Action Map Policy (AMP) reframes robot manipulation as pixel-level classification by projecting 3D keypoint trajectories onto 2D image planes, enabling cross-entropy loss to model multi-modal action distributions with sub-millimeter precision and single-forward-pass inference.
The article explains how information entropy, cross‑entropy, and KL‑divergence shape reinforcement learning for large language models, describes the phenomenon of entropy collapse, compares token‑level and policy‑level entropy, and reviews recent methods like Clip‑Cov and KL‑Cov that mitigate this issue.
The article explains linear regression and logistic regression fundamentals, covering loss functions such as mean‑squared error and cross‑entropy, analytic solutions, feature expansion for non‑linear separability, and provides Python code examples to illustrate the concepts.
This article explains how loss functions evaluate model performance, contrasts regression’s mean squared error with classification’s cross‑entropy, describes one‑hot encoding and softmax outputs, and shows why higher predicted probabilities for the correct class yield lower loss, highlighting applications in image, language, and speech tasks.
This tutorial walks through the complete FCN pipeline for semantic segmentation, covering VOC dataset loading, data augmentation, collate functions, model construction, training loops, loss computation with cross‑entropy (including ignore‑index handling), and inference, while providing full PyTorch code snippets for each step.
This article walks through implementing a basic multi‑layer neural network using only NumPy, covering terminology, network architecture, forward and backward propagation, activation functions, loss calculation, parameter updates with SGD, and compares the custom model with a Keras implementation.
The article explains Shannon information content, entropy, cross‑entropy, KL‑divergence, conditional entropy and mutual information, illustrating each concept with coin‑flip and dice examples, visual formulas, and discusses their roles as loss functions and evaluation metrics in machine‑learning models.
This article explains entropy, joint entropy, conditional entropy, and related measures such as KL divergence and cross‑entropy, using intuitive coin‑flip examples and mathematical formulas to show how they quantify uncertainty and information in probability distributions.
This article explains the concepts of entropy, joint entropy, conditional entropy, relative entropy (KL divergence) and cross‑entropy, illustrating their definitions, mathematical formulas, intuitive interpretations, and relationships through simple probability examples and visual diagrams.
