Related papers: Finding archetypal patterns for binary questionnaires

Related papers

Finding Multiple Interpretations in Datasets [0.0]
We show that the proposed method finds multiple models with highly different gene expressions than those found by the control methodology without performance penalties.<n>We argue that the proposed methodology is important whenever one aims to analyze any global characteristic of a model to extract insight into the underlying phenomenon being studied.
arXiv Detail & Related papers (2026-06-10T16:17:48Z)
Archetypal cases for questionnaires with nominal multiple choice questions [5.442955439283729]
Archetypal analysis serves as an exploratory tool that interprets a collection of observations as convex combinations of pure (extreme) patterns.<n>For the first time, we propose applying archetypoid analysis to nominal observations.
arXiv Detail & Related papers (2026-01-08T21:32:23Z)
Deciphering the Interplay of Parametric and Non-parametric Memory in Retrieval-augmented Language Models [5.274653527674298]
Retrieval-Augmented Generation (RAG) models act like retrieving information before generating responses. We use causal mediation analysis and controlled experiments to examine how internal representations influence information processing.
arXiv Detail & Related papers (2024-10-07T16:14:47Z)
Two Is Better Than One: Aligned Representation Pairs for Anomaly Detection [56.57122939745213]
Anomaly detection focuses on identifying samples that deviate from the norm.<n>Recent self-supervised methods have successfully learned such representations by employing prior knowledge about anomalies to create synthetic outliers during training.<n>We address this limitation with our new approach Con$$, which leverages prior knowledge about symmetries in normal samples to observe the data in different contexts.
arXiv Detail & Related papers (2024-05-29T07:59:06Z)
Biarchetype analysis: simultaneous learning of observations and features based on extremes [0.0]
We introduce a novel technique, termed biarchetype analysis, which extends archetype analysis to simultaneously identify archetypes of both observations and features. This innovative unsupervised machine learning tool aims to represent observations and features through instances of pure types, or biarchetypes. The application of biarchetype analysis across various machine learning challenges underscores its value.
arXiv Detail & Related papers (2023-11-18T19:44:54Z)
Sample based Explanations via Generalized Representers [42.53488505266559]
We show that generalized representers are the only class of sample based explanations satisfying a natural set of axiomatic properties. We discuss approaches to extract global importances given a kernel, and also natural choices of kernels given modern non-linear models.
arXiv Detail & Related papers (2023-10-27T22:54:47Z)
Rationalizing Predictions by Adversarial Information Calibration [65.19407304154177]
We train two models jointly: one is a typical neural model that solves the task at hand in an accurate but black-box manner, and the other is a selector-predictor model that additionally produces a rationale for its prediction. We use an adversarial technique to calibrate the information extracted by the two models such that the difference between them is an indicator of the missed or over-selected features.
arXiv Detail & Related papers (2023-01-15T03:13:09Z)
An Additive Instance-Wise Approach to Multi-class Model Interpretation [53.87578024052922]
Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. Existing methods mainly focus on selecting explanatory input features, which follow either locally additive or instance-wise approaches. This work exploits the strengths of both methods and proposes a global framework for learning local explanations simultaneously for multiple target classes.
arXiv Detail & Related papers (2022-07-07T06:50:27Z)
Self-Attention Neural Bag-of-Features [103.70855797025689]
We build on the recently introduced 2D-Attention and reformulate the attention learning methodology. We propose a joint feature-temporal attention mechanism that learns a joint 2D attention mask highlighting relevant information.
arXiv Detail & Related papers (2022-01-26T17:54:14Z)
Intersection Regularization for Extracting Semantic Attributes [72.53481390411173]
We consider the problem of supervised classification, such that the features that the network extracts match an unseen set of semantic attributes. For example, when learning to classify images of birds into species, we would like to observe the emergence of features that zoologists use to classify birds. We propose training a neural network with discrete top-level activations, which is followed by a multi-layered perceptron (MLP) and a parallel decision tree.
arXiv Detail & Related papers (2021-03-22T14:32:44Z)
Latent Feature Representation via Unsupervised Learning for Pattern Discovery in Massive Electron Microscopy Image Volumes [4.278591555984395]
In particular, we give an unsupervised deep learning approach to learning a latent representation that captures semantic similarity in the data set. We demonstrate the utility of our method applied to nano-scale electron microscopy data, where even relatively small portions of animal brains can require terabytes of image data.
arXiv Detail & Related papers (2020-12-22T17:14:19Z)
Toward Scalable and Unified Example-based Explanation and Outlier Detection [128.23117182137418]
We argue for a broader adoption of prototype-based student networks capable of providing an example-based explanation for their prediction. We show that our prototype-based networks beyond similarity kernels deliver meaningful explanations and promising outlier detection results without compromising classification accuracy.
arXiv Detail & Related papers (2020-11-11T05:58:17Z)
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? [97.77183117452235]
We carry out human subject tests to isolate the effect of algorithmic explanations on model interpretability. Clear evidence of method effectiveness is found in very few cases. Our results provide the first reliable and comprehensive estimates of how explanations influence simulatability.
arXiv Detail & Related papers (2020-05-04T20:35:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.