Supervised Learning

Supervised learning, a core machine learning paradigm, aims to train models to predict outputs based on labeled input data. Current research emphasizes improving model efficiency and robustness, particularly in scenarios with limited or noisy data, exploring techniques like self-supervised pre-training, active learning for data selection, and ensemble methods to enhance accuracy and address class imbalances. These advancements are crucial for various applications, from medical image analysis and infrastructure inspection to natural language processing and targeted advertising, enabling more accurate and reliable predictions with less reliance on extensive labeled datasets.

Papers

September 4, 2023

Adapting Classifiers To Changing Class Priors During Deployment
Natnael Daba, Bruce McIntosh, Abhijit Mahalanobis
Supervised Learning Simple Classifier Software Deployment Learning Generalizable Class Prior Adaptive Classification

August 26, 2023

Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class Discovery
Qiang Li, Qiuyang Ma, Weizhi Nie, Anan Liu
Self Supervised Learning Supervised Learning Fusion Network Novel Class Discovery Reinforcement Learning Framework Exploratory Learning Exploration Based Learning

August 16, 2023

How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning
Sandra Gilhuber, Rasmus Hvingelby, Mang Ling Ada Fok, Thomas Seidl
Active Learning Semi Supervised Learning Semi Supervised Supervised Learning Semi Supervised Image Classification Confirmation Bias Semi Supervised Active Learning

August 10, 2023

SSLRec: A Self-Supervised Learning Framework for Recommendation
Xubin Ren, Lianghao Xia, Yuhao Yang, Wei Wei, Tianle Wang, Xuheng Cai, Chao Huang
Self Supervised Supervised Learning Effective Recommendation Recommendation Model Recommendation Algorithm Self Supervised Learning Framework

August 9, 2023

August 7, 2023

Generative Forests
Richard Nock, Mathieu Guillame-Bert
Supervised Learning Tabular Data Data Generation

August 3, 2023

Cream Skimming the Underground: Identifying Relevant Information Points from Online Forums
Felipe Moreno-Vera, Mateus Nogueira, Cainã Figueiredo, Daniel Sadoc Menasché, Miguel Bicudo, Ashton Woiwood, Enrico Lovat, Anton Kocheturov, Leandro Pfleger de Aguiar
Supervised Learning Random Forest Real World eXploitation Relevant Information Block SkiM

July 28, 2023

Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning
Xindi Wang, Yufei Wang, Can Xu, Xiubo Geng, Bowen Zhang, Chongyang Tao, Frank Rudzicz, Robert E. Mercer, Daxin Jiang
Large Language Model Context Learning Supervised Learning Consistent Comparison Behaviour Learning Downstream Task Performance Label Perturbation

July 27, 2023

Mixture of Self-Supervised Learning
Aristo Renaldo Ruslim, Novanto Yudistira, Budi Darma Setiawan
Self Supervised Learning Semi Supervised Learning Supervised Learning Mixture Component Pretext Task

July 25, 2023

Learning to Design Analog Circuits to Meet Threshold Specifications
Dmitrii Krylov, Pooya Khajeh, Junhan Ouyang, Thomas Reeves, Tongkai Liu, Hiba Ajmal, Hamidreza Aghasi, Roy Fox
LeArning Abstract Supervised Learning Large Scale Circuit Canonical Threshold Analog Circuit Design Circuit Topology Radio Frequency Circuit

July 18, 2023

Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Suruchi Kumari, Pravendra Singh
Deep Learning Domain Adaptation Unsupervised Domain Adaptation Supervised Learning Medical Imaging Recent Advance Image Translation Future Perspective

July 17, 2023

Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training
Nathaniel Berger, Miriam Exel, Matthias Huck, Stefan Riezler
Neural Machine Translation Supervised Learning Maximum Likelihood K TOKEN Contrastive Feature

July 11, 2023

Fundamental limits of overparametrized shallow neural networks for supervised learning
Francesco Camilli, Daria Tieplova, Jean Barbier
Neural Network Supervised Learning Neural Network Model Information Theoretic Two Layer Neural Network Shallow Neural Network Fundamental Limit Optimal Generalization

July 10, 2023

Robust Feature Learning Against Noisy Labels
Tsung-Ming Tai, Yun-Jie Jhang, Wen-Jyi Hwang
Supervised Learning Noisy Label Bootstrapping End to End Robust Clustering Robust Feature Robust Feature Representation

June 27, 2023

A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagery
Xin Zhang, Liangxiu Han
Self Supervised Learning Supervised Learning Remote Sensing Image Individual Representation Visual Imagery Hyperspectral Datasets Spatial Spectral Multispectral Datasets

June 24, 2023

Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets
Anirudhan Badrinath, Yannis Flet-Berliac, Allen Nie, Emma Brunskill
Reinforcement Learning Offline Reinforcement Learning Supervised Learning Decision Transformer Waypoint Generation Intermediate Target

June 23, 2023

Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity
Riccardo Presotto, Sannara Ek, Gabriele Civitarese, François Portet, Philippe Lalanda, Claudio Bettini
Training Data Pre Trained Model Supervised Learning Activity Recognition Data Scarcity Unseen Data

June 20, 2023

Supervised Learning

Papers

Adapting Classifiers To Changing Class Priors During Deployment

Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class Discovery

How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning

SSLRec: A Self-Supervised Learning Framework for Recommendation

Rapid Training Data Creation by Synthesizing Medical Images for Classification and Localization

SSL-Auth: An Authentication Framework by Fragile Watermarking for Pre-trained Encoders in Self-supervised Learning

Generative Forests

Cream Skimming the Underground: Identifying Relevant Information Points from Online Forums

Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning

Mixture of Self-Supervised Learning

Learning to Design Analog Circuits to Meet Threshold Specifications

Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training

Fundamental limits of overparametrized shallow neural networks for supervised learning

Robust Feature Learning Against Noisy Labels

A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagery

Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets

Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity

A Model-free Closeness-of-influence Test for Features in Supervised Learning

Mean-field Analysis of Generalization Errors