18457169. METHODS AND APPARATUS FOR ASSISTED DATA REVIEW FOR ACTIVE LEARNING CYCLES simplified abstract (Intel Corporation)
Contents
METHODS AND APPARATUS FOR ASSISTED DATA REVIEW FOR ACTIVE LEARNING CYCLES
Organization Name
Inventor(s)
Vinnam Kim of Pyeongtaek-si (KR)
METHODS AND APPARATUS FOR ASSISTED DATA REVIEW FOR ACTIVE LEARNING CYCLES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18457169 titled 'METHODS AND APPARATUS FOR ASSISTED DATA REVIEW FOR ACTIVE LEARNING CYCLES
Simplified Explanation
The patent application describes a system and method for assisted data review in active learning cycles.
- The system uses machine learning to train a model using training data.
- The system determines the training loss associated with each data point in the training data.
- The training data is ranked based on the aggregate statistics of the training losses.
- The system selects the data point with the highest rank for annotation.
- The existing label of the selected data point is modified based on the annotation.
Potential applications of this technology:
- Improving the accuracy and efficiency of machine learning models.
- Assisting in data review and annotation tasks.
- Enhancing the performance of various applications that rely on machine learning.
Problems solved by this technology:
- Reducing the manual effort required for data review and annotation.
- Improving the selection of data points for annotation, leading to better model training.
- Streamlining the active learning process by prioritizing data points based on their training losses.
Benefits of this technology:
- Increased accuracy and performance of machine learning models.
- Time and cost savings in data review and annotation tasks.
- Enhanced decision-making capabilities in applications that utilize machine learning.
Original Abstract Submitted
Systems, apparatus, articles of manufacture, and methods are disclosed for assisted data review for active learning cycles. An example apparatus includes programmable circuitry to at least one of instantiate or execute the machine readable instructions to: determine a first training loss associated with a first data point of training data for training a machine learning model; determine a second training loss associated with a second data point of the training data; rank the training data based on aggregate statistics of the first and second training losses; select, based on the rank, the first data point for annotation; and modify an existing label of the first data point based on the annotation.
