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Notes on neural networks
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Test Case Prioritization for DNNs via Neural Collapse Instability
A new method, Neural-Collapse-Inspired Prioritization (NCIP), is proposed to improve test case prioritization for deep neural networks (DNNs) in safety-critical domains where testing budgets are limited. The method…
AI-guided stimuli discovery and generation to optimize facial emotion perception studies in autism
Researchers have developed a method to create and select facial expressions that enhance differences in emotion perception between autistic and neurotypical adults. This is done by training artificial neural networks…
SLORR: Simple and Efficient In-Training Low-Rank Regularization
The paper introduces SLORR, a simple and efficient method for in-training low-rank regularization of neural networks. It addresses the limitations of existing methods by being stateless and preserving the original…
SLORR: Simple and Efficient In-Training Low-Rank Regularization
The paper introduces SLORR, a simple and efficient low-rank regularization method for compressing neural networks during training. It addresses the limitations of existing methods by being stateless…
A Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial Systems
A new study presents a Physics-Informed Neural Network (PINN) framework to model the transient elastodynamic wave propagation in bimaterial systems, specifically focusing on a steel-aluminum specimen. The PINN…
PDEFlow: Autonomous Agentic PDE Pipelines for Neural Operator Learning and Solver-Free Inference
The paper presents PDEFlow, a framework that converts user-level descriptions of ODEs and PDEs into neural operator pipelines for autonomous agentic simulations. It includes stages for problem specification, data…
Program-as-Weights: A Programming Paradigm for Fuzzy Functions
The authors propose a new programming paradigm called fuzzy-function programming, where functions can be compiled from natural language specifications into compact, locally executable neural networks. They introduce…
GPU-Parallel Linearization Error Bounds for Real-Time Robust Optimal Control of Nonlinear and Neural Network Dynamics
The paper presents a new method called GPUSLS-LEO for real-time robust optimal control of uncertain nonlinear systems, specifically focusing on systems with linear time-varying (LTV) approximations. This method…
Can Scale Save Us From Plasticity Loss in Large Language Models?
Researchers studied plasticity loss, a challenge in creating neural networks capable of continuous learning, in modern transformer-based language models (LLMs). They found evidence of plasticity loss across various…
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