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公开(公告)号：US11928156B2
公开(公告)日：2024-03-12
申请号：US17088018
申请日：2020-11-03
发明人： Dakuo Wang , Lingfei Wu , Xuye Liu , Yi Wang , Chuang Gan , Jing Xu , Xue Ying Zhang , Jun Wang , Jing James Xu
IPC分类号： G06N3/08 , G06F16/901 , G06F16/9032 , G06F16/955 , G06F40/211
CPC分类号： G06F16/90332 , G06F16/9024 , G06F16/9558 , G06F40/211 , G06N3/08
摘要： Obtain, at a computing device, a segment of computer code. With a classification module of a machine learning system executing on the computing device, determine a required annotation category for the segment of computer code. With an annotation generation module of the machine learning system executing on the computing device, generate a natural language annotation of the segment of computer code based on the segment of computer code and the required annotation category. Provide the natural language annotation to a user interface for display adjacent the segment of computer code.
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公开(公告)号：US11763084B2
公开(公告)日：2023-09-19
申请号：US16989882
申请日：2020-08-10
发明人： Dakuo Wang , Arunima Chaudhary , Chuang Gan , Mo Yu , Qian Pan , Sijia Liu , Daniel Karl I. Weidele , Abel Valente
IPC分类号： G06F40/289 , G06N20/00 , G06N5/04
CPC分类号： G06F40/289 , G06N5/04 , G06N20/00
摘要： A method comprises receiving a new data set; identifying at least one prior data set of a plurality of prior data sets that matches the new data set; generating a natural language data science problem statement for the new data set based on information associated with the at least prior one data set that matches the new data set; outputting the generated natural language data science problem statement for user verification; and in response to receiving user input verifying the natural language generated data science problem statement, generating one or more AutoAI configuration settings for the new data set based on one or more AutoAI configuration settings associated with the at least one prior data set that matches the new data set.
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公开(公告)号：US20230153634A1
公开(公告)日：2023-05-18
申请号：US17525932
申请日：2021-11-14
发明人： Dakuo Wang , Udayan Khurana , Chuang Gan , Gregory Bramble , Abel Valente , Arunima Chaudhary , Carolina Maria Spina , Micah Smith
摘要： A domain of an input dataset is identified and one or more archived domain knowledge features corresponding to the identified domain are identified. One or more user feature definitions for one or more user features defined by a user are inputted. The identified archived domain knowledge features and the user features are processed to generate a set of candidate features for presentation to the user. A selection of a subset of the candidate features is obtained from the user and one or more predictive models are generated based on the selected features.
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公开(公告)号：US20220358851A1
公开(公告)日：2022-11-10
申请号：US17302550
申请日：2021-05-06
发明人： Dakuo Wang , Mo Yu , Chuang Gan , Saloni Potdar
摘要： In an approach to generating question answer pairs, one or more computer processors receive a corpus of text. One or more computer processors extract one or more key concepts from the corpus of text. Based on the one or more key concepts, one or more computer processors generate one or more questions associated with the key concepts, where the one or more key concepts are answers to the one or more generated questions. One or more computer processors display the one or more generated questions and the answers to the one or more generated questions.
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公开(公告)号：US20220300821A1
公开(公告)日：2022-09-22
申请号：US17207664
申请日：2021-03-20
发明人： Dakuo Wang , Kiran A. Kate , Arunima Chaudhary , Abel Valente , Ioannis Katsis , Chuang Gan , Bei Chen
摘要： A computer-implemented method of automatically generating a machine learning model includes identifying one or more visualization features of a dataset associated with a machine learning model selection process. A plurality of candidate machine learning pipelines are configured to perform respective optimizing strategies in parallel based on the identified visualization features. A machine learning model is automatically generated based on at least one of the generated candidate machine learning pipelines.
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公开(公告)号：US11436528B2
公开(公告)日：2022-09-06
申请号：US16543117
申请日：2019-08-16
发明人： Haoyu Wang , Ming Tan , Dakuo Wang , Chuang Gan , Saloni Potdar
摘要： A method includes determining, based on an input data sample, a set of probabilities. Each probability of the set of probabilities is associated with a respective label of a set of labels. A particular probability associated with a particular label indicates an estimated likelihood that the input data sample is associated with the particular label. The method includes modifying the set of probabilities based on a set of adjustment factors to generate a modified set of probabilities. The set of adjustment factors is based on a first relative frequency distribution and a second relative frequency distribution. The first relative frequency distribution indicates for each label of the set of labels, a frequency of occurrence of the label among training data. The second relative frequency distribution indicates for each label of the set of labels, a frequency of occurrence of the label among post-training data provided to the trained classifier.
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公开(公告)号：US20220138266A1
公开(公告)日：2022-05-05
申请号：US17088018
申请日：2020-11-03
发明人： Dakuo Wang , Lingfei Wu , Xuye Liu , Yi Wang , Chuang Gan , Jing Xu , Xue Ying Zhang , Jun Wang
IPC分类号： G06F16/9032 , G06N3/08 , G06F16/955 , G06F16/901 , G06F40/211
摘要： Obtain, at a computing device, a segment of computer code. With a classification module of a machine learning system executing on the computing device, determine a required annotation category for the segment of computer code. With an annotation generation module of the machine learning system executing on the computing device, generate a natural language annotation of the segment of computer code based on the segment of computer code and the required annotation category. Provide the natural language annotation to a user interface for display adjacent the segment of computer code.
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公开(公告)号：US20220101120A1
公开(公告)日：2022-03-31
申请号：US17039989
申请日：2020-09-30
发明人： Dakuo Wang , Sijia Liu , Abel Valente , Chuang Gan , Bei Chen , Dongyu Liu , Yi Sun
摘要： Use a computerized trained graph neural network model to classify an input instance with a predicted label. With a computerized graph neural network interpretation module, compute a gradient-based saliency matrix based on the input instance and the predicted label, by taking a partial derivative of class prediction with respect to an adjacency matrix of the model. With a computerized user interface, obtain user input responsive to the gradient-based saliency matrix. Optionally, modify the trained graph neural network model based on the user input; and re-classify the input instance with a new predicted label based on the modified trained graph neural network model.
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公开(公告)号：US11276419B2
公开(公告)日：2022-03-15
申请号：US16526990
申请日：2019-07-30
发明人： Yang Zhang , Chuang Gan , Sijia Liu , Dakuo Wang
IPC分类号： G10L25/57 , G10L25/30 , G06N3/04 , H04N21/81 , H04N21/845
摘要： A computing device receives a video feed. The video feed is divided into a sequence of video segments. For each video segment, visual features of the video segment are extracted. A predicted spectrogram is generated based on the extracted visual features. A synthetic audio waveform is generated from the predicted spectrogram. All synthetic audio waveforms of the video feed are concatenated to generate a synthetic soundtrack that is synchronized with the video feed.
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公开(公告)号：US20220076144A1
公开(公告)日：2022-03-10
申请号：US17015243
申请日：2020-09-09
发明人： Parikshit Ram , Dakuo Wang , Deepak Vijaykeerthy , Vaibhav Saxena , Sijia Liu , Arunima Chaudhary , Gregory Bramble , Horst Cornelius Samulowitz , Alexander Gray
摘要： The exemplary embodiments disclose a method, a computer program product, and a computer system for determining that one or more model pipelines satisfy one or more constraints. The exemplary embodiments may include detecting a user uploading data, one or more constraints, and one or more model pipelines, collecting the data, the one or more constraints, and the one or more model pipelines, and determining that one or more of the model pipelines satisfies all of the one or more constraints based on applying one or more algorithms to the collected data, constraints, and model pipelines.
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