Analog Algorithm – Landscapes of Machine Learning
نویسندگان
چکیده
منابع مشابه
Perspective: Energy Landscapes for Machine Learning
Andrew J. Ballard, Ritankar Das, Stefano Martiniani, Dhagash Mehta, Levent Sagun, Jacob D. Stevenson, and David J. Wales a) University Chemical Laboratories, Lensfield Road, Cambridge CB2 1EW, United Kingdom Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, IN, USA Mathematics Department, Courant Institute, New York University, NY, USA Microsoft Resea...
متن کاملAn Analog VLSI Deep Machine Learning Implementation
I am submitting herewith a dissertation written by Junjie Lu entitled "An Analog VLSI Deep Machine Learning Implementation." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Electrical Engineering. We have read this dissertation ...
متن کاملTest Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine
This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation alg...
متن کاملMachine learning assembly landscapes from particle tracking data.
Bottom-up self-assembly offers a powerful route for the fabrication of novel structural and functional materials. Rational engineering of self-assembling systems requires understanding of the accessible aggregation states and the structural assembly pathways. In this work, we apply nonlinear machine learning to experimental particle tracking data to infer low-dimensional assembly landscapes map...
متن کاملMachine learning landscapes and predictions for patient outcomes
The theory and computational tools developed to interpret and explore energy landscapes in molecular science are applied to the landscapes defined by local minima for neural networks. These machine learning landscapes correspond to fits of training data, where the inputs are vital signs and laboratory measurements for a database of patients, and the objective is to predict a clinical outcome. I...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: sub\urban. zeitschrift für kritische stadtforschung
سال: 2021
ISSN: 2197-2567
DOI: 10.36900/suburban.v9i1/2.696