A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Kejia Zhang, Youran Sun, Xinyu Ren, et al.
AutoSR automates symbolic regression by searching persistent research states, preserving scientific evidence to recover algebraically equivalent relations across nine benchmark challenges.
Amgad Muneer, Suliman Mohamed Fati
This paper compares seven machine learning classifiers for detecting cyberbullying on Twitter using a global dataset of 37,373 tweets, finding Logistic Regression achieves the best accuracy and F1 score.
Unknown
This paper studies model-based reinforcement learning for regret minimization in finite-hizon episodic settings with a known transition model family, proposing a value-targeted regression approach.
Ilya Kostrikov, Ashvin Nair, S. Levine
Introduces Implicit Q-Learning (IQL), an offline RL algorithm that avoids evaluating actions outside the dataset by using expectile regression and does not require unseen actions.
Unknown
This paper presents new efficient protocols for privacy-preserving machine learning for linear regression, logistic regression, and neural network training using stochastic gradient descent.
Karel G.M. Moons, Johanna AAG Damen, T. K. Kaul, et al.
PROBAST+AI is an updated tool for assessing quality, risk of bias, and applicability of prediction models using regression or AI, with separate signaling questions for model development and evaluation.
John Hancock, Taghi M. Khoshgoftaar
This paper provides the first interdisciplinary survey of CatBoost for big data, covering both its strengths and weaknesses in classification and regression tasks.
Yan Yan, Yuxing Mao, Bo Li
Proposes SECOND, a sparse convolutional network with angle loss regression and data augmentation for fast and accurate 3D object detection from LiDAR.
Unknown
A comprehensive survey of mixture of experts models for regression and classification, including training methods.
Chiara Pavese, Lucas M. Bachmann, Martin Schubert, et al.
This paper develops and validates a logistic regression model using the ISNCSCI total motor score to predict independent bowel management one year after traumatic spinal cord injury.