CIRCOL selects a minimum-energy dictionary basis spanning detected H1 classes via a density-corrected cochain inner product proven consistent for fixed smooth 1-forms under non-uniform sampling.
hub
Automatic generation of atomic consistency preserving searchoperators for search-based model engi- neering - accompanying data
13 Pith papers cite this work, alongside 193 external citations. Polarity classification is still indexing.
13
Pith papers citing it
193
external citations · OpenAlex
hub tools
representative citing papers
Quantum Fourier generative models are trained classically at over 1000-qubit scale using log-likelihood loss from Parseval's identity and deployed on superconducting hardware for fast sampling that preserves multi-modal structure.
MPD²-Router is a dual-head deferral router that uses mask-aware Gumbel-sigmoid gating, asymmetric cost-sensitive training, and rank-majorization regularization to lower clinical cost and raise MCC versus AI-only baselines while balancing expert utilization across three glaucoma cohorts.
A code-and-comment analysis method detects semantic clones in Solidity functions with 59% overall precision (84% for same-name functions) and 97% recall on 300k contracts, plus LLM summaries for uncommented code.
A closed-loop framework jointly optimizes molecular composition and geometry in multi-component systems, demonstrated by a 30% reduction in activation barrier for a Claisen rearrangement via post-hoc validation.
In XX spin chains with open boundaries, a local quench via a single-spin impurity prevents thermalization and produces a strong violation of the eigenstate thermalization hypothesis, including its weak version.
A generalized approach to automatically generate atomic consistency preserving search operators (aCPSOs) for search-based model engineering that perform comparably or better than manual operators in case studies.
MALOQ introduces a scalable SO(2)-equivariant ML framework with custom kernels and edge-wise graph distribution for predicting large-scale quantum transport operators.
Qualitative analysis of 306 rejected AI-generated PRs reveals 14 rejection reasons in four categories, highlighting needs for better guidance on implementation, validation, and prioritization.
A DFTB+MACE model that replaces the pairwise repulsive term with a trained many-body potential improves forces, phonons, and surface energies for MgO and related systems while retaining electronic-structure output.
Feedback Former improves cell image segmentation accuracy by feeding detailed feature maps back from near the output to lower transformer layers, outperforming non-feedback baselines with lower computational cost on three datasets.
A new 'Artificial Special Intelligence' method is claimed to enable error-free training of classification models to 100% accuracy on 15 of 18 MedMNIST biomedical datasets.
citing papers explorer
-
Selecting Interpretable Circular Coordinates from Data
CIRCOL selects a minimum-energy dictionary basis spanning detected H1 classes via a density-corrected cochain inner product proven consistent for fixed smooth 1-forms under non-uniform sampling.
-
Quantum Fourier Generative Models Trainable at Large Scale
Quantum Fourier generative models are trained classically at over 1000-qubit scale using log-likelihood loss from Parseval's identity and deployed on superconducting hardware for fast sampling that preserves multi-modal structure.
-
MPD$^2$-Router: Mask-aware Multi-expert Prior-regularized Dual-head Deferral Router in Glaucoma Screening and Diagnosis
MPD²-Router is a dual-head deferral router that uses mask-aware Gumbel-sigmoid gating, asymmetric cost-sensitive training, and rank-majorization regularization to lower clinical cost and raise MCC versus AI-only baselines while balancing expert utilization across three glaucoma cohorts.
-
Identifying and Characterizing Semantic Clones of Solidity Functions
A code-and-comment analysis method detects semantic clones in Solidity functions with 59% overall precision (84% for same-name functions) and 97% recall on 300k contracts, plus LLM summaries for uncommented code.
-
Hierarchical generative modeling for the design of multi-component systems
A closed-loop framework jointly optimizes molecular composition and geometry in multi-component systems, demonstrated by a 30% reduction in activation barrier for a Claisen rearrangement via post-hoc validation.
-
Absence of thermalization after a local quench and strong violation of the eigenstate thermalization hypothesis
In XX spin chains with open boundaries, a local quench via a single-spin impurity prevents thermalization and produces a strong violation of the eigenstate thermalization hypothesis, including its weak version.
-
Automatic Generation of Atomic Consistency Preserving Search Operators for Search-Based Model Engineering
A generalized approach to automatically generate atomic consistency preserving search operators (aCPSOs) for search-based model engineering that perform comparably or better than manual operators in case studies.
-
MALOQ: Massively Accelerated Learning of Operators for Quantum Transport
MALOQ introduces a scalable SO(2)-equivariant ML framework with custom kernels and edge-wise graph distribution for predicting large-scale quantum transport operators.
-
Understanding the Rejection of Fixes Generated by Agentic Pull Requests -- Insights from the AIDev Dataset
Qualitative analysis of 306 rejected AI-generated PRs reveals 14 rejection reasons in four categories, highlighting needs for better guidance on implementation, validation, and prioritization.
-
A Combined Tight Binding with Machine Learning Potential Model for Magnesium Compounds
A DFTB+MACE model that replaces the pairwise repulsive term with a trained many-body potential improves forces, phonons, and surface energies for MgO and related systems while retaining electronic-structure output.
-
Accuracy Improvement of Cell Image Segmentation Using Feedback Former
Feedback Former improves cell image segmentation accuracy by feeding detailed feature maps back from near the output to lower transformer layers, outperforming non-feedback baselines with lower computational cost on three datasets.
-
Benchmarking PNW Model for MedMNIST to 100% Accuracy
A new 'Artificial Special Intelligence' method is claimed to enable error-free training of classification models to 100% accuracy on 15 of 18 MedMNIST biomedical datasets.
- Nonequilibrium electron-phonon dynamics with high momentum resolution: Thermalization bottlenecks and the effects of phonon dispersion