3 days ago · Combining human and machine intelligence in large-scale crowdsourcing ... Leveraging crowdsourcing data for deep active learning an application: Learning intents ...

2 days ago · ... Scaling multiple-source entity resolution using statistically efficient transfer learning. ... A Pre-trained deep active learning model for data deduplication.

13 hours ago · Scaling up language models to billions of parameters has opened up possibilities for in-context learning, allowing instruction tuning and few-shot learning ...

5 days ago · This talk highlights such impactful shifts in representation learning for IR and related areas, the new challenges coming along and the remedies, including our ...

3 days ago · I am interested in all aspects of building large-scale, safe and robust AI systems. I am Research Scientist in the Robotics team at Google DeepMind, NYC. Bio

4 days ago · MLOps covers the end-to-end machine learning process, including data preparation, model training, version control, deployment, and monitoring. It emphasizes ...

7 days ago · This course will provide new learners, advanced practitioners, and other data stakeholders with information, strategies, and resources to facilitate data ...

3 days ago · While medical big data is generally characterized by large scale, diverse types, and rich potential information, for this reason some researchers have ...

5 days ago · Crowdsourcing Utilizing a large group of human volunteers to perform a task online. Workload saving Workload saving refers to the reduction in the amount of ...

9 hours ago · Gokhale et al. proposed. Corleone [31], which uses a combination of blocking rules and active learning to improve accuracy while minimizing crowdsourcing costs.