regression
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Benchmarks
Most implemented
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Weight Uncertainty in Neural Networks
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
Papers
Language Integration in Fine-Tuning Multimodal Large Language Models for Image-Based Regression
Multimodal Large Language Models (MLLMs) show promise for image-based regression tasks, but current approaches face key limitations. Recent methods fine-tune MLLMs using preset output vocabularies and generic task-level …
Aesthetics Quality AssessmentNo-Reference Image Quality AssessmentregressionNeural Network-Guided Symbolic Regression for Interpretable Descriptor Discovery in Perovskite Catalysts
Understanding and predicting the activity of oxide perovskite catalysts for the oxygen evolution reaction (OER) requires descriptors that are both accurate and physically interpretable. While symbolic regression (SR) off…
Feature ImportanceregressionSymbolic RegressionImbalanced Regression Pipeline Recommendation
Imbalanced problems are prevalent in various real-world scenarios and are extensively explored in classification tasks. However, they also present challenges for regression tasks due to the rarity of certain target value…
AutoMLMeta-LearningregressionSecond-Order Bounds for [0,1]-Valued Regression via Betting Loss
We consider the $[0,1]$-valued regression problem in the i.i.d. setting. In a related problem called cost-sensitive classification, \citet{foster21efficient} have shown that the log loss minimizer achieves an improved ge…
Distributional Reinforcement LearningregressionSparse Regression Codes exploit Multi-User Diversity without CSI
We study sparse regression codes (SPARC) for multiple access channels with multiple receive antennas, in non-coherent flat fading channels. We propose a novel practical decoder, referred to as maximum likelihood matching…
DecoderDiversityregressionBradley-Terry and Multi-Objective Reward Modeling Are Complementary
Reward models trained on human preference data have demonstrated strong effectiveness in aligning Large Language Models (LLMs) with human intent under the framework of Reinforcement Learning from Human Feedback (RLHF). H…
Attributeregression