Difference between revisions of "Neural Architecture"
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| + | = <span id="Neural Operator"></span>Neural Operator = | ||
| + | [http://www.youtube.com/results?search_query=Neural+Operator+machine+learning+AI YouTube search...] | ||
| + | [http://www.google.com/search?q=Neural+Operator+machine+learning+AI ...Google search] | ||
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| + | convovution fyrrirr transform non linear signal processing | ||
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| + | * [https://arxiv.org/abs/2108.08481 Neural Operator: Learning Maps Between Function Spaces | N. Kovachki, Z. Li, B. Liu, K. Azizzadenesheli, K. Bhattacharya, A. Stuart, [[Creatives#Anima Anandkumar | Anima Anandkumar]] | ||
Revision as of 13:29, 4 February 2023
YouTube search... ...Google search
- Hierarchical Temporal Memory (HTM)
- MIT’s AI can train neural networks faster than ever before | Christine Fisher - Engadget
- Other codeless options, Code Generators, Drag n' Drop
- Automated Learning
- Auto Keras
- Evolutionary Computation / Genetic Algorithms
- Hyperparameters Optimization
- Model Search
- Google AutoML
Neural Architecture Search (NAS)
YouTube search... ...Google search
- Literature on Neural Architecture Search | AutoML.org
- Awesome NAS; a curated list
- Neural Architecture Search (NAS) with Reinforcement Learning | Wikipedia
- Neural Architecture Search (NAS) with Evolution | Wikipedia
- Multi-objective Neural architecture search | Wikipedia
- Neural Architecture Search for Deep Face Recognition | Ning Zhu
An alternative to manual design is “neural architecture search” (NAS), a series of machine learning techniques that can help discover optimal neural networks for a given problem. Neural architecture search is a big area of research and holds a lot of promise for future applications of deep learning. * Need to find the best AI model for your problem? Try neural architecture search | Ben Dickson - TDW NAS algorithms are efficient problem solvers ... What is neural architecture search (NAS)? | Ben Dickson - TechTalks
Various approaches to Neural Architecture Search (NAS) have designed networks that are on par or even outperform hand-designed architectures. Methods for NAS can be categorized according to the search space, search strategy and performance estimation strategy used:
- The search space defines which type of ANN can be designed and optimized in principle.
- The search strategy defines which strategy is used to find optimal ANN's within the search space.
- Obtaining the performance of an ANN is costly as this requires training the ANN first. Therefore, performance estimation strategies are used obtain less costly estimates of a model's performance. Neural Architecture Search | Wikipedia
Differentiable Neural Computer (DNC)
Neural Operator
YouTube search... ...Google search
convovution fyrrirr transform non linear signal processing
- [https://arxiv.org/abs/2108.08481 Neural Operator: Learning Maps Between Function Spaces | N. Kovachki, Z. Li, B. Liu, K. Azizzadenesheli, K. Bhattacharya, A. Stuart, Anima Anandkumar
