From the 1 of 4 linked papers with an AI index.
4 papers
Efficient Conformal Prediction for Regression Models under Label Noise
Yahav Cohen, Jacob Goldberger, Tom Tirer
The paper introduces a method to apply conformal prediction to regression models when the calibration data have noisy labels, by estimating a noise‑free threshold and providing a p…
MoGU: Mixture-of-Gaussians with Uncertainty-based Gating for Time Series Forecasting
Gilad Aviv, Jacob Goldberger, Yoli Shavit
We introduce Mixture-of-Gaussians with Uncertainty-based Gating (MoGU), a novel Mixture-of-Experts (MoE) framework designed for regression tasks. MoGU replaces standard learned gat…
Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels
Yuval Grinberg, Nimrod Harel, Jacob Goldberger +1
Falsely annotated samples, also known as noisy labels, can significantly harm the performance of deep learning models. Two main approaches for learning with noisy labels are global…
The Power of Summary-Source Alignments
Ori Ernst, Ori Shapira, Aviv Slobodkin +5
Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection, followed by text generation. In this context, alignment…