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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.
One prominent example is molecular drug design. [6] [7] [8] Each input sample is a graph representation of a molecule, where atoms form the nodes and chemical bonds between atoms form the edges. In addition to the graph representation, the input also includes known chemical properties for each of the atoms.
Its machine-learning ecosystem includes Nx for computing on CPUs and GPUs, Bumblebee and Axon for serving and training models, Broadway for distributed processing pipelines, Membrane for image and video processing, Livebook for prototyping and publishing notebooks, and Nerves for embedding on devices.
In software engineering, a distributed design pattern is a design pattern focused on distributed computing problems. Classification
Game theory and distributed computing both deal with a system with many agents, in which the agents may possibly pursue different goals. However they have different focuses. For instance one of the concerns of distributed computing is to prove the correctness of algorithms that tolerate faulty agents and agents performing actions concurrently.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
A diffusion model consists of three major components: the forward process, the reverse process, and the sampling procedure. [1] The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset.
We sample s-dimensional random vector U and mix it with {x 1, ..., x N}. In detail, for each x j, create = + and use the sequence () instead of (). If we have R replications for Monte Carlo, sample s-dimensional random vector U for each replication. Randomization allows to give an estimate of the variance while still using quasi-random sequences.