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pro vyhledávání: '"Han, Fred X."'
Autor:
Mills, Keith G., Han, Fred X., Salameh, Mohammad, Lu, Shengyao, Zhou, Chunhua, He, Jiao, Sun, Fengyu, Niu, Di
Neural Architecture Search is a costly practice. The fact that a search space can span a vast number of design choices with each architecture evaluation taking nontrivial overhead makes it hard for an algorithm to sufficiently explore candidate netwo
Externí odkaz:
http://arxiv.org/abs/2403.13293
Autor:
Han, Fred X., Mills, Keith G., Chudak, Fabian, Riahi, Parsa, Salameh, Mohammad, Zhang, Jialin, Lu, Wei, Jui, Shangling, Niu, Di
Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS). Performance predictors have seen widespread use in low-cost NAS and achieve high ranking correlations between predicted and ground truth p
Externí odkaz:
http://arxiv.org/abs/2302.10835
Autor:
Mills, Keith G., Niu, Di, Salameh, Mohammad, Qiu, Weichen, Han, Fred X., Liu, Puyuan, Zhang, Jialin, Lu, Wei, Jui, Shangling
Evaluating neural network performance is critical to deep neural network design but a costly procedure. Neural predictors provide an efficient solution by treating architectures as samples and learning to estimate their performance on a given task. H
Externí odkaz:
http://arxiv.org/abs/2211.17228
Autor:
Mills, Keith G., Han, Fred X., Zhang, Jialin, Chudak, Fabian, Mamaghani, Ali Safari, Salameh, Mohammad, Lu, Wei, Jui, Shangling, Niu, Di
Predicting neural architecture performance is a challenging task and is crucial to neural architecture design and search. Existing approaches either rely on neural performance predictors which are limited to modeling architectures in a predefined des
Externí odkaz:
http://arxiv.org/abs/2211.17226
Autor:
Mills, Keith G., Han, Fred X., Zhang, Jialin, Rezaei, Seyed Saeed Changiz, Chudak, Fabian, Lu, Wei, Lian, Shuo, Jui, Shangling, Niu, Di
Neural architecture search automates neural network design and has achieved state-of-the-art results in many deep learning applications. While recent literature has focused on designing networks to maximize accuracy, little work has been conducted to
Externí odkaz:
http://arxiv.org/abs/2109.12426
Autor:
Mills, Keith G., Han, Fred X., Salameh, Mohammad, Rezaei, Seyed Saeed Changiz, Kong, Linglong, Lu, Wei, Lian, Shuo, Jui, Shangling, Niu, Di
Neural architecture search (NAS) has achieved remarkable results in deep neural network design. Differentiable architecture search converts the search over discrete architectures into a hyperparameter optimization problem which can be solved by gradi
Externí odkaz:
http://arxiv.org/abs/2109.12425
Autor:
Rezaei, Seyed Saeed Changiz, Han, Fred X., Niu, Di, Salameh, Mohammad, Mills, Keith, Lian, Shuo, Lu, Wei, Jui, Shangling
Despite the empirical success of neural architecture search (NAS) in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to assess. In this paper, we propose Generative Adversarial NAS (GA-NAS) with theoret
Externí odkaz:
http://arxiv.org/abs/2105.09356
Semantic matching of natural language sentences or identifying the relationship between two sentences is a core research problem underlying many natural language tasks. Depending on whether training data is available, prior research has proposed both
Externí odkaz:
http://arxiv.org/abs/1803.00179
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