PulseAugur
EN
LIVE 05:04:40

New algorithm efficiently learns general ReLUs using queries

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new algorithm for learning general Rectified Linear Units (ReLUs) under Gaussian distribution. This algorithm is computationally efficient and utilizes query access to unlabeled examples, significantly reducing the number of required label queries. The new method achieves an error bound of O(opt) + \u03f5, where opt is the best fit ReLU's squared loss, and its query complexity is near-optimal. AI

IMPACT Introduces a more efficient method for learning specific types of neural network components, potentially improving model training and analysis.

RANK_REASON This is a research paper detailing a new algorithm for a machine learning task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mingchen Ma ·

    Robust Regression of General ReLUs with Queries

    We study the task of agnostically learning general (as opposed to homogeneous) ReLUs under the Gaussian distribution with respect to the squared loss. In the passive learning setting, recent work gave a computationally efficient algorithm that uses $poly(d,1/ε)$ labeled examples …

RELATED TOPICS