ULPT optimizes prompts in ultra-low dimensions with frozen random up-projection to cut training parameters by 98% while matching vanilla prompt tuning performance on NLP tasks.
Approximate nearest neighbors: tow ards removing the curse of dimensionality
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
A pipeline using product quantization and systematic parameter evaluation creates data-driven soil taxonomies with higher specificity than human-derived classifications.
Dask parallelization of product quantization and inverted indexing allows large-scale approximate nearest neighbor search while preserving accuracy and reducing computation to medium-scale levels.
The paper analyzes participant opinions from a Physics of Life Reviews discussion on the simplicity revolution in high-dimensional neuroscience and its implications for machine learning.
citing papers explorer
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Ultra-Low-Dimensional Prompt Tuning via Random Projection
ULPT optimizes prompts in ultra-low dimensions with frozen random up-projection to cut training parameters by 98% while matching vanilla prompt tuning performance on NLP tasks.
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Product Quantization for Surface Soil Similarity
A pipeline using product quantization and systematic parameter evaluation creates data-driven soil taxonomies with higher specificity than human-derived classifications.
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Large-Scale Data Parallelization of Product Quantization and Inverted Indexing Using Dask
Dask parallelization of product quantization and inverted indexing allows large-scale approximate nearest neighbor search while preserving accuracy and reducing computation to medium-scale levels.
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Symphony of high-dimensional brain
The paper analyzes participant opinions from a Physics of Life Reviews discussion on the simplicity revolution in high-dimensional neuroscience and its implications for machine learning.