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公开(公告)号：US20230161629A1
公开(公告)日：2023-05-25
申请号：US17534385
申请日：2021-11-23
发明人： Surya Shravan Kumar Sajja , Kanthi Sarpatwar , Lam Minh Nguyen , Yuan Yuan Jia , Stephane Michel , Roman Vaculin
CPC分类号： G06F9/5038 , G06F9/5088 , G06N7/005 , G06F9/5033 , G06F2209/504 , G06F2209/508
摘要： A computer implemented method using an artificial intelligence (A.I.) module to explain large scale scheduling solutions includes receiving an original instance of a resource constrained scheduling problem. The instance includes a set of tasks and a variety of resource requirements and a variety of constraints. An optimizer process determines a schedule for the set of tasks while minimizing a makespan of the schedule. A minimal set of resource links is generated based on resource dependencies between tasks. The resource links are added to the original instance of scheduling problem, as precedence constraints. All the resource constraints are removed from the original instance of the resource constrained scheduling problem. A set of critical tasks is computed using a non-resource constrained critical path. Schedules are provided with an explanation of an optimized order of the set of tasks based on the use of the non-resource constrained critical path.
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公开(公告)号：US20210092137A1
公开(公告)日：2021-03-25
申请号：US16577038
申请日：2019-09-20
摘要： Aspects of the present disclosure describe techniques for detecting anomalous data in an encrypted data set. An example method generally includes receiving a data set of encrypted data points. A tree data structure having a number of levels is generated for the data set. Each level of the tree data structure generally corresponds to a feature of the encrypted plurality of features, and each node in the tree data structure at a given level represents a probability distribution of a likelihood that each data point is less than or greater than a split value determined for a given feature. An encrypted data point is received for analysis, and anomaly score is calculated based on a probability identified for each of the plurality of encrypted features. Based on determining that the calculated anomaly score exceeds a threshold value, the encrypted data point is identified as potentially anomalous.
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公开(公告)号：US10338970B2
公开(公告)日：2019-07-02
申请号：US15270975
申请日：2016-09-20
摘要： A method of scheduling assignment of resources to a plurality of applications includes: determining shares of the resources assigned to each application during a first period; determining shares of the resources assigned to each application during a second period that occurs after the first period; determining an imbalance value for each application that is based on a sum of the shares assigned to the corresponding application over both periods; and considering requests of the applications for resources in an order that depends on a result of comparing the imbalance values of the applications.
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4.发明公开公开(公告)号：US20240176988A1
公开(公告)日：2024-05-30
申请号：US18070388
申请日：2022-11-28
IPC分类号： G06N3/0455 , G06N3/044 , G06N3/047
CPC分类号： G06N3/0455 , G06N3/044 , G06N3/047
摘要： A computer-implemented method, system and computer program product for utilizing a variational autoencoder for neighborhood sampling. A variational autoencoder is trained to generate in-distribution neighborhood samples. Upon training the variational autoencoder to generate in-distribution neighborhood samples, in-distribution neighborhood samples of an instance of a dataset in latent space that satisfy a distortion constraint are generated using the trained variational autoencoder. A set of interpretable examples for the in-distribution neighborhood samples are then generated using a k-nearest neighbors algorithm. Such interpretable examples are then used to explain the black box model's predictions. As a result, the accuracy of the decision making ability of post-hoc local explanation methods is improved.
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公开(公告)号：US11694110B2
公开(公告)日：2023-07-04
申请号：US16438495
申请日：2019-06-12
发明人： Venkata Sitaramagiridharganesh Ganapavarapu , Kanthi Sarpatwar , Karthikeyan Shanmugam , Roman Vaculin
IPC分类号： G06N20/00 , G06N7/01 , H04L67/1097
CPC分类号： G06N20/00 , G06N7/01 , H04L67/1097
摘要： An example operation may include one or more of generating, by a plurality of training participant clients, gradient calculations for machine learning model training, each of the training participant clients comprising a training dataset, converting, by a training aggregator coupled to the plurality of training participant clients, the gradient calculations to a plurality of transaction proposals, receiving, by one or more endorser nodes or peers of a blockchain network, the plurality of transaction proposals, executing, by each of the endorser nodes or peers, a verify gradient smart contract, and providing endorsements corresponding to the plurality of transaction proposals to the training aggregator.
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公开(公告)号：US11562228B2
公开(公告)日：2023-01-24
申请号：US16438500
申请日：2019-06-12
发明人： Venkata Sitaramagiridharganesh Ganapavarapu , Kanthi Sarpatwar , Karthikeyan Shanmugam , Roman Vaculin
摘要： An example operation may include one or more of generating, by a training participant client comprising a training dataset, a plurality of transaction proposals that each correspond to a training iteration for machine learning model training related to stochastic gradient descent, the machine learning model training comprising a plurality of training iterations, the transaction proposals comprising a gradient calculation performed by the training participant client, a batch from the private dataset, a loss function, and an original model parameter, receiving, by one or more endorser nodes of peers of a blockchain network, the plurality of transaction proposals, and evaluating each transaction proposal.
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公开(公告)号：US11475365B2
公开(公告)日：2022-10-18
申请号：US16844987
申请日：2020-04-09
发明人： Kanthi Sarpatwar , Karthikeyan Shanmugam , Venkata Sitaramagiridharganesh Ganapavarapu , Roman Vaculin
摘要： An example operation includes one or more of computing, by a data owner node, updated gradients on a loss function based on a batch of private data and previous parameters of a machine learning model associated with a blockchain, encrypting, by the data owner node, update information, recording, by the data owner, the encrypted update information as a new transaction on the blockchain, and providing the update information for an audit.
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公开(公告)号：US11599806B2
公开(公告)日：2023-03-07
申请号：US16907578
申请日：2020-06-22
发明人： Kanthi Sarpatwar , Nalini K. Ratha , Karthikeyan Shanmugam , Karthik Nandakumar , Sharathchandra Pankanti , Roman Vaculin , James Thomas Rayfield
摘要： This disclosure provides a method, apparatus and computer program product to create a full homomorphic encryption (FHE)-friendly machine learning model. The approach herein leverages a knowledge distillation framework wherein the FHE-friendly (student) ML model closely mimics the predictions of a more complex (teacher) model, wherein the teacher model is one that, relative to the student model, is more complex and that is pre-trained on large datasets. In the approach herein, the distillation framework uses the more complex teacher model to facilitate training of the FHE-friendly model, but using synthetically-generated training data in lieu of the original datasets used to train the teacher.
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公开(公告)号：US20220374904A1
公开(公告)日：2022-11-24
申请号：US17315409
申请日：2021-05-10
发明人： Roman Vaculin , Kanthi Sarpatwar , Hong Min
摘要： A method, apparatus and computer program product that provides multi-phase privacy-preserving inferencing in a high throughput data environment, e.g., to facilitate fraud prediction, detection and prevention. In one embodiment, two (2) machine learning models are used, a first model that is trained in the clear on first transaction data, and a second model that is trained in the clear but on the first transaction data, and user data. The first model is used to perform inferencing in the clear on the high throughput received data. In this manner, the first model provides a first level evaluation of whether a particular transaction might be fraudulent. If a transaction is flagged in this first phase, a second more secure inference is then carried out using the second model. The inferencing performed by the second model is done on homomorphically encrypted data. Thus, only those transactions marked by the first model are passed to the second model for secure evaluation.
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公开(公告)号：US20210376995A1
公开(公告)日：2021-12-02
申请号：US16884567
申请日：2020-05-27
发明人： Nalini K. Ratha , Kanthi Sarpatwar , Karthikeyan Shanmugam , Sharathchandra Pankanti , Karthik Nandakumar , Roman Vaculin
摘要： A technique for computationally-efficient privacy-preserving homomorphic inferencing against a decision tree. Inferencing is carried out by a server against encrypted data points provided by a client. Fully homomorphic computation is enabled with respect to the decision tree by intelligently configuring the tree and the real number-valued features that are applied to the tree. To that end, and to the extent the decision tree is unbalanced, the server first balances the tree. A cryptographic packing scheme is then applied to the balanced decision tree and, in particular, to one or more entries in at least one of: an encrypted feature set, and a threshold data set, that are to be used during the decision tree evaluation process. Upon receipt of an encrypted data point, homomorphic inferencing on the configured decision tree is performed using a highly-accurate approximation comparator, which implements a “soft” membership recursive computation on real numbers, all in an oblivious manner.
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