Fig. 5. The universal network
Thinking
I guess that the phenomenon of synesthesia is the result of multi-input
and multi-output, because I don’t have other sensory data. Because the
parameters of network shared by the multi-input and multi-output, so one
input may affect other output when the two outputs are the attributes of
some similar object, such as fire and sun which have red color and hot
attributes. For example, When we see red(vision input), we may feel
warm(warm sensation output), however When we see blue, we may feel cool.
Because we can also use the fully connected network to process sequence
data (The last paragraph is the proof)and nature language processing is
also represented by functions, so nature language processing can use the
same network as image recognition and speech recognition 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 the
different inputs. We can first send image or speech to the network and
the output is language or other information, then send the language or
information to the network’s input, process it or predict it. I guess
that connection in mind can realize through the universal network and
sending the output into input. For example, it’s cloudy now. This
information is sent to the network’s input, the predict output is there
will be a rain. This output is sent to network’s input, the predict
output is that I need an umbrella. Because the information about it’s
cloudy and there will be a rain is represented by image or audio or
language, so only universal network can do it. The most likelyhood event
can be seen as the effect of the cause. It’s causal reasoning.
Connection in mind is the key of creativity, synesthesia is the
assistant.
The Proof
The fully connected network can be expressed as that.
[1] (1)
(2)
(3)
has relations with because of . Meanwhile have relations with because of
. So we don’t need recurrent neural network. Because fully connected
network also can do what convolution neural network do, so fully
connected network is the universal network.
Conclusions
1.The abstract network(prediction network) achieves the intended
functionality of multi-attribute recognition through multi-dimension
regression. The Concrete 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.
2.We can use one neural network to do image recognition, speech
recognition, nature language processing and other things simultaneously
so long as the output node and the input node and more parameters add
into the network. The network is universal so long as it can process
different inputs. I guess that the phenomenon of synesthesia 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.
References
- Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT
Press, (2016)
- Andrew L. Maas, Awni Y. Hannum and Andrew Y. Ng, Rectified
Nonlinearities Improve Neural Network Acoustic Models, ICML, (2013)
- Tensorflow Tutorials and Apis, https://tensorflow.google.cn/learn last
accessed 2020/3/20
- Jin-xin Wei, Qun-ying Ren, A Functionally Separate
Autoencoder, unpublished