Encoder-Classifier Model Notation

Parameterized Prediction Function using a BERT model

Classification via an Encoder Function ($Classify_{\omega}(Encode_{\hat{\theta}}(\cdot))$)

Consider two functions, $M_{\alpha, \beta}(x)$ and $M_{\gamma, \delta}(x)$. Both functions are designed to perform the same underlying computational task. However, when given the exact same input value for $x$, they produce different results. Based on the provided notation, what is the most likely reason for this difference in output?

A machine learning model, designed to perform a specific task, is represented by the function $M$. Initially, its performance is poor. After a training process that adjusts the model's internal settings, its performance on the same task improves significantly. Let the set of internal settings before training be denoted by $\theta_{initial}$ and after training by $\theta_{final}$. Which notation correctly represents the model before and after training, respectively, when applied to an input $x$?

Explaining Model Behavior Change

Adaptation of Pre-trained Models via Full Fine-Tuning