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

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

June 18, 2023

The Importance of Human-Labeled Data in the Era of LLMs
Yang Liu
Large Language Model Machine Learning Model Supervised Learning Importance Aware New Era Human Labeled Human Labelling

June 16, 2023

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, Shirui Pan
Self Supervised Learning Self Supervised Time Series Supervised Learning Comprehensive Taxonomy Time Series Data Much Progress Time Series Analysis Visionary ProSpect

June 10, 2023

Any-dimensional equivariant neural networks
Eitan Levin, Mateo Díaz
Supervised Learning Inter Part Equivariance Equivariant Neural Network Input Output Pair Stationary Representation

June 6, 2023

Supervised Knowledge May Hurt Novel Class Discovery Performance
Ziyun Li, Jona Otholt, Ben Dai, Di Hu, Christoph Meinel, Haojin Yang
Self Supervised Learning Supervised Learning Semantic Similarity Novel Class Discovery

May 31, 2023

Additional Positive Enables Better Representation Learning for Medical Images
Dewen Zeng, Yawen Wu, Xinrong Hu, Xiaowei Xu, Jingtong Hu, Yiyu Shi
Representation Learning Medical Image Supervised Learning Self Supervised Model Sample Gradient

May 30, 2023

Audio classification using ML methods
Krishna Kumar
Machine Learning Supervised Learning Classification Task Machine Learning System Audio Classification

May 26, 2023

On the Generalization and Approximation Capacities of Neural Controlled Differential Equations
Linus Bleistein, Agathe Guilloux
Neural Network Deep Neural Network Strong Generalization Supervised Learning Differential Equation Sampling Bias Approximation Power

May 24, 2023

Reverse Engineering Self-Supervised Learning
Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti, Shai Dekel, Yann LeCun
Self Supervised Supervised Learning Meaningful Representation Semantic Label Semantic Class

May 23, 2023

RLBoost: Boosting Supervised Models using Deep Reinforcement Learning
Eloy Anguiano Batanero, Ángela Fernández Pascual, Álvaro Barbero Jiménez
Deep Reinforcement Learning Supervised Learning Data Quality

May 17, 2023

How does Contrastive Learning Organize Images?
Yunzhe Zhang, Yao Lu, Qi Xuan
Contrastive Learning Contrastive Loss Supervised Learning Inductive Bias

May 15, 2023

Double-Weighting for Covariate Shift Adaptation
José I. Segovia-Martín, Santiago Mazuelas, Anqi Liu
Supervised Learning Generalization Bound Covariate Shift Covariate Shift Adaptation Minimax Risk Classifier

May 10, 2023

May 4, 2023

Towards Weakly-Supervised Hate Speech Classification Across Datasets
Yiping Jin, Leo Wanner, Vishakha Laxman Kadam, Alexander Shvets
Supervised Learning Hate Speech Weak Supervision Text Classification Model