The framework of reinforcement learning or optimal control provides a mathematical formalization of intelligent decision making that is powerful and broadly applicable.
The first model follows conventional matrix factorization which factorizes a group of key users' rating matrix to obtain meta latents.
The evaluation of object detection models is usually performed by optimizing a single metric, e. g. mAP, on a fixed set of datasets, e. g. Microsoft COCO and Pascal VOC.
Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge.
The major challenge in automated feature generation is to efficiently and accurately identify useful features from a vast pool of candidate features.
Neural Radiance Fields (NeRF) coupled with GANs represent a promising direction in the area of 3D reconstruction from a single view, owing to their ability to efficiently model arbitrary topologies.
By simply applying depthwise separable convolutions as token mixer in the bottom stages and vanilla self-attention in the top stages, the resulting model CAFormer sets a new record on ImageNet-1K: it achieves an accuracy of 85. 5% at 224x224 resolution, under normal supervised training without external data or distillation.
Once the subject is embedded in the output domain of the model, the unique identifier can then be used to synthesize fully-novel photorealistic images of the subject contextualized in different scenes.