X4Val learns transferable neural predictors from non-paired multi-domain data and incorporates them into control-variates estimators to reduce variance in real-world robotic policy evaluation by up to 38.4%.
arXiv preprint arXiv:2201.05867 (2022)
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RADAR is a geometrically grounded metric that predicts cross-domain transferability by comparing layer-wise representation trajectory distributions in foundation models.
Synthetic noise domains serve as surrogate sources to tighten generalization bounds and improve performance in semi-supervised target domains via the proposed Noise Adaptation Framework.
A comparative review with experiments identifying optimal preprocessing, models, and transfer strategies for large-scale pixel-wise crop mapping using Landsat 8 data across five sites.
A systematic survey of over 200 works on deep learning and AI techniques for crops, fisheries, and livestock in agriculture.
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X4Val: Learning Neural Surrogates for Variance-Reduced Policy Evaluation
X4Val learns transferable neural predictors from non-paired multi-domain data and incorporates them into control-variates estimators to reduce variance in real-world robotic policy evaluation by up to 38.4%.
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RADAR: Relative Angular Divergence Across Representations
RADAR is a geometrically grounded metric that predicts cross-domain transferability by comparing layer-wise representation trajectory distributions in foundation models.
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Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain
Synthetic noise domains serve as surrogate sources to tighten generalization bounds and improve performance in semi-supervised target domains via the proposed Noise Adaptation Framework.
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From Time-series Generation, Model Selection to Transfer Learning: A Comparative Review of Pixel-wise Approaches for Large-scale Crop Mapping
A comparative review with experiments identifying optimal preprocessing, models, and transfer strategies for large-scale pixel-wise crop mapping using Landsat 8 data across five sites.
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AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock
A systematic survey of over 200 works on deep learning and AI techniques for crops, fisheries, and livestock in agriculture.