SEMIR replaces dense voxel computation with a learned topology-preserving graph minor that supports exact decoding and GNN-based inference for small-structure segmentation in large medical images.
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8 Pith papers cite this work. Polarity classification is still indexing.
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A mixture model with adaptive KDE and per-image cross-validation raises estimated human fixation consistency by 5-15% median log-likelihood and up to 2 AUC points over fixed-bandwidth Gaussian baselines.
A single Bowden cable passively tensioned by a torsional spring enables compact wrist abduction-adduction actuation, with simulation-guided stiffness selection validated through user experiments showing consistent motion and torque performance.
Unsupervised discriminator-guided fine-tuning of a pretrained u-sleep model improves Cohen's kappa by 0.03-0.29 on artificially degraded sleep signals but falls short of supervised optima and yields insignificant gains on real domain mismatches.
Neural networks learn via sparse retrospective updates triggered internally when prediction error exceeds a threshold derived from recent error statistics, leading to stepwise parameter changes in simulations.
Wrist abduction-adduction assistance in a tongue-controlled 6-DoF upper-limb exoskeleton improves task success rates and reduces spillage and failures for ALS and SCI users without increasing discomfort.
Adding an active tendon-driven wrist Ab-Ad joint to a 5 DoF exoskeleton cut drinking spillage from 56% to 3% and raised scratching leveling success from 28% to 75% in controlled tests.
A-ROM delivers competitive MedMNIST performance via pretrained ViT metric spaces, a concept dictionary, and kNN without backpropagation or fine-tuning, framed as interpretable few-shot learning under the Platonic Representation Hypothesis.
citing papers explorer
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SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentation
SEMIR replaces dense voxel computation with a learned topology-preserving graph minor that supports exact decoding and GNN-based inference for small-structure segmentation in large medical images.
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Raising the Ceiling: Better Empirical Fixation Densities for Saliency Benchmarking
A mixture model with adaptive KDE and per-image cross-validation raises estimated human fixation consistency by 5-15% median log-likelihood and up to 2 AUC points over fixed-bandwidth Gaussian baselines.
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Design, Modelling and Experimental Evaluation of a Tendon-driven Wrist Abduction-Adduction Mechanism for an upper limb exoskeleton
A single Bowden cable passively tensioned by a torsional spring enables compact wrist abduction-adduction actuation, with simulation-guided stiffness selection validated through user experiments showing consistent motion and torque performance.
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Unsupervised domain transfer: Overcoming signal degradation in sleep monitoring by increasing scoring realism
Unsupervised discriminator-guided fine-tuning of a pretrained u-sleep model improves Cohen's kappa by 0.03-0.29 on artificially degraded sleep signals but falls short of supervised optima and yields insignificant gains on real domain mismatches.
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Internally triggered retrospective learning in neural networks
Neural networks learn via sparse retrospective updates triggered internally when prediction error exceeds a threshold derived from recent error statistics, leading to stepwise parameter changes in simulations.
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Clinical Evaluation of a Tongue-Controlled Wrist Abduction-Adduction Assistance in a 6-DoF Upper-Limb Exoskeleton for Individuals with ALS and SCI
Wrist abduction-adduction assistance in a tongue-controlled 6-DoF upper-limb exoskeleton improves task success rates and reduces spillage and failures for ALS and SCI users without increasing discomfort.
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A Tendon-Driven Wrist Abduction-Adduction Joint Improves Performance of a 5 DoF Upper Limb Exoskeleton -- Implementation and Experimental Evaluation
Adding an active tendon-driven wrist Ab-Ad joint to a 5 DoF exoskeleton cut drinking spillage from 56% to 3% and raised scratching leveling success from 28% to 75% in controlled tests.
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Toward Aristotelian Medical Representations: Backpropagation-Free Layer-wise Analysis for Interpretable Generalized Metric Learning on MedMNIST
A-ROM delivers competitive MedMNIST performance via pretrained ViT metric spaces, a concept dictionary, and kNN without backpropagation or fine-tuning, framed as interpretable few-shot learning under the Platonic Representation Hypothesis.