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Artificial Neural Network Train-Time Poison Defense via Energy-Based Model Dynamics Sampling (Case No. 2024-199)
Summary: UCLA researchers in the Department of Electrical and Computer Engineering have developed a novel preprocessing algorithm to purify imperceptibly poisoned training datasets that may lead to misclassification and maintain image quality. Background: Large datasets are important for the effectiveness of machine learning models, serving as the...
Published: 9/30/2024
|
Inventor(s):
Gregory Pottie
,
Omead Pooladzandi
,
Sunay Bhat
,
Jeffrey Jiang
Keywords(s):
AI algorithms
,
Algorithm
,
Artificial Intelligence
,
artificial intelligence augmentation
,
Automation
,
Autonomous driving
,
Big Data
,
Computer Aided Learning
,
Data Corruption
,
Digital Health
,
Machine Learning
,
Machine Learning Autonomous Car Gradient Descent
Category(s):
Software & Algorithms
,
Platforms > Diagnostic Platform Technologies
,
Therapeutics > Radiology
,
Medical Devices > Monitoring And Recording Systems
,
Medical Devices > Neural Stimulation
,
Mechanical > Sensors
2021-400 COMET: On-Die and In-Controller Collaborative Memory ECC Technique for Stronger and Safer Correction of DRAM Errors
Summary: UCLA researchers in the Department of Electrical and Computer Engineering have developed a method titled Collaborative Memory ECC Technique (COMET), to efficiently detect two error correcting codes (ECC) and eliminate silent
data corruption
(SDC) when double-bit errors occur within DRAMs. Background: Technological abundance has been the...
Published: 12/11/2023
|
Inventor(s):
Puneet Gupta
,
Irina Alam
Keywords(s):
Analogue Electronics
,
Big Data
,
Data Corruption
,
DDR3 SDRAM
,
DDR4 SDRAM
,
Random-Access Memory
,
Semiconductor
,
Semiconductor Device
,
Soft Error
,
Software
Category(s):
Electrical
,
Electrical > Electronics & Semiconductors
,
Materials > Semiconducting Materials
,
Energy & Environment > Energy Storage