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Neural Architecture Search (NAS) is one of the focal points for the Deep Learning community, but reproducing NAS methods is extremely challenging due to numerous low-level implementation details.
These datasets, however, focus predominantly on computer vision and NLP tasks and thus suffer from the problem of limited coverage of application domains.
Recently, a special kind of graph, i. e., supernet, which allows two nodes connected by multi-choice edges, has exhibited its power in neural architecture search (NAS) by searching better architectures for computer vision (CV) and natural language processing (NLP) tasks.
State-of-the-art results in deep learning have been improving steadily, in good part due to the use of larger models.
An important goal of neural architecture search (NAS) is to automate-away the design of neural networks on new tasks in under-explored domains, thus helping to democratize machine learning.
Uniform-precision neural network quantization has gained popularity thanks to its simple arithmetic unit densely packed for high computing capability.
PhlatCam, with its form factor potentially reduced by orders of magnitude, has emerged as a promising solution to the first aforementioned challenge, while the second one remains a bottleneck.
Differentiable Neural Architecture Search is one of the most popular Neural Architecture Search (NAS) method for its search efficiency and simplicity, accomplished by jointly optimizing the model weight and architecture parameters in a weight-sharing supernet via gradient-based algorithms.