Enhancing Accuracy

Enhancing the accuracy of machine learning models is a central focus of current research, driven by the need for reliable predictions across diverse applications. This involves exploring novel architectures like Bayesian networks and attention mechanisms, optimizing training processes through techniques such as adversarial training and energy natural gradients, and improving data efficiency via knowledge transfer and parameter-efficient representations. These advancements are crucial for improving the reliability of AI systems in critical domains like healthcare and autonomous driving, as well as for advancing fundamental understanding of model behavior and limitations.

Papers

August 5, 2024

Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability
Masoud Muhammed Hassan
Inherent Interpretability Bayesian Inference Kolmogorov Arnold Network Bayesian Learning Prediction Uncertainty Probabilistic Approach Enhancing Accuracy Imbalanced Medical

June 29, 2024

Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization
Hongjun Choi, Jayaraman J. Thiagarajan, Ruben Glatt, Shusen Liu
Neural Representation Parameter Efficient Neural Network Weight Enhancing Accuracy Weight Re Mapping Prediction Network Parameterized Network

May 29, 2024

EntProp: High Entropy Propagation for Improving Accuracy and Robustness
Shohei Enomoto
Deep Neural Network Native Robustness Adversarial Training Understanding Out of Distribution Enhancing Accuracy Entropy Sampling

May 27, 2024

Enhancing Accuracy in Generative Models via Knowledge Transfer
Xinyu Tian, Xiaotong Shen
Generative Model Knowledge Transfer Enhancing Accuracy

April 2, 2024

A Methodology for Improving Accuracy of Embedded Spiking Neural Networks through Kernel Size Scaling
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
NCD Method Spiking Neural Network Enhancing Accuracy Accuracy Tradeoff SNN Architecture Embedded Neural Network Kernel Size

January 2, 2024

Hybrid Pooling and Convolutional Network for Improving Accuracy and Training Convergence Speed in Object Detection
Shiwen Zhao, Wei Wang, Junhui Hou, Hai Wu
Object Detection Deep Convolutional Neural Network Pooling Layer PAPER Demand \Cite{meir2021market Enhancing Accuracy High Precision Training Convergence

October 4, 2023

Enhancing Accuracy in Deep Learning Using Random Matrix Theory
Leonid Berlyand, Etienne Sandier, Yitzchak Shmalo, Lei Zhang
Neural Network Deep Learning Random Matrix Enhancing Accuracy Deep Neural Network Parameter Layer Pruning Random Pruning

September 11, 2023

Studying Accuracy of Machine Learning Models Trained on Lab Lifting Data in Solving Real-World Problems Using Wearable Sensors for Workplace Safety
Joseph Bertrand, Nick Griffey, Ming-Lun Lu, Rashmi Jha
Training Data Machine Learning Model Model Performance Wearable Sensor Enhancing Accuracy Laboratory Value

August 21, 2023

Unlocking Accuracy and Fairness in Differentially Private Image Classification
Leonard Berrada, Soham De, Judy Hanwen Shen, Jamie Hayes, Robert Stanforth, David Stutz, Pushmeet Kohli, Samuel L. Smith, Borja Balle
Differential Privacy Procedural Fairness Privacy Preserving Image Enhancing Accuracy Privacy Preserving Training

June 21, 2023

Residual-Based Error Corrector Operator to Enhance Accuracy and Reliability of Neural Operator Surrogates of Nonlinear Variational Boundary-Value Problems
Prashant K. Jha
Neural Operator Nonlinear Model Variational Method Solution Operator Enhancing Accuracy Operator Surrogate

June 7, 2023

Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions
John Joon Young Chung, Ece Kamar, Saleema Amershi
Large Language Model Text Generation Diversity Awareness Diverse Datasets Data to Text Generation Enhancing Accuracy

May 23, 2023

Enhancing Accuracy and Robustness through Adversarial Training in Class Incremental Continual Learning
Minchan Kwon, Kangil Kim
Adversarial Attack Native Robustness Adversarial Example Adversarial Training Class Incremental Continual Learning Enhancing Accuracy

February 25, 2023

Achieving High Accuracy with PINNs via Energy Natural Gradients
Johannes Müller, Marius Zeinhofer
Physic Informed Neural Network Natural Gradient Gradient Flow Hessian Matrix Natural Gradient Descent Enhancing Accuracy

February 23, 2023

A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers
Sotiris Pelekis, Ioannis-Konstantinos Seisopoulos, Evangelos Spiliotis, Theodosios Pountridis, Evangelos Karakolis, Spiros Mouzakitis, Dimitris Askounis
Deep Learning Model Long Short Term Memory Comparative Evaluation Day Ahead Enhancing Accuracy Mid Term Electricity

December 13, 2022

Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting
Yiqi Sun, Zhengxin Shi, Jianshen Zhang, Yongzhi Qi, Hao Hu, Zuojun Max Shen
Inherent Interpretability Data Driven Time Series Forecasting Machine Learning Approach Enhancing Accuracy Decomposition Method Forecasting Algorithm Sale Forecast

November 21, 2022

Enhancing Accuracy and Robustness of Steering Angle Prediction with Attention Mechanism
Swetha Nadella, Pramiti Barua, Jeremy C. Hagler, David J. Lamb, Qing Tian
Native Robustness Attention Mechanism Deep Learning Architecture Enhancing Accuracy Autonomous Driving Task Steering Angle Prediction

September 14, 2022

Improving Accuracy and Explainability of Online Handwriting Recognition
Hilda Azimi, Steven Chang, Jonathan Gold, Koray Karabina
High Explainability Handwriting Recognition Signature Verification Enhancing Accuracy Online Handwriting Handwriting Data

September 1, 2022

Optimising 2D Pose Representation: Improve Accuracy, Stability and Generalisability Within Unsupervised 2D-3D Human Pose Estimation
Peter Hardy, Srinandan Dasmahapatra, Hansung Kim
Core Stability Stronger Generalizability 3D Pose 2 Dimensional Human Pose Estimation 2 Dimensional Pose Enhancing Accuracy Lifted Inference Unsupervised 3D Pose

June 11, 2022

Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution
Julia Moosbauer, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
Bayesian Optimization Hyperparameter Optimization Related Hyperparameters Enhancing Accuracy Bayesian Algorithm Interpretability Evaluation

April 28, 2022

Unlocking High-Accuracy Differentially Private Image Classification through Scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L. Smith, Borja Balle
Deep Learning Differential Privacy Visual Analogue Scale Private Stochastic Gradient Descent Privacy Preserving Image Differential Privacy SGD Enhancing Accuracy

Enhancing Accuracy

Papers

Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability

Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization

EntProp: High Entropy Propagation for Improving Accuracy and Robustness

Enhancing Accuracy in Generative Models via Knowledge Transfer

A Methodology for Improving Accuracy of Embedded Spiking Neural Networks through Kernel Size Scaling

Hybrid Pooling and Convolutional Network for Improving Accuracy and Training Convergence Speed in Object Detection

Enhancing Accuracy in Deep Learning Using Random Matrix Theory

Studying Accuracy of Machine Learning Models Trained on Lab Lifting Data in Solving Real-World Problems Using Wearable Sensors for Workplace Safety

Unlocking Accuracy and Fairness in Differentially Private Image Classification

Residual-Based Error Corrector Operator to Enhance Accuracy and Reliability of Neural Operator Surrogates of Nonlinear Variational Boundary-Value Problems

Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

Enhancing Accuracy and Robustness through Adversarial Training in Class Incremental Continual Learning

Achieving High Accuracy with PINNs via Energy Natural Gradients

A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers

Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting

Enhancing Accuracy and Robustness of Steering Angle Prediction with Attention Mechanism

Improving Accuracy and Explainability of Online Handwriting Recognition

Optimising 2D Pose Representation: Improve Accuracy, Stability and Generalisability Within Unsupervised 2D-3D Human Pose Estimation

Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution

Unlocking High-Accuracy Differentially Private Image Classification through Scale