Cover Image

Supplementation of deep neural networks with simplified physics-based features to increase model prediction accuracy

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Clinkinbeard, Nicholus R., Hashemi, Nicole N.
Format:	Preprint
Published:	2022
Subjects:	Emerging Technologies Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Model-free front-to-end training of a large high performance laser neural network
by: Skalli, Anas, et al.
Published: (2025)

Impact of white noise in artificial neural networks trained for classification: performance and noise mitigation strategies
by: Semenova, Nadezhda, et al.
Published: (2024)

Photonic convolutional neural network with pre-trained in-situ training
by: Ranjan, Saurabh, et al.
Published: (2026)

Gradients of unitary optical neural networks using parameter-shift rule
by: Jiang, Jinzhe, et al.
Published: (2025)

Application of machine learning models to predict the relationship between air pollution, ecosystem degradation, and health disparities and lung cancer in Vietnam
by: Tran, Ngoc Hong, et al.
Published: (2025)

Energy-convergence trade off for the training of neural networks on bio-inspired hardware
by: Garg, Nikhil, et al.
Published: (2025)

Neuromorphic on-chip reservoir computing with spiking neural network architectures
by: Karki, Samip, et al.
Published: (2024)

Scaling on-chip photonic neural processors using arbitrarily programmable wave propagation
by: Onodera, Tatsuhiro, et al.
Published: (2024)

Physical Foundation Models: Fixed hardware implementations of large-scale neural networks
by: Wright, Logan G, et al.
Published: (2026)

Trustworthy Tree-based Machine Learning by $MoS_2$ Flash-based Analog CAM with Inherent Soft Boundaries
by: Wen, Bo, et al.
Published: (2025)

Training single-electron and single-photon stochastic physical neural networks
by: Dou, Tong, et al.
Published: (2026)

Decision-making and control with diffractive optical networks
by: Qiu, Jumin, et al.
Published: (2022)

A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference
by: Gallo, Manuel Le, et al.
Published: (2022)

Adaptive Semantic Token Communication for Transformer-based Edge Inference
by: Devoto, Alessio, et al.
Published: (2025)

Device Modeling Bias in ReRAM-based Neural Network Simulations
by: Yousuf, Osama, et al.
Published: (2022)

Mixed-Precision Training and Compilation for RRAM-based Computing-in-Memory Accelerators
by: Pelke, Rebecca, et al.
Published: (2026)

Pruning for Improved ADC Efficiency in Crossbar-based Analog In-memory Accelerators
by: Ibrayev, Timur, et al.
Published: (2024)

Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model
by: Priyadarsini, Indra, et al.
Published: (2024)

GNN-based Path-aware multi-view Circuit Learning for Technology Mapping
by: Jiang, Wentao, et al.
Published: (2026)

Q-SCALE: Quantum computing-based Sensor Calibration for Advanced Learning and Efficiency
by: Bergadano, Lorenzo, et al.
Published: (2024)

IMPLY-based Approximate Full Adders for Efficient Arithmetic Operations in Image Processing and Machine Learning
by: Qiu, Melanie, et al.
Published: (2024)

NEURODNAAI: Neural pipeline approaches for the advancing dna-based information storage as a sustainable digital medium using deep learning framework
by: Thakur, Rakesh, et al.
Published: (2025)

Auditing an Automatic Grading Model with deep Reinforcement Learning
by: Condor, Aubrey, et al.
Published: (2024)

Single chip photonic deep neural network with accelerated training
by: Bandyopadhyay, Saumil, et al.
Published: (2022)

Fully analogue in-memory neural computing via quantum tunneling effect
by: Li, Songyuan, et al.
Published: (2025)

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing
by: Abbott, Cliff B., et al.
Published: (2025)

Extractive summarization on a CMOS Ising machine
by: Zeng, Ziqing, et al.
Published: (2026)

Online unsupervised Hebbian learning in deep photonic neuromorphic networks
by: Li, Xi, et al.
Published: (2026)

Design and development of opto-neural processors for simulation of neural networks trained in image detection for potential implementation in hybrid robotics
by: Shetty, Sanjana
Published: (2024)

Noise-adaptive hybrid quantum convolutional neural networks based on depth-stratified feature extraction
by: Kim, Taehyun, et al.
Published: (2026)

Measurement-driven neural-network training for integrated magnetic tunnel junction arrays
by: Borders, William A., et al.
Published: (2023)

Nonideality-aware training makes memristive networks more robust to adversarial attacks
by: Joksas, Dovydas, et al.
Published: (2024)

Clustering Guided Residual Neural Networks for Multi-Tx Localization in Molecular Communications
by: Sonmez, Ali, et al.
Published: (2025)

Multi-VQC: A Novel QML Approach for Enhancing Healthcare Classification
by: Tudisco, Antonio, et al.
Published: (2025)

Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
by: Gallo, Manuel Le, et al.
Published: (2023)

Beyond the Mean: Modelling Annotation Distributions in Continuous Affect Prediction
by: Pinitas, Kosmas, et al.
Published: (2026)

A novel RF-enabled Non-Destructive Inspection Method through Machine Learning and Programmable Wireless Environments
by: Tsimpoukis, Stavros, et al.
Published: (2026)

Affordable Precision Agriculture: A Deployment-Oriented Review of Low-Cost, Low-Power Edge AI and TinyML for Resource-Constrained Farming Systems
by: Samanta, Riya, et al.
Published: (2026)

Quantum Algorithms: A New Frontier in Financial Crime Prevention
by: Weinberg, Abraham Itzhak, et al.
Published: (2024)

An Automated Machine Learning Approach to Inkjet Printed Component Analysis: A Step Toward Smart Additive Manufacturing
by: Sahu, Abhishek, et al.
Published: (2024)