Difference between revisions of "Lifelong Learning"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
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| − | [http://www.youtube.com/results?search_query=Multi-model+Forgetting YouTube search...] | + | [http://www.youtube.com/results?search_query=Multi-model+Catastrophic+Forgetting YouTube search...] |
| − | [http://www.google.com/search?q=Multi-model+Forgetting ...Google search] | + | [http://www.google.com/search?q=Multi-model+Catastrophic+Forgetting ...Google search] |
| − | * [[ | + | * [[Automated Machine Learning (AML) - AutoML]] |
In recent years, researchers have developed deep neural networks that can perform a variety of tasks, including visual recognition and natural language processing (NLP) tasks. Although many of these models achieved remarkable results, they typically only perform well on one particular task due to what is referred to as "catastrophic forgetting." Essentially, catastrophic forgetting means that when a model that was initially trained on task A is later trained on task B, its performance on task A will significantly decline. [http://techxplore.com/news/2019-03-approach-multi-model-deep-neural-networks.html A new approach to overcome multi-model forgetting in deep neural networks | Ingrid Fadelli] | In recent years, researchers have developed deep neural networks that can perform a variety of tasks, including visual recognition and natural language processing (NLP) tasks. Although many of these models achieved remarkable results, they typically only perform well on one particular task due to what is referred to as "catastrophic forgetting." Essentially, catastrophic forgetting means that when a model that was initially trained on task A is later trained on task B, its performance on task A will significantly decline. [http://techxplore.com/news/2019-03-approach-multi-model-deep-neural-networks.html A new approach to overcome multi-model forgetting in deep neural networks | Ingrid Fadelli] | ||
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<youtube>5uQ0q0x_Xpk</youtube> | <youtube>5uQ0q0x_Xpk</youtube> | ||
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Revision as of 23:32, 13 March 2019
YouTube search... ...Google search
In recent years, researchers have developed deep neural networks that can perform a variety of tasks, including visual recognition and natural language processing (NLP) tasks. Although many of these models achieved remarkable results, they typically only perform well on one particular task due to what is referred to as "catastrophic forgetting." Essentially, catastrophic forgetting means that when a model that was initially trained on task A is later trained on task B, its performance on task A will significantly decline. A new approach to overcome multi-model forgetting in deep neural networks | Ingrid Fadelli
