A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
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Mixed citation behavior. Most common role is method (57%).
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baseline 1
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representative citing papers
Introduces Calibrated Size Ratio (CSR) and confidence-weighted metrics to better detect overconfidence risk and calibration issues beyond the limitations of ECE.
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
A low-stake adversary can degrade a liquid staking pool's performance via consensus manipulation and profit from the resulting drop in its LST value through application-layer financial positions.
Players exhibit consistent flexibility or specialization behavior across two games with conflicting performance incentives, indicating individual agency dominates structural differences.
An LLM-orchestrated physics simulation search identifies polymers with strong insulin interactions, outperforming standard optimization methods by significant margins.
AutoLLMResearch trains agents in a multi-fidelity LLMConfig-Gym environment formulated as a long-horizon MDP to enable cross-fidelity extrapolation for automating high-cost LLM experiment configurations.
Coverage tests for simulation-based inference of f_NL can pass while the posteriors are underconfident in the tails and sometimes yield weaker constraints than using power spectrum or bispectrum alone.
RL-STPA adapts STPA for RL via hierarchical subtask decomposition, coverage-guided perturbation testing, and iterative checkpoints that feed hazards back into training, demonstrated on autonomous drone navigation to reveal loss scenarios missed by standard evaluations.
CivBench trains models on turn-level states in Civilization V to predict victory probabilities, providing a progress-based evaluation of LLM strategic capabilities across 307 games with 7 models.
Physics-informed graph attention networks predict multi-phase equilibria in Ag-Bi-Cu-Sn alloys with 96% exact-set accuracy on in-domain data and strong generalization to unseen sections.
Unsupervised domain adaptation via feature alignment raises radioisotope identification accuracy on real LaBr3 gamma spectra from 0.754 to 0.904 for models trained only on synthetic data.
CAPS provides an iterative differentially private synthesis method that outperforms one-shot baselines on authentic educational real-world data.
Milky Way-mass dark matter density profiles in IllustrisTNG are largely insensitive to astrophysics and cosmology variations, dominated by halo-to-halo variance instead.
Tabular foundation models outperform standard methods in credit risk PD and LGD tasks, with larger gains on smaller datasets when used out-of-the-box.
ML regressors trained on APOGEE DR17 red giants predict C, O, Mg, Si abundances from kinematics and [Fe/H] more accurately than [Fe/H] baseline, with external validation on HARPS FGK dwarfs and reproduction of Galactic chemical evolution trends.
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
Symetra uses visual overviews and group comparison tools to help experts tune symbolic execution parameters, achieving higher branch coverage and faster tuning than fully automated methods.
FlaXifyer applies few-shot learning on pre-trained language models to categorize intermittent CI job failures from logs at 84.3% Macro F1 and 92.0% Top-2 accuracy using 12 examples per category, with LogSift reducing log review effort by 74.4%.
Spared lower-limb EMG after SCI was decoded to drive closed-loop FES, yielding 33-40% gains in foot flexion range and proportional control up to six stimulation levels in a small cohort.
Combines evolutionary algorithms and MPC to perform privacy-preserving distributed optimization under time limits, tested on assignment and traveling salesperson problems with optional result obfuscation.
FPRO applies Frenet-frame RL with curvature-torsion manufacturability constraints and PPO optimization to produce collision-free, fabricable pipe paths for aeroengines, outperforming Cartesian and baseline RL methods in experiments and real fabrication.
Pre-training GNNs on ECFP prediction produces statistically significant QSAR gains on five of six Biogen benchmarks with OOD splits, but underperforms on heterogeneous datasets and complex endpoints like binding affinity.
A neural network is trained to predict probabilities for lower mass gap components and neutron star involvement in gravitational-wave candidates, with reported mean errors of 9% and 6% on O4a events.
citing papers explorer
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NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework
A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
-
Beyond ECE: Calibrated Size Ratio, Risk Assessment, and Confidence-Weighted Metrics
Introduces Calibrated Size Ratio (CSR) and confidence-weighted metrics to better detect overconfidence risk and calibration issues beyond the limitations of ECE.
-
Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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Your Loss is My Gain: Low Stake Attacks on Liquid Staking Pools
A low-stake adversary can degrade a liquid staking pool's performance via consensus manipulation and profit from the resulting drop in its LST value through application-layer financial positions.
-
Change is Hard: Consistent Player Behavior Across Games with Conflicting Incentives
Players exhibit consistent flexibility or specialization behavior across two games with conflicting performance incentives, indicating individual agency dominates structural differences.
-
Towards Discovery of Polymers for Insulin Delivery via Physics-Grounded Agentic Workflows
An LLM-orchestrated physics simulation search identifies polymers with strong insulin interactions, outperforming standard optimization methods by significant margins.
-
AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration - Learning from Cheap, Optimizing Expensive
AutoLLMResearch trains agents in a multi-fidelity LLMConfig-Gym environment formulated as a long-horizon MDP to enable cross-fidelity extrapolation for automating high-cost LLM experiment configurations.
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Coverage is not enough: Frequentist tests of simulation-based inference for primordial non-Gaussianity
Coverage tests for simulation-based inference of f_NL can pass while the posteriors are underconfident in the tails and sometimes yield weaker constraints than using power spectrum or bispectrum alone.
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RL-STPA: Adapting System-Theoretic Hazard Analysis for Safety-Critical Reinforcement Learning
RL-STPA adapts STPA for RL via hierarchical subtask decomposition, coverage-guided perturbation testing, and iterative checkpoints that feed hazards back into training, demonstrated on autonomous drone navigation to reveal loss scenarios missed by standard evaluations.
-
CivBench: Progress-Based Evaluation for LLMs' Strategic Decision-Making in Civilization V
CivBench trains models on turn-level states in Civilization V to predict victory probabilities, providing a progress-based evaluation of LLM strategic capabilities across 307 games with 7 models.
-
Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks
Physics-informed graph attention networks predict multi-phase equilibria in Ag-Bi-Cu-Sn alloys with 96% exact-set accuracy on in-domain data and strong generalization to unseen sections.
-
Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy
Unsupervised domain adaptation via feature alignment raises radioisotope identification accuracy on real LaBr3 gamma spectra from 0.754 to 0.904 for models trained only on synthetic data.
-
Cyclic Adaptive Private Synthesis for Sharing Real-World Data in Education
CAPS provides an iterative differentially private synthesis method that outperforms one-shot baselines on authentic educational real-world data.
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The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Dark Matter Density Profiles
Milky Way-mass dark matter density profiles in IllustrisTNG are largely insensitive to astrophysics and cosmology variations, dominated by halo-to-halo variance instead.
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Foundation Models for Credit Risk Prediction: A Game Changer?
Tabular foundation models outperform standard methods in credit risk PD and LGD tasks, with larger gains on smaller datasets when used out-of-the-box.
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Inferring stellar metallicity and elemental abundances from kinematic and spectroscopic data using machine learning -- Implications for exoplanet host stars
ML regressors trained on APOGEE DR17 red giants predict C, O, Mg, Si abundances from kinematics and [Fe/H] more accurately than [Fe/H] baseline, with external validation on HARPS FGK dwarfs and reproduction of Galactic chemical evolution trends.
-
Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
-
Symetra: Visual Analytics for the Parameter Tuning Process of Symbolic Execution Engines
Symetra uses visual overviews and group comparison tools to help experts tune symbolic execution parameters, achieving higher branch coverage and faster tuning than fully automated methods.
-
Predicting Intermittent Job Failure Categories for Diagnosis Using Few-Shot Fine-Tuned Language Models
FlaXifyer applies few-shot learning on pre-trained language models to categorize intermittent CI job failures from logs at 84.3% Macro F1 and 92.0% Top-2 accuracy using 12 examples per category, with LogSift reducing log review effort by 74.4%.
-
Closed-loop Neuroprosthetic Control through Spared Neural Activity Enables Proportional Foot Movements after Spinal Cord Injury
Spared lower-limb EMG after SCI was decoded to drive closed-loop FES, yielding 33-40% gains in foot flexion range and proportional control up to six stimulation levels in a small cohort.
-
Privacy-Preserving Distributed Optimization Under Time Constraints Using Secure Multi-Party Computation and Evolutionary Algorithms
Combines evolutionary algorithms and MPC to perform privacy-preserving distributed optimization under time limits, tested on assignment and traveling salesperson problems with optional result obfuscation.
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Design for Manufacturing: A Manufacturability Knowledge-Integrated Reinforcement Learning Framework for Free-Form Pipe Routing in Aeroengines
FPRO applies Frenet-frame RL with curvature-torsion manufacturability constraints and PPO optimization to produce collision-free, fabricable pipe paths for aeroengines, outperforming Cartesian and baseline RL methods in experiments and real fabrication.
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On Improving Graph Neural Networks for QSAR by Pre-training on Extended-Connectivity Fingerprints
Pre-training GNNs on ECFP prediction produces statistically significant QSAR gains on five of six Biogen benchmarks with OOD splits, but underperforms on heterogeneous datasets and complex endpoints like binding affinity.
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Training a neural network to rapidly identify candidate gravitational-wave events in the lower mass gap
A neural network is trained to predict probabilities for lower mass gap components and neutron star involvement in gravitational-wave candidates, with reported mean errors of 9% and 6% on O4a events.
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Accelerating Nonlinear Time-History Analysis with Complex Constitutive Laws via Heterogeneous Memory Management: From 3D Seismic Simulation to Neural Network Training
A heterogeneous memory framework runs memory-intensive nonlinear ensemble simulations on CPU-GPU systems and supplies the resulting data for neural network surrogate training.
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Optimization with SpotOptim
spotoptim is an open-source Python package that implements a Kriging-based optimization loop with Expected Improvement, mixed-variable support, noise handling via OCBA, parallelization, and restart mechanisms for black-box optimization.
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