公众演讲六种技巧-勤学培训网

Curriculum Learning (CL) is a popular frontier in recent years. The concept was first proposed by Professor Bengio, and is inspired by the characteristic of human learning: learning lessons in an order from easy to difficult (in machine learning, this corresponds to easy to learn samples and not easy to learn samples). The goal of CL is for the model to start with simple samples and gradually progress to complex samples and knowledge 123.

Let's dive into some of the key aspects of course learning:

Problem definition:
Course learning is a training strategy in which samples are gradually introduced in the training process. The training samples are gradually added to the training set of the model in order from easy to difficult.
At each training step, the training criteria sequence \(\mathcal{C} = \langle Q_1, \ldots, Q_t, \ldots, Q_T \rangle\) contains a series of criteria \(Q_t\), Each criterion corresponds to the weight of the target training distribution \(P(z)\). These guidelines can relate to data/tasks, model capacity, learning goals, etc.
Effectiveness analysis:
The effectiveness of CL is analyzed from two perspectives of model optimization and data distribution:
Model optimization perspective: CL is similar to a special continuation approach that optimizes problems that are relatively smooth and then gradually optimizes problems that are not smooth enough. This helps the model better find global minima.
Data distribution Angle: CL is weighted to assign less weight to noisy data at the beginning, and then gradually increase the weight. This helps reduce the influence from negative samples.
Method summary:
The core problem of CL is to get a ranking function that gives its learning priority to each piece of data/each task. This is usually done by the Difficulty Measurer.
The design of CL is usually based on the framework of "difficulty meter + training scheduler". The difficulty meter is used to measure the difficulty of the sample, and the training scheduler decides when to enter the more difficult sample into the training.
Future research directions:
Better theoretical analysis: Explore why typical CL is effective.
More CL algorithms and applications: Design more automated CL methods, including different optimization methods and objectives.
Explore the application of CL in more fields.
In conclusion, curriculum learning is an interesting and promising research direction that draws on the way humans learn and provides an effective strategy for model training.

Tina Gilliland Curriculum