Waymo Open Dataset
The Waymo Open Dataset is a large-scale dataset designed for the development of autonomous vehicle technologies, featuring diverse driving scenarios and high-definition maps.
Artificial Intelligence
Logit bias
Logit bias refers to the intentional adjustment of the logit values in machine learning models to influence prediction probabilities. It is commonly used in natural language processing and classification tasks to control or steer model outputs.
Intersection over union (IoU)
Intersection over Union (IoU) is a metric used to quantify the accuracy of an object detector on a particular dataset. It measures the overlap between two bounding boxes by dividing the area of their intersection by the area of their union, providing a value between 0 and 1.
AVA (atomic visual actions dataset)
The AVA dataset is a large-scale dataset for recognizing atomic visual actions in video clips, facilitating advancements in computer vision and machine learning.
Fisher-BRC (behavior regularized critic)
Fisher-BRC is a reinforcement learning framework that incorporates behavior regularization to enhance the learning process of an agent.
Concept activation vectors (CAV)
Concept activation vectors (CAV) are a method used in machine learning to interpret neural networks by associating specific directions in the latent space with human-understandable concepts.
PV-RCNN (point-voxel RCNN for 3D detection)
PV-RCNN is a 3D object detection framework that integrates point-based and voxel-based features to improve accuracy in tasks such as autonomous driving. It uses a novel point-voxel feature set abstraction to enhance perception from LiDAR data.
Daniel Dennett
Daniel Dennett is an influential American philosopher, cognitive scientist, and author, known for his work on the philosophy of mind and consciousness.
MAE (masked autoencoder for vision)
MAE is a framework for self-supervised learning in computer vision, focusing on reconstructing masked portions of images.
VoiceBox (non-autoregressive TTS)
VoiceBox is a non-autoregressive text-to-speech (TTS) system designed to generate natural-sounding speech efficiently by predicting audio features in parallel rather than sequentially. It leverages advanced neural network architectures to improve synthesis speed while maintaining high audio quality.