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公开(公告)号：US12229604B2
公开(公告)日：2025-02-18
申请号：US17573221
申请日：2022-01-11
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Pradeep Dogga
Abstract: Shared resource interference detection techniques are described. In an example, a resource detection module supports techniques to quantify levels of interference through use of working set sizes. The resource detection module selects working set sizes. The resource detection module then initiates execution of code that utilizes the shared resource based on the first working set size. The resource detection module detects a resource consumption amount based on the execution of the code. The resource detection module then determines whether the detected resource consumption amount corresponds to the defined resource consumption amount for the selected working set size.
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公开(公告)号：US20230222005A1
公开(公告)日：2023-07-13
申请号：US17573221
申请日：2022-01-11
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Pradeep Dogga
CPC classification number: G06F9/5077 , G06F9/5016 , G06F9/5022 , G06N20/00
Abstract: Shared resource interference detection techniques are described. In an example, a resource detection module supports techniques to quantify levels of interference through use of working set sizes. The resource detection module selects working set sizes. The resource detection module then initiates execution of code that utilizes the shared resource based on the first working set size. The resource detection module detects a resource consumption amount based on the execution of the code. The resource detection module then determines whether the detected resource consumption amount corresponds to the defined resource consumption amount for the selected working set size.
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公开(公告)号：US10943497B2
公开(公告)日：2021-03-09
申请号：US15964869
申请日：2018-04-27
Applicant: ADOBE INC.
Inventor： Shiv Kumar Saini , Ritwick Chaudhry , Pradeep Dogga , Harvineet Singh
Abstract: Techniques are described for jointly modeling knowledge tracing and hint-taking propensity. During a read phase, a co-learning model accepts as inputs an identification of a question and the current knowledge state for a learner, and the model predicts probabilities that the learner will answer the question correctly and that the learner will use a learning aid (e.g., accept a hint). The predictions are used to personalize an e-learning plan, for example, to provide a personalized assessment. By using these predictions to personalize a learner's experience, for example, by offering hints at optimal times, the co-learning system increases efficiencies in learning and improves learning outcomes. Once a learner has interacted with a question, the interaction is encoded and provided to the co-learning model to update the learner's knowledge state during an update phase.
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4.发明申请公开(公告)号：US20190333400A1
公开(公告)日：2019-10-31
申请号：US15964869
申请日：2018-04-27
Applicant: ADOBE INC.
Inventor： Shiv Kumar Saini , Ritwick Chaudhry , Pradeep Dogga , Harvineet Singh
Abstract: Techniques are described for jointly modeling knowledge tracing and hint-taking propensity. During a read phase, a co-learning model accepts as inputs an identification of a question and the current knowledge state for a learner, and the model predicts probabilities that the learner will answer the question correctly and that the learner will use a learning aid (e.g., accept a hint). The predictions are used to personalize an e-learning plan, for example, to provide a personalized assessment. By using these predictions to personalize a learner's experience, for example, by offering hints at optimal times, the co-learning system increases efficiencies in learning and improves learning outcomes. Once a learner has interacted with a question, the interaction is encoded and provided to the co-learning model to update the learner's knowledge state during an update phase.
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