Noisy Data

Noisy data, ubiquitous in real-world applications, poses a significant challenge to machine learning model accuracy and reliability. Current research focuses on developing robust algorithms and model architectures, such as deep learning networks (including CNNs and Transformers), that can effectively handle various types of noise (e.g., label noise, missing data, sensor noise) across diverse data modalities (e.g., images, text, time series). These advancements are crucial for improving the performance and trustworthiness of machine learning systems in fields ranging from medical imaging and financial prediction to environmental monitoring and natural language processing.

Papers

July 14, 2022

Have we been Naive to Select Machine Learning Models? Noisy Data are here to Stay!
Felipe Costa Farias, Teresa Bernarda Ludermir, Carmelo José Albanez Bastos-Filho
Machine Learning Model Model Selection Model Overfitting Noisy Data Multi Criterion Decision Single Stage

June 30, 2022

Learning nonparametric ordinary differential equations from noisy data
Kamel Lahouel, Michael Wells, Victor Rielly, Ethan Lew, David Lovitz, Bruno M. Jedynak
Machine Learning Strong Generalization Noisy Data Reproducing Kernel Hilbert Space Ordinary Differential Equation Non Parametric Learning

June 12, 2022

DRNet: Decomposition and Reconstruction Network for Remote Physiological Measurement
Yuhang Dong, Gongping Yang, Yilong Yin
Noisy Data Microbial Decomposition Remote Photoplethysmography Reconstruction Network Physiological Change Remote Physiological Measurement Physiological State Blood Volume Pulse

June 9, 2022

It's a super deal -- train recurrent network on noisy data and get smooth prediction free
Boris Rubinstein
Human Prediction Noisy Data Recurrent Network Input Sequence Local Prediction

May 19, 2022

Neural ODEs with Irregular and Noisy Data
Pawan Goyal, Peter Benner
Neural ODE Neural Ordinary Differential Equation Noisy Data Dynamical Model Sampled Time Series

May 14, 2022

Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data
Xu Liu, Wen Yao, Wei Peng, Weien Zhou
Physic Informed Neural Network Physic Informed Machine Learning Bayesian Perspective Noisy Data Extreme Learning Machine Inverse PDE Problem

May 2, 2022

Modeling and mitigation of occupational safety risks in dynamic industrial environments
Ashutosh Tewari, Antonio R. Paiva
Noisy Data Faulty Negative Mitigation Resource Constrained Risk Management Safety Critical Safety Engineering

April 27, 2022

Robust Face Anti-Spoofing with Dual Probabilistic Modeling
Yuanhan Zhang, Yichao Wu, Zhenfei Yin, Jing Shao, Ziwei Liu
Probabilistic Model Noisy Data Face Anti Spoofing Box Embeddings Noise Modeling

April 22, 2022

Implicit Object Mapping With Noisy Data
Jad Abou-Chakra, Feras Dayoub, Niko Sünderhauf
Neural Radiance Field Noisy Data Instance Mask Implicit Map

April 1, 2022

March 29, 2022

Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries
Jihwan Bang, Hyunseo Koh, Seulki Park, Hwanjun Song, Jung-Woo Ha, Jonghyun Choi
Continual LEArning Noisy Data Online Continual Learning Episodic Memory Continual Learning Model Sample Diversity Task Boundary

March 28, 2022

UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning
Nazmul Karim, Mamshad Nayeem Rizve, Nazanin Rahnavard, Ajmal Mian, Mubarak Shah
Contrastive Learning Semi Supervised Learning Noisy Label Label Noise Noisy Data Random Selection

March 15, 2022

Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels
Yikai Wang, Xinwei Sun, Yanwei Fu
LeArning Abstract Noisy Label Noisy Data Real World Noisy Datasets Noise Detection Noisy Label Detection

February 24, 2022

Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning
Gabriel Spadon, Martha D. Ferreira, Amilcar Soares, Stan Matwin
Neural Network Machine Learning Deep Learning Model Recurrent Neural Network Noisy Data Vessel Trajectory Automatic Identification System

February 11, 2022

Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett
Gradient Descent Generalization Error Noisy Data Two Layer Neural Network Benign Overfitting Neural Network Classifier

February 9, 2022

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery
Masoud Jalayer, Amin Kaboli, Carlotta Orsenigo, Carlo Vercellis
Long Short Term Memory Fault Detection Noisy Data Extreme Learning Machine Group Imbalance Industrial Machine Hybrid Framework

February 7, 2022

Lossy Compression of Noisy Data for Private and Data-Efficient Learning
Berivan Isik, Tsachy Weissman
Training Data Sensitive Data Noisy Data Data Efficient PRIvacy Leakage Lossy Compression Privacy Preserving Learning

January 13, 2022

Fully Adaptive Bayesian Algorithm for Data Analysis, FABADA
Pablo M Sanchez-Alarcon, Yago Ascasibar Sequeiros
Bayesian Inference Noisy Data Signal to Noise Ratio Adaptive Bayesian

January 7, 2022

Optimality in Noisy Importance Sampling
Fernando Llorente, Luca Martino, Jesse Read, David Delgado-Gómez
Noisy Data Importance Sampling Near Optimality Efficient Realization Noisy Evaluation Proposal Distribution Dynamic Proposal

Noisy Data

Papers

Have we been Naive to Select Machine Learning Models? Noisy Data are here to Stay!

Learning nonparametric ordinary differential equations from noisy data

DRNet: Decomposition and Reconstruction Network for Remote Physiological Measurement

It's a super deal -- train recurrent network on noisy data and get smooth prediction free

Neural ODEs with Irregular and Noisy Data

Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data

Modeling and mitigation of occupational safety risks in dynamic industrial environments

Robust Face Anti-Spoofing with Dual Probabilistic Modeling

Implicit Object Mapping With Noisy Data

Hierarchical model reduction driven by machine learning for parametric advection-diffusion-reaction problems in the presence of noisy data

Perception Prioritized Training of Diffusion Models

Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries

UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning

Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels

Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning

Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Lossy Compression of Noisy Data for Private and Data-Efficient Learning

Fully Adaptive Bayesian Algorithm for Data Analysis, FABADA

Optimality in Noisy Importance Sampling