A new corpus of 108 mixed string-numeric tables shows that advanced tabular learners with basic string embeddings perform well on most real-world data, while large LLM encoders help on free-text heavy tables.
and Cadima, Jorge , title =
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Presents a quantum soft PCA framework with Fermi-Dirac filter for principal subspace scoring without eigenvector recovery, claiming dimension-independent sample complexity O(η^{-2}).
Intrinsic dimension of quantum trajectories serves as an unsupervised probe sensitive to chaos, integrability, and ergodicity breaking in dissipative quantum systems.
A new GPU-oriented batch SVD solver based on the one-sided Jacobi method delivers significant speedups over vendor libraries and prior open-source implementations across precisions and matrix shapes.
DIVE proposes a dimensionality-reduction adapter using self-limiting gradients and implicit view ensembles that outperforms prior adapters on all six BEIR datasets at every tested compression ratio.
In one Parkinson patient, higher occlusion produced the smallest longitudinal shift in PCA gait latent space over 11 weeks while immediate performance stayed comparable, supporting a viability level focused on sustained organization.
Converting percentage scores to A/B/C/D grades reduces information entropy by 69 percent, makes optimal student clusters sensitive to single data points, and drops temporal diagnostic consistency from 93-96 percent to 52-96 percent.
TBER describes representational emergence as a five-stage bootstrap process triggered by explanatory insufficiency in AI, biology, and science.
In one Parkinsonian subject, a neural network approximates the observed shift in PCA gait latent space between two sessions across six occlusal conditions.
A conceptual bootstrap framework with five levels is introduced to derive increasingly informative latent representations from performance data in adaptive biological systems, illustrated via prior gait studies.
citing papers explorer
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STRABLE: Benchmarking Tabular Machine Learning with Strings
A new corpus of 108 mixed string-numeric tables shows that advanced tabular learners with basic string embeddings perform well on most real-world data, while large LLM encoders help on free-text heavy tables.
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Quantum principal component analysis without eigenvector recovery
Presents a quantum soft PCA framework with Fermi-Dirac filter for principal subspace scoring without eigenvector recovery, claiming dimension-independent sample complexity O(η^{-2}).
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Complexity of Quantum Trajectories
Intrinsic dimension of quantum trajectories serves as an unsupervised probe sensitive to chaos, integrability, and ergodicity breaking in dissipative quantum systems.
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An Efficient Batch Solver for the Singular Value Decomposition on GPUs
A new GPU-oriented batch SVD solver based on the one-sided Jacobi method delivers significant speedups over vendor libraries and prior open-source implementations across precisions and matrix shapes.
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DIVE: Embedding Compression via Self-Limiting Gradient Updates
DIVE proposes a dimensionality-reduction adapter using self-limiting gradients and implicit view ensembles that outperforms prior adapters on all six BEIR datasets at every tested compression ratio.
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From Organization to Viability: A Multi-Level Analysis of Gait Dynamics Under Occlusal Constraint
In one Parkinson patient, higher occlusion produced the smallest longitudinal shift in PCA gait latent space over 11 weeks while immediate performance stayed comparable, supporting a viability level focused on sustained organization.
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Data Aphasia: An Institutional Counterfactual Study of the Stability of Academic Cognition Under Letter-Grade Evaluation Systems
Converting percentage scores to A/B/C/D grades reduces information entropy by 69 percent, makes optimal student clusters sensitive to single data points, and drops temporal diagnostic consistency from 93-96 percent to 52-96 percent.
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Bootstrap Theory of Representational Emergence: Explanatory Insufficiency as a Driver of Representation Learning and World Models
TBER describes representational emergence as a five-stage bootstrap process triggered by explanatory insufficiency in AI, biology, and science.
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From Observed Viability to Internal Predictive Approximation: A Single-Subject Latent-Space Analysis of Gait Dynamics Under Occlusal Constraint
In one Parkinsonian subject, a neural network approximates the observed shift in PCA gait latent space between two sessions across six occlusal conditions.
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From Performance to Viability: A Bootstrap Framework for Latent-Space Representation Learning in Adaptive Biological Systems
A conceptual bootstrap framework with five levels is introduced to derive increasingly informative latent representations from performance data in adaptive biological systems, illustrated via prior gait studies.