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公开(公告)号：US11087177B2
公开(公告)日：2021-08-10
申请号：US16176075
申请日：2018-10-31
Applicant: salesforce.com, inc.
Inventor： Lily Hu , Caiming Xiong , Richard Socher
Abstract: Approaches to zero-shot learning include partitioning training data into first and second sets according to classes assigned to the training data, training a prediction module based on the first set to predict a cluster center based on a class label, training a correction module based on the second set and each of the class labels in the first set to generate a correction to a cluster center predicted by the prediction module, presenting a new class label for a new class to the prediction module to predict a new cluster center, presenting the new class label, the predicted new cluster center, and each of the class labels in the first set to the correction module to generate a correction for the predicted new cluster center, augmenting a classifier based on the corrected cluster center for the new class, and classifying input data into the new class using the classifier.
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公开(公告)号：US20210174204A1
公开(公告)日：2021-06-10
申请号：US17093478
申请日：2020-11-09
Applicant: salesforce.com, inc.
Inventor： Wenpeng Yin , Nazneen Rajani , Richard Socher , Caiming Xiong
IPC: G06N3/08 , G06F16/332 , G06F16/33 , G06F40/279 , G06F40/30
Abstract: A method for using a neural network model for natural language processing (NLP) includes receiving training data associated with a source domain and a target domain; and generating one or more query batches. Each query batch includes one or more source tasks associated with the source domain and one or more target tasks associated with the target domain. For each query batch, class representations are generated for each class in the source domain and the target domain. A query batch loss for the query batch is generated based on the corresponding class representations. An optimization is performed on the neural network model by adjusting its network parameters based on the query batch loss. The optimized neural network model is used to perform one or more new NLP tasks.
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公开(公告)号：US10902289B2
公开(公告)日：2021-01-26
申请号：US16394992
申请日：2019-04-25
Applicant: salesforce.com, inc.
Inventor： Mingfei Gao , Richard Socher , Caiming Xiong
Abstract: Embodiments described herein provide a two-stage online detection of action start system including a classification module and a localization module. The classification module generates a set of action scores corresponding to a first video frame from the video, based on the first video frame and video frames before the first video frames in the video. Each action score indicating a respective probability that the first video frame contains a respective action class. The localization module is coupled to the classification module for receiving the set of action scores from the classification module and generating an action-agnostic start probability that the first video frame contains an action start. A fusion component is coupled to the localization module and the localization module for generating, based on the set of action scores and the action-agnostic start probability, a set of action-specific start probabilities, each action-specific start probability corresponding to a start of an action belonging to the respective action class.
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公开(公告)号：US20200302236A1
公开(公告)日：2020-09-24
申请号：US16394992
申请日：2019-04-25
Applicant: Salesforce.com, Inc,
Inventor： Mingfei Gao , Richard Socher , Caiming Xiong
Abstract: Embodiments described herein provide a two-stage online detection of action start system including a classification module and a localization module. The classification module generates a set of action scores corresponding to a first video frame from the video, based on the first video frame and video frames before the first video frames in the video. Each action score indicating a respective probability that the first video frame contains a respective action class. The localization module is coupled to the classification module for receiving the set of action scores from the classification module and generating an action-agnostic start probability that the first video frame contains an action start. A fusion component is coupled to the localization module and the localization module for generating, based on the set of action scores and the action-agnostic start probability, a set of action-specific start probabilities, each action-specific start probability corresponding to a start of an action belonging to the respective action class.
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95.发明授权公开(公告)号：US10783875B2
公开(公告)日：2020-09-22
申请号：US16027111
申请日：2018-07-03
Applicant: salesforce.com, inc.
Inventor： Ehsan Hosseini-Asl , Caiming Xiong , Yingbo Zhou , Richard Socher
Abstract: A system for domain adaptation includes a domain adaptation model configured to adapt a representation of a signal in a first domain to a second domain to generate an adapted presentation and a plurality of discriminators corresponding to a plurality of bands of values of a domain variable. Each of the plurality of discriminators is configured to discriminate between the adapted representation and representations of one or more other signals in the second domain.
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Patent Agency Ranking
公开(公告)号：US10747761B2
公开(公告)日：2020-08-18
申请号：US15885613
申请日：2018-01-31
Applicant: salesforce.com, inc.
Inventor： Victor Zhong , Caiming Xiong , Richard Socher
IPC: G06F16/2452 , G06N3/08 , G06N7/00 , G06N3/04 , G06N3/00 , G06F16/13 , G06F16/2457
Abstract: A computing system uses neural networks to translate natural language queries to database queries. The computing system uses a plurality of machine learning based models, each machine learning model for generating a portion of the database query. The machine learning models use an input representation generated based on terms of the input natural language query, a set of columns of the database schema, and the vocabulary of a database query language, for example, structured query language SQL. The plurality of machine learning based models may include an aggregation classifier model for determining an aggregation operator in the database query, a result column predictor model for determining the result columns of the database query, and a condition clause predictor model for determining the condition clause of the database query. The condition clause predictor is based on reinforcement learning.
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公开(公告)号：US10558750B2
公开(公告)日：2020-02-11
申请号：US15817153
申请日：2017-11-17
Applicant: salesforce.com, inc.
Inventor： Jiasen Lu , Caiming Xiong , Richard Socher
Abstract: The technology disclosed presents a novel spatial attention model that uses current hidden state information of a decoder long short-term memory (LSTM) to guide attention and to extract spatial image features for use in image captioning. The technology disclosed also presents a novel adaptive attention model for image captioning that mixes visual information from a convolutional neural network (CNN) and linguistic information from an LSTM. At each timestep, the adaptive attention model automatically decides how heavily to rely on the image, as opposed to the linguistic model, to emit the next caption word. The technology disclosed further adds a new auxiliary sentinel gate to an LSTM architecture and produces a sentinel LSTM (Sn-LSTM). The sentinel gate produces a visual sentinel at each timestep, which is an additional representation, derived from the LSTM's memory, of long and short term visual and linguistic information.
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公开(公告)号：US10542270B2
公开(公告)日：2020-01-21
申请号：US15874515
申请日：2018-01-18
Applicant: salesforce.com, inc.
Inventor： Yingbo Zhou , Luowei Zhou , Caiming Xiong , Richard Socher
IPC: H04N7/12 , H04N11/12 , H04N19/46 , H04N19/44 , H04N19/60 , H04N19/187 , H04N21/81 , H04N19/33 , H04N19/126 , H04N21/488 , H04N19/132
Abstract: Systems and methods for dense captioning of a video include a multi-layer encoder stack configured to receive information extracted from a plurality of video frames, a proposal decoder coupled to the encoder stack and configured to receive one or more outputs from the encoder stack, a masking unit configured to mask the one or more outputs from the encoder stack according to one or more outputs from the proposal decoder, and a decoder stack coupled to the masking unit and configured to receive the masked one or more outputs from the encoder stack. Generating the dense captioning based on one or more outputs of the decoder stack. In some embodiments, the one or more outputs from the proposal decoder include a differentiable mask. In some embodiments, during training, error in the dense captioning is back propagated to the decoder stack, the encoder stack, and the proposal decoder.
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公开(公告)号：US20190258939A1
公开(公告)日：2019-08-22
申请号：US15980207
申请日：2018-05-15
Applicant: salesforce.com, inc.
Inventor： Sewon Min , Victor Zhong , Caiming Xiong , Richard Socher
Abstract: A natural language processing system that includes a sentence selector and a question answering module. The sentence selector receives a question and sentences that are associated with a context. For a question and each sentence, the sentence selector determines a score. A score represents whether the question is answerable with the sentence. Sentence selector then generates a minimum set of sentences from the scores associated with the question and sentences. The question answering module generates an answer for the question from the minimum set of sentences.
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公开(公告)号：US20180336198A1
公开(公告)日：2018-11-22
申请号：US15885613
申请日：2018-01-31
Applicant: salesforce.com, inc.
Inventor： Victor Zhong , Caiming Xiong , Richard Socher
Abstract: A computing system uses neural networks to translate natural language queries to database queries. The computing system uses a plurality of machine learning based models, each machine learning model for generating a portion of the database query. The machine learning models use an input representation generated based on terms of the input natural language query, a set of columns of the database schema, and the vocabulary of a database query language, for example, structured query language SQL. The plurality of machine learning based models may include an aggregation classifier model for determining an aggregation operator in the database query, a result column predictor model for determining the result columns of the database query, and a condition clause predictor model for determining the condition clause of the database query. The condition clause predictor is based on reinforcement learning.
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