-
公开(公告)号：US20150269595A1
公开(公告)日：2015-09-24
申请号：US14486111
申请日：2014-09-15
Applicant: salesforce.com, inc.
Inventor： Arun Jagota , Gregory Haardt , Govardana Sachithanandam Ramachandran , Lei Ming , Matthew Fuchs , George Vitchev , Fang Wong
IPC: G06Q30/02
CPC classification number: G06Q30/0202 , G06Q10/06315 , G06Q10/067 , G06Q10/103 , G06Q10/105 , G06Q30/00 , G06Q30/02 , G06Q30/0235 , G06Q30/0255 , G06Q30/06 , G06Q30/0631 , G06Q40/00
Abstract: Contact recommendations based on purchase history are described. A system creates a directed graph of nodes in which at least some of the nodes are connected by directed arcs, wherein a directed arc from a first node to a second node represents a conditional probability that previous users who purchased a first contact also purchased a second contact. The system identifies a set of contacts purchased by a current user. The system estimates a prospective purchase probability based on a historical probability that previous users purchased a specific contact and a related probability that previous users who purchased the specific contact also purchased a contact in the set of contacts, for each candidate contact. The system outputs a recommendation for the current user to purchase a recommended candidate contact based on a corresponding prospective purchase probability.
Abstract translation: 描述了基于购买历史的联系建议。 系统创建节点的有向图，其中至少一些节点通过定向弧连接，其中从第一节点到第二节点的有向弧表示先前用户购买第一个联系人的条件概率也购买了第二个节点 联系。 该系统识别当前用户购买的一组联系人。 该系统基于以前用户购买特定联系人的历史概率以及购买特定联系人的先前用户也为每个候选联系人购买联系人的联系人的相关概率来估计预期购买概率。 该系统基于相应的预期购买概率输出针对当前用户购买推荐候选联系人的建议。
-
公开(公告)号：US20230113750A1
公开(公告)日：2023-04-13
申请号：US17498155
申请日：2021-10-11
Applicant: salesforce.com, inc.
Abstract: A system performs group testing on a population of items. The group testing identifies items satisfying particular criteria from a population of items, for example, defective items from the population. The group testing may be performed for software or hardware testing, for testing a human population, for training of deep learning applications, and so on. The system trains a machine learning based model, for example, a reinforcement learning based model to evaluate groups. The model may further determine system dynamics that may represent priors of items. An agent treats the population and groups of items being tested as the environment and performs actions, for example, adjusting the groups. The system also performs a non-adaptive strategy based on monte carlo simulation of tests based on a simulation results.
-
Patent Agency Ranking
公开(公告)号：US11537899B2
公开(公告)日：2022-12-27
申请号：US16877333
申请日：2020-05-18
Applicant: salesforce.com, inc.
Inventor： Govardana Sachithanandam Ramachandran , Ka Chun Au , Shashank Harinath , Wenhao Liu , Alexis Roos , Caiming Xiong
Abstract: An embodiment proposed herein uses sparsification techniques to train the neural network with a high feature dimension that may yield desirable in-domain detection accuracy but may prune away dimensions in the output that are less important. Specifically, a sparsification vector is generated based on Gaussian distribution (or other probabilistic distribution) and is used to multiply with the higher dimension output to reduce the number of feature dimensions. The pruned output may be then used for the neural network to learn the sparsification vector. In this way, out-of-distribution detection accuracy can be improved.
-
公开(公告)号：US20210256370A1
公开(公告)日：2021-08-19
申请号：US16950853
申请日：2020-11-17
Applicant: salesforce.com, inc.
Inventor： Govardana Sachithanandam Ramachandran , Ivan BRUGERE , Lav Varshney , Caiming Xiong
Abstract: A method for using a neural network to generate an improved graph model includes receiving, by the neural network, a graph model. The graph model is based on data relating to an environment for allocating resources to a first group and a second group. The method further includes receiving, by the neural network, a budget for editing the graph model based on a cost of corresponding modification to the environment, and determining, by the neural network, a fairness representation based on a fairness requirement between the first and second groups. It is determined by the neural network, a utility function for the graph model based on first and second group utilities representing resource allocation to the first and second groups respectively. Reinforcement learning is performed on the neural network to generate the improved graph model using the utility function and the fairness representation.
-
公开(公告)号：US20210150366A1
公开(公告)日：2021-05-20
申请号：US16877333
申请日：2020-05-18
Applicant: salesforce.com, inc.
Inventor： Govardana Sachithanandam Ramachandran , Ka Chun Au , Shashank Harinath , Wenhao Liu , Alexis Roos , Caiming Xiong
Abstract: An embodiment proposed herein uses sparsification techniques to train the neural network with a high feature dimension that may yield desirable in-domain detection accuracy but may prune away dimensions in the output that are less important. Specifically, a sparsification vector is generated based on Gaussian distribution (or other probabilistic distribution) and is used to multiply with the higher dimension output to reduce the number of feature dimensions. The pruned output may be then used for the neural network to learn the sparsification vector. In this way, out-of-distribution detection accuracy can be improved.
-
公开(公告)号：US20210150340A1
公开(公告)日：2021-05-20
申请号：US16877339
申请日：2020-05-18
Applicant: salesforce.com, inc.
Inventor： Wenhao Liu , Ka Chun Au , Shashank Harinath , Bryan McCann , Govardana Sachithanandam Ramachandran , Alexis Roos , Caiming Xiong
Abstract: Embodiments described herein provides a training mechanism that transfers the knowledge from a trained BERT model into a much smaller model to approximate the behavior of BERT. Specifically, the BERT model may be treated as a teacher model, and a much smaller student model may be trained using the same inputs to the teacher model and the output from the teacher model. In this way, the student model can be trained within a much shorter time than the BERT teacher model, but with comparable performance with BERT.
-
公开(公告)号：US10354264B2
公开(公告)日：2019-07-16
申请号：US14486111
申请日：2014-09-15
Applicant: salesforce.com, inc.
Inventor： Arun Jagota , Gregory Haardt , Govardana Sachithanandam Ramachandran , Lei Ming , Matthew Fuchs , George Vitchev , Fang Wong
Abstract: Contact recommendations based on purchase history are described. A system creates a directed graph of nodes in which at least some of the nodes are connected by directed arcs, wherein a directed arc from a first node to a second node represents a conditional probability that previous users who purchased a first contact also purchased a second contact. The system identifies a set of contacts purchased by a current user. The system estimates a prospective purchase probability based on a historical probability that previous users purchased a specific contact and a related probability that previous users who purchased the specific contact also purchased a contact in the set of contacts, for each candidate contact. The system outputs a recommendation for the current user to purchase a recommended candidate contact based on a corresponding prospective purchase probability.
-
-
-
-
-
-
Search Results
17
records
(took 0.017 seconds)
