Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Modern convolutional networks, incorporating rectifiers and max-pooling, are neither smooth nor convex. Standard guarantees therefore do not apply. Nevertheless, methods from convex optimization such as gradient descent and Adam are widely used as building blocks for deep learning algorithms.  provides the first convergence guarantee applicable to modern convnets. The guarantee matches a lor bound for convex nonsmooth functions. The key technical tool is the neural Taylor approximation -- a straightforward application of Taylor expansions to neural networks -- and the associated Taylor loss. Experiments on a range of optimizers, layers, and tasks provide evidence that the analysis accurately captures the dynamics of neural optimization. #R##N#The second half of the paper applies the Taylor approximation to isolate the main difficulty in training rectifier nets: that gradients are shattered.  investigate the hypothesis that, by exploring the space of activation configurations more thoroughly, adaptive optimizers such as RMSProp and Adam are able to converge to better solutions.
['David Balduzzi', 'Brian McWilliams', 'Tony Butler-Yeoman']
I am writing a research paper on a idea Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks
[{"from": "human", "value": "I am writing a research paper on a idea Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Modern convolutional networks, incorporating rectifiers and max-pooling, are neither smooth nor convex. Standard guarantees therefore do not apply. Nevertheless, methods from convex optimization such as gradient descent and Adam are widely used as building blocks for deep learning algorithms.  provides the first convergence guarantee applicable to modern convnets. The guarantee matches a lor bound for convex nonsmooth functions. The key technical tool is the neural Taylor approximation -- a straightforward application of Taylor expansions to neural networks -- and the associated Taylor loss. Experiments on a range of optimizers, layers, and tasks provide evidence that the analysis accurately captures the dynamics of neural optimization. #R##N#The second half of the paper applies the Taylor approximation to isolate the main difficulty in training rectifier nets: that gradients are shattered.  investigate the hypothesis that, by exploring the space of activation configurations more thoroughly, adaptive optimizers such as RMSProp and Adam are able to converge to better solutions."}]