supervised-learning

DualRes is a compact oscillatory state-space model for machine fault diagnosis from vibration data, achieving state-of-the-art performance with limited labels and reduced computational requirements for edge deployment.

The paper demonstrates that supervised fine-tuning can correct mode collapse and over-dispersion in large language models by showing that diversity converges to the target distribution with sufficient data, supported by theoretical bounds and experiments.

The paper proposes a framework for processing and classifying invasive bird species vocalizations using Bayesian wavelet shrinkage and supervised learning models, with SVM achieving high accuracy in classification.

The paper proposes 'unit' as an explicit primitive in machine learning, where learning tasks declare persistent individuals, and supervised learning specializes to unit-conditioned response laws with tokenization.

This paper proposes a supervised structure learning method for training agent-curated knowledge bases, treating the store as a model to improve retrieval accuracy and reduce action usage in retrieval-augmented generation systems.

The paper proposes a supervised machine learning model for estimating helicopter weight during takeoff, using data from Airbus's fleet, and details its implementation for on-board use with regulatory compliance.

This study compares zero-shot prompting of LLMs against supervised baselines for detecting shared decision-making in pediatric clinical encounters, finding that supervised models outperform zero-shot approaches and highlighting critical data leakage issues in evaluation pipelines.

This research revisits likelihood-based data selection for reasoning fine-tuning, showing that its effectiveness depends on model capacity and training duration, with a capacity-dependent 'Fast-Fit/Slow-Gain' pattern observed.

MIITA is a memory-induced inference-time adaptation framework for continual learning with small language models. It stores correction-direction prototypes and applies gated hidden-state adaptation at inference time to mitigate catastrophic forgetting without updating backbone parameters.

This paper investigates the use of large language models (LLMs) and supervised classifiers for depression detection from social media text, proposing a prompt-based embedding method that enhances interpretability. Experiments on multiple datasets show that zero-shot LLMs perform well for binary classification but struggle with fine-grained severity, while supervised models on LLM summary embeddings achieve more consistent performance across multi-class and ordinal tasks.

This paper investigates using supervised learning to build an oracle that decides when to apply the computationally expensive Energetic Reasoning propagator in constraint programming, showing high prediction accuracy and highlighting key design choices.

This paper characterizes the inherent interpretability of linear models vs. single-qubit mixed-state models for binary classification, showing that the quantum model learns a hyperellipsoid instead of a hyperplane, with implications for inductive biases and pedagogy.

Proposes Partially Adjudicated Design-Based Supervised Learning (PA-DSL), a method that corrects noisy human labels using a small set of adjudicated cases to debias automated classifiers, achieving nominal coverage and reducing RMSE by 10-17% in experiments.

This paper consolidates the state of the art in supervised political scaling, investigating whether joint prediction of ideological scales and a middle ground between classification and regression can improve performance.

The paper argues that data-driven machine learning systems, including GPT-5, cannot achieve symbolic-level logical reasoning through scaling alone, due to inherent limitations in distinguishing logical structures from statistical regularities.

This paper proposes a Supervised Reinforcement Learning (SRL) framework for coordinating distributed energy resources, pre-training on demonstration data and fine-tuning with RL to improve sample efficiency and performance.

Introduces NatureBench, a cross-disciplinary benchmark of 90 tasks from Nature papers to test AI coding agents, finding the best agent (Claude Opus 4.7) surpasses SOTA on only 17.8% of tasks and often succeeds by reducing science to supervised ML rather than genuine discovery.

This paper uses layer-wise probing to investigate how wav2vec 2.0 and Whisper encode consonant cluster reduction in African American English, finding that both models distinguish reduced and canonical forms and preserve cues to underlying stops.

This paper models how interactive targeted ads can leak user attribute information through observable interactions, and evaluates Bayesian, supervised, and other attack methods on synthetic data. It also discusses disclosure controls as a defense.

This paper evaluates cross-dataset generalization of supervised ML/DL models and prompted LLMs for automatic Bloom's taxonomy classification of assessment questions, finding that LLMs are more robust across diverse educational contexts.