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Deep image prior is a type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly initialized and used as prior to solve inverse problems such as noise reduction, super-resolution, and inpainting.
The message-passing model is one of the most commonly used models in distributed computing. In this model, each process is modeled as a node of a graph. Each communication channel between two processes is an edge of the graph. Two commonly used algorithms for the classical minimum spanning tree problem are Prim's algorithm and Kruskal's ...
This problem can be seen as a generalization of the linear assignment problem. [2] In words, the problem can be described as follows: An instance of the problem has a number of agents (i.e., cardinality parameter) and a number of job characteristics (i.e., dimensionality parameter) such as task, machine, time interval, etc. For example, an ...
The problem for graphs is NP-complete if the edge lengths are assumed integers. The problem for points on the plane is NP-complete with the discretized Euclidean metric and rectilinear metric. The problem is known to be NP-hard with the (non-discretized) Euclidean metric. [3]: ND22, ND23
A distributed algorithm is an algorithm designed to run on computer hardware constructed from interconnected processors.Distributed algorithms are used in different application areas of distributed computing, such as telecommunications, scientific computing, distributed information processing, and real-time process control.
Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems. It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to solve problems that require the processing of very large data sets.
TensorFlow and PyTorch, by far the most popular machine learning libraries, [20] as of 2023 largely only include Adam-derived optimizers, as well as predecessors to Adam such as RMSprop and classic SGD. PyTorch also partially supports Limited-memory BFGS, a line-search method, but only for single-device setups without parameter groups. [19] [21]
Later, GLaM [39] demonstrated a language model with 1.2 trillion parameters, each MoE layer using top-2 out of 64 experts. Switch Transformers [21] use top-1 in all MoE layers. The NLLB-200 by Meta AI is a machine translation model for 200 languages. [40] Each MoE layer uses a hierarchical MoE with two levels.