As the use of cloud computing continues to rise, controlling cost becomes increasingly important.
We describe a novel approach for generating music using a self-correcting, non-chronological, autoregressive model.
We then extend the count-min sketch to a Higher-Order sketch to capture complex relations in graph data, and to reduce detecting suspicious dense subgraph problem to finding a dense submatrix in constant time.
A case study demonstrates a typical use case for the library: picking a suitable model for a question answering task.
In this paper, we showcase HLAT: a family of 7B and 70B decoder-only LLMs pre-trained using 4096 AWS Trainium accelerators over 1. 8 trillion tokens.
We present a method for provably defending any pretrained image classifier against $\ell_p$ adversarial attacks.
We demonstrate that this embedding is capable of predicting task similarities that match our intuition about semantic and taxonomic relations between different visual tasks (e. g., tasks based on classifying different types of plants are similar) We also demonstrate the practical value of this framework for the meta-task of selecting a pre-trained feature extractor for a new task.
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.
Deep neural networks often require copious amount of labeled-data to train their scads of parameters.
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
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