The neural architecture search (NAS) algorithm with reinforcement learning can be a powerful and novel framework for the automatic discovering process of neural architectures. However, its application is restricted by noncontinuous and high-dimensional search spaces, which result in difficulty in optimization. To resolve these problems, we proposed NAS in embedding space (NASES), which is a novel framework. Unlike other NAS with reinforcement learning approaches that search over a discrete and high-dimensional architecture space, this approach enables reinforcement learning to search in an embedding space by using architecture encoders and decoders. The current experiment demonstrated that the performance of the final architecture network using the NASES procedure is comparable with that of other popular NAS approaches for the image classification task on CIFAR-10. The beneficial-performance and effectiveness of NASES was impressive even when only the architecture-embedding searching and pre-training controller were applied without other NAS tricks such as parameter sharing. Specifically, considerable reduction in searches was achieved by reducing the average number of searching to 100 architectures to achieve a final architecture for the NASES procedure. Introduction Deep neural networks have enabled advances in image recognition, sequential pattern recognition, recommendation systems, and various tasks in the past decades.
I have been around AMD for the past 20 years as a commercial customer, general consumer, and an analyst tracking the company and the industry around it. I've seen AMD transition from the X86 second source to the 64-bit innovator to the company struggling to survive. Now I see a company under Lisa Su that has reinvented itself and its products. Despite the company's ups and downs, a year ago was the only time that I truly had my doubts that the company would survive. Over the past 10 years, AMD has been plagued with products that were late or just not as competitive than they should be, ventures in new markets like gaming consoles and embedded that were either long to produce results or not enough to keep the company going, and the promise of new a new CPU architecture and products that was more than a year off.
Developing architectures for NN's is still in the early stages of development. The multi-network designs so far have been limited to narrowly defined concepts within a single domain. The design of a complex system such as a ship will first require extensive training in current designs and second, the ability to envision inter-processings between system components. The cutting edge in NN architectures is in massive NN's such as those done by Google for recognizing hand written digits and the "Borg Cube" NN architecture developed by Affectiva for recognizing image components in pictures . In Google's approach, a number of layers, each of a number of NN's were run through with the image data.
The use of automatic methods, often referred to as Neural Architecture Search (NAS), in designing neural network architectures has recently drawn considerable attention. In this work, we present an efficient NAS approach, named HM- NAS, that generalizes existing weight sharing based NAS approaches. Existing weight sharing based NAS approaches still adopt hand-designed heuristics to generate architecture candidates. As a consequence, the space of architecture candidates is constrained in a subset of all possible architectures, making the architecture search results sub-optimal. HM-NAS addresses this limitation via two innovations. First, HM-NAS incorporates a multi-level architecture encoding scheme to enable searching for more flexible network architectures. Second, it discards the hand-designed heuristics and incorporates a hierarchical masking scheme that automatically learns and determines the optimal architecture. Compared to state-of-the-art weight sharing based approaches, HM-NAS is able to achieve better architecture search performance and competitive model evaluation accuracy. Without the constraint imposed by the hand-designed heuristics, our searched networks contain more flexible and meaningful architectures that existing weight sharing based NAS approaches are not able to discover.