System and method for increasing accuracy of approximating query results using neural networks
A system and method for increasing the accuracy of generated approximations of query results. The method includes sending a received query to a first neural network and a second neural network; receiving from the first neural network a first predicted result to the received query; providing the first predicted result as a first output to a device having initiated the received query; receiving from the second neural network a second predicted result to the received query; and providing the second predicted result as a second output to the device having initiated the received query, wherein the first neural network requires less computational resources than the second neural network, and whereby the first output is provided before the second output.
1. A method for improving accuracy of generated approximations of query results, comprising:
sending a received query to a first neural network and a second neural network;
receiving from the first neural network a first predicted result to the received query;
determining a confidence level for the first predicted result, wherein the received query is sent to the second neural network only when the confidence level is below a first predefined threshold;
providing the first predicted result as a first output to a device having initiated the received query;
receiving from the second neural network a second predicted result to the received query; and
providing the second predicted result as a second output to the device having initiated the received query, wherein the first neural network requires less computational resources than the second neural network, and whereby the first output is provided before the second output.
2. The method of claim 1 , further comprising:
querying the database with the received query to retrieve a real result.
3. The method of claim 2 , wherein the second predicted result has a margin of error lower than a margin of error of the first predicted result.
4. The method of claim 2 , further comprising:
determining if the second predicted result exceeds a predetermined threshold of the real result.
5. The method of claim 4 , further comprising:
providing the real result as a third output, when the second predicted result exceeds the predetermined threshold of the real result.
6. The method of claim 4 , further comprising:
providing the second output when the second output exceeds the predetermined threshold.
7. The method of claim 1 , wherein the first neural network and the second neural network each include a plurality of neurons configured to generate the first predicted result and the second predicted result respectively, and wherein each neuron is assigned a weight value.
8. The method of claim 7 , wherein the second neural network includes more neurons than the first neural network.
9. The method of claim 1 , wherein the first predicted result and the second predicted result are provided without querying the database with the received query.
10. The method of claim 1 , wherein the confidence level is determined using a loss function.
11. The method of claim 1 , further comprising:
receiving the query and providing any of the first output, the second output, and the third output to any one of: a user node and an approximation server.
12. The method of claim 11 , wherein the approximation server is connected to a plurality of user devices and a plurality of neural networks.
13. A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to perform a process, the process comprising:
sending a received query to a first neural network and a second neural network;
receiving from the first neural network a first predicted result to the received query;
determining a confidence level for the first predicted result, wherein the received query is sent to the second neural network only when the confidence level is below a first predefined threshold;
providing the first predicted result as a first output to a device having initiated the received query;
receiving from the second neural network a second predicted result to the received query; and
providing the second predicted result as a second output to the device having initiated the received query, wherein the first neural network requires less computational resources than the second neural network, and whereby the first output is provided before the second output.
14. A system for increasing the accuracy of generated approximations of query results, comprising:
a processing circuitry; and
a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
send a received query to a first neural network and a second neural network;
receive from the first neural network a first predicted result to the received query;
determine a confidence level for the first predicted result, wherein the received query is sent to the second neural network only when the confidence level is below a first predefined threshold;
provide the first predicted result as a first output to a device having initiated the received query;
receive from the second neural network a second predicted result to the received query; and
provide the second predicted result as a second output to the device having initiated the received query, wherein the first neural network requires less computational resources than the second neural network, and whereby the first output is provided before the second output.
15. The system of claim 14 , wherein the system is further configured to:
query the database with the received query to retrieve a real result.
16. The system of claim 15 , wherein the second predicted result has a margin of error lower than a margin of error of the first predicted result.
17. The system of claim 15 , wherein the system is further configured to:
determine if the second predicted result exceeds a predetermined threshold of the real result.
18. The system of claim 17 , wherein the system is further configured to:
provide the real result as a third output, when the second predicted result exceeds the predetermined threshold of the real result.
19. The system of claim 17 , wherein the system is further configured to:
provide the second output when the second output exceeds the predetermined threshold.
20. The system of claim 14 , wherein the first neural network and the second neural network each include a plurality of neurons configured to generate the first predicted result and the second predicted result respectively, and wherein each neuron is assigned a weight value.
21. The system of claim 20 , wherein the second neural network includes more neurons than the first neural network.
22. The system of claim 14 , wherein the first predicted result and the second predicted result are provided without querying the database with the received query.
23. The system of claim 14 , wherein the confidence level is determined using a loss function.
24. The system of claim 14 , wherein the system is further configured to:
receive the query and providing any of the first output, the second output, and the third output to any one of: a user node and an approximation server.
25. The system of claim 24 , wherein the approximation server is connected to a plurality of user devices and a plurality of neural networks.