Encoder-Classifier Model Notation

Parameterized Prediction Function using a BERT model

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

Classification via an Encoder Function ($\mathrm{Classify}_{\omega}(\mathrm{Encode}_{\hat{\theta}}(\cdot))$)

Probabilistic Model for Text Classification using an Encoder-Classifier Architecture

A machine learning engineer is building a system to categorize news articles. They use a component, represented by the notation $Classify_ω(·)$, which takes a numerical summary of an article and outputs a category label. After an initial training phase, the engineer finds the component's performance is unsatisfactory. To improve the system, they decide to adjust the values represented by $ω$. What is the most direct and intended outcome of modifying $ω$?

In the standard mathematical representation of a text classification component, Classify_ω(·), the symbol that represents the set of the classifier's learnable parameters is ____.

Deconstructing Classifier Notation

Classification via an Encoder Function ($\mathrm{Classify}_{\omega}(\mathrm{Encode}_{\hat{\theta}}(\cdot))$)