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Language Models are Unsupervised Multitask Learners
It is demonstrated that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText, suggesting a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations. Expand
Generative Pretraining From Pixels
This work trains a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure, and finds that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and low-data classification. Expand
AUTOMATIC DETECTION AND RECOGNITION OF CRATERS BASED ON THE SPECTRAL FEATURES OF LUNAR ROCKS AND MINERALS
Abstract. Crater-detection approaches can be divided into four categories: manual recognition, shape-profile fitting algorithms, machine-learning methods and geological information-based analysis… Expand