Network architecture of DeepGAM.
<p>( <b>a</b>) Each feature passes through a Straight-Through Estimator (STE), which learns to select relevant features during training. The selected feature <i>z</i><sub><i>j</i></sub> is then fed into a 3-layer neural network <i>f</i&g...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| الملخص: | <p>( <b>a</b>) Each feature passes through a Straight-Through Estimator (STE), which learns to select relevant features during training. The selected feature <i>z</i><sub><i>j</i></sub> is then fed into a 3-layer neural network <i>f</i>, and and its output is combined with a learnable bias parameter to represent the depression status. ( <b>b</b>) The STE selects a feature by multiplying it with a binary value in the forward pass, and enables parameter <i>ϕ</i> to be updated via gradient descent by omitting the non-differentiable round function in the backward pass. ( <b>c</b>) The 3-layer neural network consists of Fully Connected (FC) layers with ReLU and tanh activations. Each feature has distinct learnable parameters <i>ϕ</i> and .</p> |
|---|