👾 A library of state-of-the-art pretrained models for Natural Language Processing (NLP)
We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching.
SOTA for Image Generation on CIFAR-10
However, it has been so far limited to simple, shallow models or low-dimensional data, due to the difficulty of computing the Hessian of log-density functions.
The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving.
SOTA for Robust Object Detection on COCO
Deep learning techniques have become the method of choice for researchers working on algorithmic aspects of recommender systems.
We hypothesize that the degree of distributional shift is related to the breadth of the training data distribution, and conduct experiments that demonstrate this.
Although our baseline system is a straightforward combination of standard methods, we obtain the state-of-the-art results.
SOTA for 3D Multi-Object Tracking on KITTI