Aug 26, 2024 · Distributionally robust optimization approaches minimize worst-case loss. Differences in action spaces and their distributions can cause loss magnitudes to be ...

5 days ago · - Exploring crowd-sourcing and active learning for efficient multimodal data annotation. ... - Scaling deduplication processes to very large datasets ...

2 days ago · The field of process automation possesses a substantial corpus of textual documentation that can be leveraged with Large Language Models (LLMs) and Natural ...

6 days ago · Use Case: Crowdsourcing is crucial in creating labelled datasets that are vital in training AI and machine learning models including image recognition, natural ...

Aug 27, 2024 · Cleanlab is well-designed, scalable and theoretically grounded: it accurately finds data errors, even on well-known and established datasets.

22 hours ago · Project sidewalk: A web-based crowdsourcing tool for collecting sidewalk accessibility data at scale (CHI 2019) pdf ... active learning: An experimental study ( ...

Aug 29, 2024 · The approach may face challenges when scaling to huge datasets. Handling conditional dependencies between attributes may require extensive computational ...

5 days ago · Distributed learning has become the standard approach for training large-scale machine learning models across private data silos. While distributed learning ...

Sep 4, 2024 · The paper presents a novel big data approach and analytics framework for the management and analysis of machine-generated data in the Cloud. It brings together ...

Aug 24, 2024 · Challenging course, best advice is to not overthink the assignments. Overall I learned a lot and it changed the way I think about ML.