Fixing the visual encoder in multilingual CLIP isolates text-branch deficits as the cause of lower visual grounding performance for low-resource languages, with model scaling widening some gaps but not others.
Towards the systematic reporting of the energy and carbon footprints of machine learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Ensemble methods improve recommender accuracy by 0.3-5.7% but raise energy use by 19-2549%, with selective strategies being more efficient than full averaging.
citing papers explorer
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Language-Conditioned Visual Grounding with CLIP Multilingual
Fixing the visual encoder in multilingual CLIP isolates text-branch deficits as the cause of lower visual grounding performance for low-resource languages, with model scaling widening some gaps but not others.
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Ensembles at Any Cost? Accuracy-Energy Trade-offs in Recommender Systems
Ensemble methods improve recommender accuracy by 0.3-5.7% but raise energy use by 19-2549%, with selective strategies being more efficient than full averaging.