2024年5月27日 · We learn the joint distribution of node and cluster labels con-ditioned on their representations, and train GNNs with the obtained joint loss. In this way, the data-label reference signals extracted from the local cluster explicitly strengthen the discrimination ability on …
[2405.15564] Rethinking Independent Cross-Entropy Loss For Graph …
2024年5月24日 · It is found that the independent distribution modeling of node labels restricts GNNs' capability to generalize over the entire graph and defend adversarial attacks. In this work, we propose a new framework, termed joint-cluster supervised learning, to model the joint distribution of each node with its corresponding cluster.
What Is Cross-Entropy Loss Function? - GeeksforGeeks
2024年1月3日 · Key features of Cross Entropy loss. Probabilistic Interpretation: Cross-entropy loss encourages the model to output predicted probabilities that are close to the true class probabilities. Gradient Descent Optimization: The mathematical properties of Cross-Entropy Loss make it well-suited for optimization algorithms like gradient descent. The ...
Cross-Entropy Loss Function in Machine Learning: Enhancing …
2024年8月10日 · Cross-entropy, also known as logarithmic loss or log loss, is a popular loss function used in machine learning to measure the performance of a classification model. It measures the average number of bits required to identify an event from one probability distribution, p, using the optimal code for another probability distribution, q.
We present a theoretical analysis of a broad family of loss functions, comp-sum losses, that includes cross-entropy (or logistic loss), gen-eralized cross-entropy, the mean absolute error and other cross-entropy-like loss functions. We give the first H …
Loss Function: How should y be \similar to" ^y ? MMSE Solution: ^y ! E [~yj~x] Gradient Descent: How do we improve W and b? Given some initial neural net parameter (called ukj in this gure), we want to nd a better value of the same parameter. We do that using gradient descent: is a learning rate (some small constant, e.g., so).
Loss Functions — ML Glossary documentation - Read the Docs
Cross-Entropy ¶ Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label.
Understanding binary cross-entropy / log loss: a visual explanation
2018年11月21日 · For a binary classification like our example, the typical loss function is the binary cross-entropy / log loss. If you look this loss function up, this is what you’ll find: where y is the label (1 for green points and 0 for red points) and p (y) is the predicted probability of the point being green for all N points.
Cross-entropy loss functions | Proceedings of the 40th …
2023年7月23日 · We present a theoretical analysis of a broad family of loss functions, comp-sum losses, that includes cross-entropy (or logistic loss), generalized cross-entropy, the mean absolute error and other cross-entropy-like loss functions.
Rethinking Independent Cross-Entropy Loss For Graph …
Graph neural networks (GNNs) have exhibited prominent performance in learning graph-structured data. Considering node classification task, based on the i.i.d assumption among node labels, the traditional supervised learning simply sums up cross-entropy losses of the independent training nodes and applies the average loss to optimize GNNs’ weights.