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

February 19, 2023

Mixed Semi-Supervised Generalized-Linear-Regression with applications to Deep-Learning and Interpolators
Oren Yuval, Saharon Rosset
Deep Learning Financial Application Semi Supervised Supervised Learning Unlabeled Data Regression Task Generalized Linear Model

February 18, 2023

Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least
Siddharth Joshi, Baharan Mirzasoleiman
Contrastive Learning Self Supervised Supervised Learning Self Supervised Contrastive Learning High Quality Representation Positive Example

February 16, 2023

Tragic and Comical Networks. Clustering Dramatic Genres According to Structural Properties
Szemes Botond, Vida Bence
Supervised Learning Play Style Structural Characterization Character Network

February 6, 2023

January 27, 2023

January 20, 2023

Transforming Unstructured Text into Data with Context Rule Assisted Machine Learning (CRAML)
Stephen Meisenbacher, Peter Norlander
Context Learning Raw Data Supervised Learning Unstructured Text Text Classification Model

January 14, 2023

Gated Self-supervised Learning For Improving Supervised Learning
Erland Hilman Fuadi, Aristo Renaldo Ruslim, Putu Wahyu Kusuma Wardhana, Novanto Yudistira
Self Supervised Learning Image Classification Supervised Learning Self Supervised Transformer Mixture Density Network Channel Data Augmentation

December 21, 2022

December 20, 2022

Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysis
Josip Jukić, Jan Šnajder
Pre Trained Language Model Active Learning Supervised Learning Large Pre Trained Language Model Average Smoothness Smooth Representation Label Complexity

December 16, 2022

Learning on Persistence Diagrams as Radon Measures
Alex Elchesen, Iryna Hartsock, Jose A. Perea, Tatum Rask
LeArning Abstract Supervised Learning Novel Descriptor Continuous Space Persistence Diagram Indoor Radon

December 7, 2022

Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability
Ramin Ghorbani, Marcel J. T. Reinders, David M. J. Tax
Self Supervised Learning Supervised Learning PPG Signal Inter Subject

December 6, 2022

Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning
Hongbin Liu, Wenjie Qu, Jinyuan Jia, Neil Zhenqiang Gong
Self Supervised Learning Pre Trained Supervised Learning Encoder Side Inference Attack Secure Approach

December 3, 2022

Semi-supervised Learning with Robust Loss in Brain Segmentation
Hedong Zhang, Anand A. Joshi
Deep Learning Model Semi Supervised Learning Semi Supervised Supervised Learning Brain Segmentation Robust Loss

November 30, 2022

An Empirical Study on the Efficacy of Deep Active Learning for Image Classification
Yu Li, Muxi Chen, Yannan Liu, Daojing He, Qiang Xu
Image Classification Empirical Study Supervised Learning Unlabeled Data Deep Active Learning Semi Supervised Training

November 26, 2022

Enhancing Constraint Programming via Supervised Learning for Job Shop Scheduling
Yuan Sun, Su Nguyen, Dhananjay Thiruvady, Xiaodong Li, Andreas T. Ernst, Uwe Aickelin
Supervised Learning Constraint Programming Constraint Satisfaction Job Shop Scheduling Variable Ordering

November 24, 2022

PC-SNN: Supervised Learning with Local Hebbian Synaptic Plasticity based on Predictive Coding in Spiking Neural Networks
Mengting Lan, Xiaogang Xiong, Zixuan Jiang, Yunjiang Lou
Spiking Neural Network Supervised Learning Neuromorphic Hardware Predictive Coding Hebbian Plasticity Local Learning Rule

Supervised Learning

Papers

Mixed Semi-Supervised Generalized-Linear-Regression with applications to Deep-Learning and Interpolators

Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least

Tragic and Comical Networks. Clustering Dramatic Genres According to Structural Properties

Evaluating Self-Supervised Learning via Risk Decomposition

The SSL Interplay: Augmentations, Inductive Bias, and Generalization

L'explicabilit\'e au service de l'extraction de connaissances : application \`a des donn\'ees m\'edicales

Meta Temporal Point Processes

Interpreting learning in biological neural networks as zero-order optimization method

Transforming Unstructured Text into Data with Context Rule Assisted Machine Learning (CRAML)

Gated Self-supervised Learning For Improving Supervised Learning

Text classification in shipping industry using unsupervised models and Transformer based supervised models

BTS: Bifold Teacher-Student in Semi-Supervised Learning for Indoor Two-Room Presence Detection Under Time-Varying CSI

Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysis

Learning on Persistence Diagrams as Radon Measures

Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability

Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning

Semi-supervised Learning with Robust Loss in Brain Segmentation

An Empirical Study on the Efficacy of Deep Active Learning for Image Classification

Enhancing Constraint Programming via Supervised Learning for Job Shop Scheduling

PC-SNN: Supervised Learning with Local Hebbian Synaptic Plasticity based on Predictive Coding in Spiking Neural Networks