tag: supervised learning

More Accurate And Detailed Analysis of Semiconductor Defects In SEM Images Using SEMI-PointRend
By Technical Paper Link - 10 Mar, 2023 - Comments: 0

A technical paper titled "SEMI-PointRend: Improved Semiconductor Wafer Defect Classification and Segmentation as Rendering" was published (preprint) by researchers at imec, University of Ulsan, and KU Leuven. Abstract: "In this study, we applied the PointRend (Point-based Rendering) method to semiconductor defect segmentation. PointRend is an iterative segmentation algorithm inspired by ima... » read more

Multiexpert Adversarial Regularization For Robust And Data-Efficient Deep Supervised Learning
By Samsung - 16 Nov, 2022 - Comments: 0

Deep neural networks (DNNs) can achieve high accuracy when there is abundant training data that has the same distribution as the test data. In practical applications, data deficiency is often a concern. For classification tasks, the lack of enough labeled images in the training set often results in overfitting. Another issue is the mismatch between the training and the test domains, which resul... » read more

MEMprop: Gradient-based Learning To Train Fully Memristive SNNs
By Technical Paper Link - 04 Jul, 2022 - Comments: 0

New technical paper titled "Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays" from IEEE researchers. Abstract "We present MEMprop, the adoption of gradient-based learning to train fully memristive spiking neural networks (MSNNs). Our approach harnesses intrinsic device dynamics to trigger naturally arising voltage spikes. These spikes emitted by memristive dynamics are anal... » read more

Coverage-Directed Test Selection Method for Automatic Test Biasing During Simulation-Based Verification
By Technical Paper Link - 22 Jun, 2022 - Comments: 0

New research paper titled "Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification" from researchers at University of Bristol and Infineon Technologies. Abstract: "Constrained random test generation is one the most widely adopted methods for generating stimuli for simulation-based verification. Randomness leads to test diversity, but tests tend to repeate... » read more

How Do Machines Learn?
By Weiyang (Will) Zhou - 06 Apr, 2021 - Comments: 0

We depend, or hope to depend, on machines, especially computers, to do many things, from organizing our photos to parking our cars. Machines are becoming less and less "mechanical" and more and more "intelligent." Machine learning has become a familiar phrase to many people in advanced manufacturing. The next natural question people may ask is: How do machines learn? Recognizing diverse obje... » read more

Are Better Machine Training Approaches Ahead?
By Bryon Moyer - 01 Jul, 2020 - Comments: 1

We live in a time of unparalleled use of machine learning (ML), but it relies on one approach to training the models that are implemented in artificial neural networks (ANNs) — so named because they’re not neuromorphic. But other training approaches, some of which are more biomimetic than others, are being developed. The big question remains whether any of them will become commercially viab... » read more

Making AI More Dependable
By Ed Sperling - 07 May, 2019 - Comments: 0

Ira Leventhal, vice president of Advantest’s new concept product initiative, looks at why AI has taken so long to get going, what role it will play in improving the reliability of all chips, and how to use AI to improve the reliability of AI chips themselves. » read more

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