GNNs are neural networks that can be used to analyse graphs directly. We're going to discuss utilising GNNs for link prediction even though they can also be used for node-level tasks (like giving each ...

Slide 16: Loss Function Selection. Selecting the appropriate loss function is crucial for training neural networks effectively. The choice of loss function depends on various factors, such as the ...

Loss functions are crucial components of artificial neural networks, as they measure how well the network performs on a given task and provide feedback for optimization. However, implementing and ...

In evaluating the robustness of a neural network, it is a common practice to measure the zero-one loss of the neural network with respect to adversarially perturbed examples. However, because the zero ...

Neural networks minimize the loss through a process called gradient descent. Gradient descent is an iterative algorithm that updates the weights of the neural network in the direction that reduces the ...

Gradient descent looks at the network as a calculus function and adjusts the values to minimize the loss function. Next, we will look at a variety of neural network styles that learn from and also ...

In this paper, we study the generalization capabilities of geometric graph neural networks (GNNs). We consider GNNs over a geometric graph constructed from a finite set of randomly sampled points over ...

The function takes the following parameters: - X: The input data.- W1, W2: The weight matrices for the two layers of the neural network. - batch_size: The size of the mini-batch for training. - alpha: ...

Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...