公开(公告)号：US11120337B2

公开(公告)日：2021-09-14

申请号：US15789628

申请日：2017-10-20

申请人： Dalei Wu ,  Md Akmal Haidar ,  Mehdi Rezagholizadeh ,  Alan Do-Omri

发明人： Dalei Wu ,  Md Akmal Haidar ,  Mehdi Rezagholizadeh ,  Alan Do-Omri

IPC分类号： G06N3/04 ,  G06N3/08

摘要： A method and system for augmenting a training dataset for a generative adversarial network (GAN). The training dataset includes labelled data samples and unlabelled data samples. The method includes: receiving generated samples generated using a first neural network of the GAN and the unlabelled samples of training dataset; determining a decision value for a sample from a decision function, wherein the sample is a generated sample of the generated samples or an unlabelled sample of the unlabelled samples of the training dataset; comparing the decision value to a threshold; in response to determining that the decision value exceeds the threshold: predicting a label for a sample; assigning the label to the sample; and augmenting the training dataset to include the sample with the assigned label as a labelled sample.

公开(公告)号：US20200097554A1

公开(公告)日：2020-03-26

申请号：US16143128

申请日：2018-09-26

申请人： Mehdi REZAGHOLIZADEH ,  Md Akmal HAIDAR ,  Alan DO-OMRI ,  Ahmad RASHID

发明人： Mehdi REZAGHOLIZADEH ,  Md Akmal HAIDAR ,  Alan DO-OMRI ,  Ahmad RASHID

IPC分类号： G06F17/28 ,  G06N3/08

摘要： In at least one broad aspect, described herein are systems and methods in which a latent representation shared between two languages is built and/or accessed, and then leveraged for the purpose of text generation in both languages. Neural text generation techniques are applied to facilitate text generation, and in particular the generation of sentences (i.e., sequences of words or subwords) in both languages, in at least some embodiments.

公开(公告)号：US11151334B2

公开(公告)日：2021-10-19

申请号：US16143128

申请日：2018-09-26

申请人： Mehdi Rezagholizadeh ,  Md Akmal Haidar ,  Alan Do-Omri ,  Ahmad Rashid

发明人： Mehdi Rezagholizadeh ,  Md Akmal Haidar ,  Alan Do-Omri ,  Ahmad Rashid

IPC分类号： G06F40/58 ,  G06N3/08

摘要： In at least one broad aspect, described herein are systems and methods in which a latent representation shared between two languages is built and/or accessed, and then leveraged for the purpose of text generation in both languages. Neural text generation techniques are applied to facilitate text generation, and in particular the generation of sentences (i.e., sequences of words or subwords) in both languages, in at least some embodiments.

公开(公告)号：US20190122120A1

公开(公告)日：2019-04-25

申请号：US15789628

申请日：2017-10-20

申请人： Dalei Wu ,  Md Akmal Haidar ,  Mehdi Rezagholizadeh ,  Alan Do-Omri

发明人： Dalei Wu ,  Md Akmal Haidar ,  Mehdi Rezagholizadeh ,  Alan Do-Omri

IPC分类号： G06N3/08 ,  G06N3/04

摘要： A method and system for augmenting a training dataset for a generative adversarial network (GAN). The training dataset includes labelled data samples and unlabelled data samples. The method includes: receiving generated samples generated using a first neural network of the GAN and the unlabelled samples of training dataset; determining a decision value for a sample from a decision function, wherein the sample is a generated sample of the generated samples or an unlabelled sample of the unlabelled samples of the training dataset; comparing the decision value to a threshold; in response to determining that the decision value exceeds the threshold: predicting a label for a sample; assigning the label to the sample; and augmenting the training dataset to include the sample with the assigned label as a labelled sample.