Two steps of gradient descent on first-layer weights in linear-width two-layer networks produce a spiked random matrix with floor(alpha2/(1/2-alpha1)) outliers, each a learned direction, and batch reuse allows capturing directions with information exponent exceeding one.
Proceedings of the IEEE conference on computer vision and pattern recognition , pages=
6 Pith papers cite this work. Polarity classification is still indexing.
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Natural language descriptions generated via a closed-loop pipeline with digital twins capture the selectivity of most neurons in macaque V1 and V4, with synthesized images driving 96% of V4 neurons into the top or bottom 5% of natural-image response distributions.
QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markovian datasets.
Semi-LAR is a semi-supervised contrastive learning framework with linear attention for nighttime flare removal that refines pseudo-labels via quality assessment and uses flare-aware patch-level contrastive losses.
A drone-mounted stereo camera pipeline with YOLO segmentation, deep stereo depth, centroid triangulation, and MAD outlier rejection achieves robust 3D positioning of thin pine branches at 1-2 m distances.
Optimizes ImageNet-pretrained AlexNet, UMAP, and a bagging multi-cluster voting scheme with K-means, Birch and Agg for unsupervised galaxy morphology classification, reporting improved stability and consistency with galaxy evolution expectations.
citing papers explorer
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Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent
Two steps of gradient descent on first-layer weights in linear-width two-layer networks produce a spiked random matrix with floor(alpha2/(1/2-alpha1)) outliers, each a learned direction, and batch reuse allows capturing directions with information exponent exceeding one.
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Letting the neural code speak: Automated characterization of monkey visual neurons through human language
Natural language descriptions generated via a closed-loop pipeline with digital twins capture the selectivity of most neurons in macaque V1 and V4, with synthesized images driving 96% of V4 neurons into the top or bottom 5% of natural-image response distributions.
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QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markovian datasets.
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Semi-LAR: Semi-supervised Contrastive Learning with Linear Attention for Removal of Nighttime Flares
Semi-LAR is a semi-supervised contrastive learning framework with linear attention for nighttime flare removal that refines pseudo-labels via quality assessment and uses flare-aware patch-level contrastive losses.
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Low-Cost Stereo Vision for Robust 3D Positioning of Thin Radiata Pine Branches in Autonomous Drone Pruning
A drone-mounted stereo camera pipeline with YOLO segmentation, deep stereo depth, centroid triangulation, and MAD outlier rejection achieves robust 3D positioning of thin pine branches at 1-2 m distances.
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Robustness Analysis of USmorph: II. Optimizing Feature Extraction, Dimensionality Reduction, and Clustering for Unsupervised Galaxy Morphology Classification
Optimizes ImageNet-pretrained AlexNet, UMAP, and a bagging multi-cluster voting scheme with K-means, Birch and Agg for unsupervised galaxy morphology classification, reporting improved stability and consistency with galaxy evolution expectations.