Text embeddings recover 57-63% of the reliable variance in exam-item difficulty, and apparent differences in predictability across IRT parameters are mostly artifacts of calibration noise rather than text signal.
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=
4 Pith papers cite this work. Polarity classification is still indexing.
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MOMENT is a moment-based method for selecting and estimating parameters in multiresponse linear mixed-effects models, with claimed finite-sample consistency under sub-Weibull errors.
DECO is a sparse MoE architecture with ReLU-based routing, learnable expert scaling, and NormSiLU activation that matches dense Transformer performance at 20% expert activation and delivers 2.93x speedup on Jetson AGX Orin.
MOSAIC recovers identifiable latent variables and their sparse associated observations in scientific time series by combining temporal causal representation learning with support recovery through a sparse additive decoder.
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Moment-Based Selection of Multiresponse Linear Mixed-Effects Models
MOMENT is a moment-based method for selecting and estimating parameters in multiresponse linear mixed-effects models, with claimed finite-sample consistency under sub-Weibull errors.