Kinetics (human action dataset)
Kinetics is a large-scale dataset designed for action recognition, consisting of thousands of video clips categorized into various human actions.
Technology & Innovation
ImageNet
ImageNet is a large visual database designed for use in visual object recognition software research, playing a significant role in advancing machine learning.
Herbert A. Simon
Herbert A. Simon was an American polymath known for his contributions to economics, psychology, artificial intelligence, and cognitive science. He was awarded the Nobel Prize in Economics in 1978 for his research into decision-making processes within economic organizations.
Comma.ai (open-source driving dataset)
Comma.ai is an open-source driving dataset designed to assist in the development of autonomous vehicle technologies, providing a valuable resource for researchers and developers.
Feature visualization
Feature visualization is a technique in machine learning that helps interpret complex models by visualizing the features learned by the model.
ColBERT (contextualized late interaction over BERT)
ColBERT (Contextualized Late Interaction over BERT) is a neural information retrieval model that leverages BERT embeddings combined with a novel late interaction mechanism to efficiently rank documents based on query relevance. It aims to balance retrieval effectiveness with computational efficiency.
Top-k sampling
Top-k sampling is a probabilistic method used in natural language processing to generate text by selecting the next word from the k most likely options. It balances creativity and coherence in language models by limiting the choice to a subset of probable candidates.
EMNIST
EMNIST (Extended MNIST) is a dataset for handwritten character recognition, extending the original MNIST dataset with additional characters and complexity.
Offline-to-online RL fine-tuning
Offline-to-online RL fine-tuning refers to the process of enhancing reinforcement learning models trained on offline data by further training them online.
Domain adaptation (machine learning)
Domain adaptation is a subfield of machine learning focused on adapting models trained on one domain to perform well on a different but related domain.