Multi-attribute Recognition,the key to Universal Neural Network

<p>To achieve the recognition of multi-attribute of object, I redesign the mnist dataset, change the color, size, location of the number. Meanwhile, I change the label accordingly. The deep neural network I use is the most common convolution neural network. Through test,we can conclude that we can use one neural network to recognize multi-attribute so long as the attribute difference of objects can be represented by functions. The concrete network(generation network) can generate the output which the input rarely contained from the attributes the network learned. Its generalization ability is good because the network is a continuous function. Through one more test, We can conclude that one neural network can do image recognition, speech recognition,and nature language processing and other things so long as the output node and the input node and more parameters add into the network. The network is universal so long as the network can process different inputs. I guess that the phenomenon of synaesthesia is the result of multi-input and multi-output. I guess that connection in mind can realize through the universal network and sending the output into input.<b></b></p>

Multi-attribute Recognition,the key to Universal Neural Network

<p>To achieve the recognition of multi-attribute of object, I redesign the mnist dataset, change the color, size, location of the number. Meanwhile, I change the label accordingly. The deep neural network I use is the most common convolution neural network. Through test,we can conclude that we can use one neural network to recognize multi-attribute so long as the attribute difference of objects can be represented by functions. The concrete network(generation network) can generate the output which the input rarely contained from the attributes the network learned. Its generalization ability is good because the network is a continuous function. Through one more test, We can conclude that one neural network can do image recognition, speech recognition,and nature language processing and other things so long as the output node and the input node and more parameters add into the network. The network is universal so long as the network can process different inputs. I guess that the phenomenon of synaesthesia is the result of multi-input and multi-output. I guess that connection in mind can realize through the universal network and sending the output into input.<b></b></p>

Multi-attribute Recognition,The key to Universal Neural Network

<p><b>To achieve the recognition of multi-attribute of object, I redesign the mnist dataset, change the color, size, location of the number. Meanwhile, I change the label accordingly.</b><b> </b><b>The deep neural network I use is the most common convolution neural network. Through test,we can conclude that we can use one neural network to recognize multi-attribute so long as the attribute difference of objects can be represented by functions. The </b><b>c</b><b>oncrete network(generation network) can generate the output which the input rarely contained from the attributes the network learned. Its generalization ability is good because the network is a continuous function. Through one more test, We can conclude that one neural network can do image recognition, speech recognition,and nature language processing and other things so long as the output node and the input node and more parameters add into the network. The network is universal so long as the network can process different inputs.</b><b> I guess that t</b><b>he phenomenon of synaesthesia is the result of multi-input and multi-output. </b><b>I guess that c</b><b>onnection in mind can realize through the universal network and sending the output into input.</b><b></b></p>

Multi-attribute Recognition,The key to universal neural network

<p><b>To achieve the recognition of multi-attribute of object, I redesign the mnist dataset, change the color, size, location of the number. Meanwhile, I change the label accordingly.</b><b> </b><b>The deep neural network I use is the most common convolution neural network. Through test,we can conclude that we can use one neural network to recognize multi-attribute so long as the attribute difference of objects can be represented by functions. The </b><b>c</b><b>oncrete network(generation network) can generate the output which the input rarely contained from the attributes the network learned. Its generalization ability is good because the network is a continuous function. Through one more test, We can conclude that one neural network can do image recognition, speech recognition,and nature language processing and other things so long as the output node and the input node and more parameters add into the network. The network is universal so long as the network can process different inputs.</b><b> I guess that t</b><b>he phenomenon of synaesthesia is the result of multi-input and multi-output. </b><b>I guess that c</b><b>onnection in mind can realize through the universal network and sending the output into input.</b><b></b></p>

Multi-attribute Recognition,The key to universal neural network

<p><b>To achieve the recognition of multi-attribute of object, I redesign the mnist dataset, change the color, size, location of the number. Meanwhile, I change the label accordingly.</b><b> </b><b>The deep neural network I use is the most common convolution neural network. Through test,we can conclude that we can use one neural network to recognize multi-attribute so long as the attribute difference of objects can be represented by functions. The </b><b>c</b><b>oncrete network(generation network) can generate the output which the input rarely contained from the attributes the network learned. Its generalization ability is good because the network is a continuous function. Through one more test, We can conclude that one neural network can do image recognition, speech recognition,and nature language processing and other things so long as the output node and the input node and more parameters add into the network. The network is universal so long as the network can process different inputs.</b><b> I guess that t</b><b>he phenomenon of synaesthesia is the result of multi-input and multi-output. </b><b>I guess that c</b><b>onnection in mind can realize through the universal network and sending the output into input.</b><b></b></p>

Deep learning enabled inverse design in nanophotonics

Deep learning has become the dominant approach in artificial intelligence to solve complex data-driven problems. Originally applied almost exclusively in computer-science areas such as image analysis and nature language processing, deep learning has rapidly entered a wide variety of scientific fields including physics, chemistry and material science. Very recently, deep neural networks have been introduced in the field of nanophotonics as a powerful way of obtaining the nonlinear mapping between the topology and composition of arbitrary nanophotonic structures and their associated functional properties. In this paper, we have discussed the recent progress in the application of deep learning to the inverse design of nanophotonic devices, mainly focusing on the three existing learning paradigms of supervised-, unsupervised-, and reinforcement learning. Deep learning forward modelling i.e. how artificial intelligence learns how to solve Maxwell’s equations, is also discussed, along with an outlook of this rapidly evolving research area.