Agglomerative Contextual Decomposition: An Overview
Agglomerative Contextual Decomposition, also known as ACD, is a technique used to interpret the output of a neural network prediction. It produces hierarchical interpretations for a single prediction, which provides insight into how the neural network arrived at its decision.
Neural networks are trained using large datasets and complex mathematical algorithms. They are capable of making accurate predictions, but their decision-making process
CAM: An Overview
In recent years, computer vision has grown exponentially, with machines becoming advanced enough to identify and classify objects through deep learning and neural networks. Consequently, the interpretation of neural network decision making has become a complex task. One such technique to interpret these decisions is CAM, which stands for Class Activation Maps.
What is CAM?
CAM or Class Activation Maps is a technique that uses Convolutional Neural Networks (CNNs) to visualize
Understanding CDEP: A Guide to Contextual Decomposition Explanation Penalization
If you're interested in the field of artificial intelligence and machine learning, you might be familiar with neural networks. Neural networks are computer systems modeled after the structure of the human brain, and they're used for a wide range of applications, from predicting stock prices to detecting cancer. However, as with any machine learning system, neural networks are only as good as the quality of their tr
Disentangled Attribution Curves (DAC) are a method to interpret tree ensemble models through feature importance curves. These curves show the importance of a variable or group of variables based on their value changes.
What are Tree Ensemble Methods?
Tree Ensemble Methods are models that use a collection of decision trees to achieve classification or regression tasks. Decision trees are flowcharts consisting of nodes and edges, and each node represents a decision. They learn to map input feat
Hierarchical Network Dissection (HND) is a technique used to interpret face-centric inference models. This method pairs units of the model with concepts in a "Face Dictionary" to understand the internal representation of the model. HND is inspired by Network Dissection, which is used to interpret object-centric and scene-centric models.
Understanding HND
Convolution is a widely used technique in deep learning models. A convolutional layer in a deep learning model contains multiple filters, an
What is LIME?
LIME stands for Local Interpretable Model-Agnostic Explanations, and it is an algorithm that allows users to understand and explain the predictions of any classifier or regressor. LIME approximates a prediction for a single data sample by tweaking the feature values and observing the resulting impact on the output. This makes LIME an "explainer" that can provide a local interpretation of a model's predictions.
How Does LIME Work?
The first step in using LIME is to select a data
Network Dissection is a fascinating technology that helps us better understand neural networks. Specifically, it focuses on [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks), or convolutional neural networks, which are used in machine learning to classify images or objects in photos. Through Network Dissection, we can evaluate how individual hidden units in a CNN align with specific objects, parts, and other visual elements.
How Network Dissection Works
The pro
Neural Additive Models (NAMs) are a type of machine learning model that are designed to be both accurate and easy to interpret. They are a part of a larger model family called Generalized Additive Models (GAMs), which make restrictions on the structure of neural networks so that the resulting models are more easily understood by humans.
How NAMs Work
The idea behind NAMs is relatively simple. They learn a linear combination of networks, meaning they combine the results of multiple neural netw
What is SHAP and How Does It Work?
SHAP, or SHapley Additive exPlanations, is a game theoretical approach that aims to explain the output of any machine learning model. By linking optimal credit allocation with local explanations, SHAP uses classic Shapley values from game theory and their related extensions to provide explanations for machine learning models.
The basic idea behind SHAP is that when a machine learning model gives a prediction, it has assigned some amount of "credit" to each fe
Symbolic Deep Learning: An Overview
Symbolic deep learning is a technique that involves converting a neural network into an analytic equation. This general approach allows for a better understanding of the neural network's learned representations and has applications in discovering novel physical principles.
The Technique
The technique used in symbolic deep learning involves three steps:
1. Encourage sparse latent representations
Sparse latent representations refer to the idea that the ne
Syntax Heat Parse Tree and Its Significance
Syntax Heat Parse Tree is a type of heatmap that is used in analyzing text data to identify common patterns in sentence structure. It uses the parse tree structure, which represents the grammatical structure of a sentence, and creates a visual representation of the most frequent patterns. This allows analysts to quickly identify and explore the most common syntactical features.
The Basics of Syntax Heat Parse Tree
Every sentence can be represented
TE2Rules: A Method to Make AI Models More Transparent
What is TE2Rules?
TE2Rules is a method used to convert a Tree Ensemble model, which is a type of artificial intelligence (AI) model used in machine learning, into a Rule list. Essentially, this process breaks down the complex decision-making processes employed by AI models into simple rules that can be easily understood and interpreted by humans. This makes it possible for humans to understand how a decision was reached and to identify any