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ModelLakeFishing framework enables efficient retrieval from million-scale model lakes

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ModelLakeFishing, a novel framework designed to efficiently retrieve suitable models from vast collections, known as model lakes, which can contain millions of reusable models. This system consolidates metadata and historical performance data into a graph, learns embeddings for models and queries, and uses a Hierarchical Navigable Small World (HNSW) index for rapid candidate retrieval. The framework prioritizes speed by initially retrieving 1,000 candidates without exhaustive scoring, followed by a reranking process to identify the top 10 models based on specific query criteria such as dataset, task, and evaluation metric. AI

IMPACT Enables faster discovery and utilization of pre-existing models, potentially accelerating research and development cycles.

RANK_REASON The cluster contains a research paper detailing a new framework for model retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ModelLakeFishing framework enables efficient retrieval from million-scale model lakes

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The cluster contains a research paper detailing a new framework for model retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Renée J. Miller ·

    ModelLakeFishing: Efficient Retrieval over Million-Scale Model Lakes

    Open model lakes may contain millions of reusable models, making it costly to identify suitable models for a new dataset. We present ModelLakeFishing, a model-retrieval framework for queries specifying a target dataset, prediction task, and evaluation metric. It consolidates meta…

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