Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:40:28.790015Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2506.04673.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:40:28.790015Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
46 of 46 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 85c08559-0ce4-4aa3-800a-b11ad1901db7 · outbound
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts This looks like that: deep learning for interpretable image recognition,
Reference 1
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Observation b7fd64db-9ef2-4593-927e-73da633b46c8 · outbound
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Learning transferable conceptual prototypes for interpretable unsupervised domain adaptation,
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Describe, spot and explain: Interpretable representation learning for discriminative visual reasoning,
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts An interpretable deep architecture for similarity learning built upon hierarchical concepts,
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Joint answering and explanation for visual commonsense reasoning,
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Interpretable image recognition by constructing transparent embedding space,
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Observation 0b6c944f-89dc-46af-9e6d-e2ae145f41d6 · outbound
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts A grass- mann manifold handbook: Basic geometry and computational aspects,
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Pip- net: Patch-based intuitive prototypes for interpretable image classification,
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Reference 12
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Observation caafa0f5-1878-4aad-8a5e-d8faf0daee3f · outbound
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts An image is worth 16x16 words: Transformers for image recognition at scale,
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Observation 72cdea4c-1a81-4610-9ac1-8266c4f65db1 · outbound
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Learning to prompt for vision-language models,
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Conditional prompt learning for vision-language models,
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Visual-language prompt tuning with knowledge-guided context optimization,
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Prompt- aligned gradient for prompt tuning,
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Reference 18
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Reference 19
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Task resid- ual for tuning vision-language models,
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Adaptive mixtures of local experts,
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Hierarchical mixtures of experts and the em algorithm,
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Nonlinear models using dirichlet process mixtures,
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts A parallel mixture of svms for very large scale problems,
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Reference 25
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Observation b5d53f13-18ca-4c13-9e93-b52f5988ff7f · outbound
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Reference 26
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Reference 27
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Observation 424d5a08-7bbf-4703-9097-85dbac2ea2e0 · outbound
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Reference 29
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Reference 30
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Observation fa188cab-b1aa-441d-b0a0-f5f9ac0a0a02 · outbound
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Reference 33
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Observation 572f2ffa-91f8-48a1-84e6-94ae436f645f · outbound
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Matching networks for one shot learning,
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Few-Shot Learning with Metric-Agnostic Conditional Embeddings
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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Describing textures in the wild,
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Reference 46
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No inbound Pith citation observations are available.