Five is an artificial intelligence system developed by OpenAI to play the complex multiplayer online battle arena game Dota 2. It demonstrated advanced capabilities in strategic gameplay by competing against and defeating professional human players.
LiT (locked-image text tuning)
LiT, or locked-image text tuning, is a method in machine learning that enhances text generation by integrating visual information from images.
Role prompting
Role prompting is a technique used in artificial intelligence and natural language processing where a system is given a specific role or persona to guide its responses. This method helps shape the behavior and output of AI models by instructing them to adopt particular perspectives or functions.
Model compression
Model compression refers to a set of techniques aimed at reducing the size and computational requirements of machine learning models while maintaining their performance. It enables deployment of models on resource-constrained devices and improves inference efficiency.