Topological structure of complex predictions

• URL: http://arxiv.org/abs/2207.14358v1
• Date: Thu, 28 Jul 2022 19:28:05 GMT
• Title: Topological structure of complex predictions
• Authors: Meng Liu, Tamal K. Dey, David F. Gleich
• Abstract summary: Complex prediction models such as deep learning are the output from fitting machine learning, neural networks, or AI models to a set of training data. We use topological data analysis to transform these complex prediction models into pictures representing a topological view. The methods scale up to large datasets across different domains and enable us to detect labeling errors in training data, understand generalization in image classification, and inspect predictions of likely pathogenic mutations in the BRCA1 gene.
• Score: 15.207535648404765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Complex prediction models such as deep learning are the output from fitting machine learning, neural networks, or AI models to a set of training data. These are now standard tools in science. A key challenge with the current generation of models is that they are highly parameterized, which makes describing and interpreting the prediction strategies difficult. We use topological data analysis to transform these complex prediction models into pictures representing a topological view. The result is a map of the predictions that enables inspection. The methods scale up to large datasets across different domains and enable us to detect labeling errors in training data, understand generalization in image classification, and inspect predictions of likely pathogenic mutations in the BRCA1 gene.

Related papers

• Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning [4.910937238451485]
  Transformer models have consistently achieved remarkable results in various domains such as natural language processing and computer vision. Despite ongoing research efforts to better understand these models, the field still lacks a comprehensive understanding. Time series data, unlike image and text information, can be more challenging to interpret and analyze.
  arXiv  Detail & Related papers  (2024-10-17T17:32:35Z)
• Learning Augmentation Policies from A Model Zoo for Time Series Forecasting [58.66211334969299]
  We introduce AutoTSAug, a learnable data augmentation method based on reinforcement learning. By augmenting the marginal samples with a learnable policy, AutoTSAug substantially improves forecasting performance.
  arXiv  Detail & Related papers  (2024-09-10T07:34:19Z)
• Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease [13.630617713928197]
  Graph neural networks have emerged as promising alternatives to classical statistical and machine learning methods. This study evaluates various graph representation learning models for case-control classification. We compare topologies derived from sample similarity networks and molecular interaction networks, including protein-protein and metabolite-metabolite interactions.
  arXiv  Detail & Related papers  (2024-06-20T16:06:39Z)
• RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data [59.4141628321618]
  We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.
  arXiv  Detail & Related papers  (2022-08-11T13:55:04Z)
• Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation [29.623692599892365]
  Trajectory prediction is one of the essential tasks for autonomous vehicles. Recent progress in machine learning gave birth to a series of advanced trajectory prediction algorithms.
  arXiv  Detail & Related papers  (2022-08-06T20:19:52Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Learning Curves for SGD on Structured Features [23.40229188549055]
  We show that the geometry of the data in the induced feature space is crucial to accurately predict the test error throughout learning. We show that modeling the geometry of the data in the induced feature space is indeed crucial to accurately predict the test error throughout learning.
  arXiv  Detail & Related papers  (2021-06-04T20:48:20Z)
• Deducing neighborhoods of classes from a fitted model [68.8204255655161]
  In this article a new kind of interpretable machine learning method is presented. It can help to understand the partitioning of the feature space into predicted classes in a classification model using quantile shifts. Basically, real data points (or specific points of interest) are used and the changes of the prediction after slightly raising or decreasing specific features are observed.
  arXiv  Detail & Related papers  (2020-09-11T16:35:53Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.