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PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce.
In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [ 24 ] PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo , a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and ...
The architecture of a generic GNN implements the following fundamental layers: [12]. Permutation equivariant: a permutation equivariant layer maps a representation of a graph into an updated representation of the same graph.
Diabetes 130-US hospitals for years 1999–2008 Dataset 9 years of readmission data across 130 US hospitals for patients with diabetes. Many features of each readmission are given. 100,000 Text Classification, clustering 2014 [274] [275] J. Clore et al. Diabetic Retinopathy Debrecen Dataset
For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...
As hand-crafting weights defeats the purpose of machine learning, the model must compute the attention weights on its own. Taking analogy from the language of database queries, we make the model construct a triple of vectors: key, query, and value. The rough idea is that we have a "database" in the form of a list of key-value pairs.
The special token is an architectural hack to allow the model to compress all information relevant for predicting the image label into one vector. Animation of ViT. The 0th token is the special <CLS>. The other 9 patches are projected by a linear layer before being fed into the Transformer encoder as input tokens 1 to 9.
The softmax function takes as input a vector z of K real numbers, and normalizes it into a probability distribution consisting of K probabilities proportional to the exponentials of the input numbers.