HiSS sampling uses logistic bridging in a Metropolis-within-Gibbs framework to enable transitions between disconnected modes while preserving the target discrete distribution.
[Yes] (b) All the training details (e.g., data splits, hy- perparameters, how they were chosen)
4 Pith papers cite this work. Polarity classification is still indexing.
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New conditional independence assumptions enable mixture proportion estimation and kernel tests for conditional independence without relying on irreducibility.
Defining the Rashomon set for dimension reduction enables interpretable, robust visualizations by aligning embeddings with known structure and extracting consistent local relationships across multiple good embeddings.
Optimizing a single scalar temperature improves semantic calibration, discrimination, and entropy in language model question-answering over heuristic baselines and token-level recalibration methods.
citing papers explorer
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Slithering Through Gaps: Capturing Discrete Isolated Modes via Logistic Bridging
HiSS sampling uses logistic bridging in a Metropolis-within-Gibbs framework to enable transitions between disconnected modes while preserving the target discrete distribution.
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Mixture Proportion Estimation and Weakly-supervised Kernel Test for Conditional Independence
New conditional independence assumptions enable mixture proportion estimation and kernel tests for conditional independence without relying on irreducibility.
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The Rashomon Effect for Visualizing High-Dimensional Data
Defining the Rashomon set for dimension reduction enables interpretable, robust visualizations by aligning embeddings with known structure and extracting consistent local relationships across multiple good embeddings.
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Improving Semantic Uncertainty Quantification in Language Model Question-Answering via Token-Level Temperature Scaling
Optimizing a single scalar temperature improves semantic calibration, discrimination, and entropy in language model question-answering over heuristic baselines and token-level recalibration methods.