A method of biasing a deep neural network includes determining whether an element has an increased probability of being present in an input to the network. The method also includes adjusting a bias of activation functions of neurons in the network to increase sensitivity to the element. In one configuration, the bias is adjusted without adjusting weights of the network. The method further includes adjusting an output of the network based on the biasing.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of biasing a deep neural network, comprising: determining whether an element has an increased probability of being present in an input to the network; adjusting a bias of activation functions of neurons in the network to increase sensitivity to the element, the bias being adjusted without adjusting weights of the network; and adjusting an output of the network based at least in part on the biasing. 2. The method of claim 1, further comprising determining an amount of adjustment for the bias as a constant, function of a synaptic weight, or function of activations in response to presentation of a target class. 3. The method of claim 1, in which adjusting the bias comprises scaling the bias. 4. The method of claim 3, in which a scale of the adjusting is based at least in part on a priori knowledge of what is likely to be present in the input and/or what is not likely to be present in the input. 5. The method of claim 1, in which the adjusting is performed at an internal level of the network. 6. An apparatus for biasing a deep neural network, comprising: means for determining whether an element has an increased probability of being present in an input to the network; means for adjusting a bias of activation functions of neurons in the network to increase sensitivity to the element, the bias being adjusted without adjusting weights of the network; and means for adjusting an output of the network based at least in part on the biasing. 7. The apparatus of claim 6, further comprising means for determining an amount of adjustment for the bias as a constant, function of a synaptic weight, or function of activations in response to presentation of a target class. 8. The apparatus of claim 6, in which the means for adjusting the bias comprises means for scaling the bias. 9. The apparatus of claim 8, in which a scale of the adjusting is based at least in part on a priori knowledge of what is likely to be present in the input and/or what is not likely to be present in the input. 10. The apparatus of claim 6, in which the means for adjusting is performed at an internal level of the network. 11. An apparatus for biasing a deep neural network, comprising: a memory unit; and at least one processor coupled to the memory unit, the at least one processor configured: to determine whether an element has an increased probability of being present in an input to the network; to adjust a bias of activation functions of neurons in the network to increase sensitivity to the element, the bias being adjusted without adjusting weights of the network; and to adjust an output of the network based at least in part on the biasing. 12. The apparatus of claim 11, in which the at least one processor is further configured to determine an amount of adjustment for the bias as a constant, function of a synaptic weight, or function of activations in response to presentation of a target class. 13. The apparatus of claim 11, in which the at least one processor is further configured to adjust the bias by scaling the bias. 14. The apparatus of claim 13, in which a scale of the adjusting is based at least in part on a priori knowledge of what is likely to be present in the input and/or what is not likely to be present in the input. 15. The apparatus of claim 11, in which the at least one processor is further configured to adjust the bias at an internal level of the network. 16. A non-transitory computer-readable medium having program code recorded thereon, the program code being executed by a processor and comprising: program code to determine whether an element has an increased probability of being present in an input to a network; program code to adjust a bias of activation functions of neurons in the network to increase sensitivity to the element, the bias being adjusted without adjusting weights of the network; and program code to adjust an output of the network based at least in part on the biasing. 17. The computer-readable medium of claim 16, in which the program code further comprises program code to determine an amount of adjustment for the bias as a constant, function of a synaptic weight, or function of activations in response to presentation of a target class. 18. The computer-readable medium of claim 16, in which the program code to adjust the bias comprises program code to adjust the bias by scaling the bias. 19. The computer-readable medium of claim 18, in which a scale of the adjusting is based at least in part on a priori knowledge of what is likely to be present in the input and/or what is not likely to be present in the input. 20. The computer-readable medium of claim 16, in which the program code to adjust the bias comprises program code to adjust the bias at an internal level of the network.