You are out of context!

• URL: http://arxiv.org/abs/2411.02464v1
• Date: Mon, 04 Nov 2024 10:17:43 GMT
• Title: You are out of context!
• Authors: Giancarlo Cobino, Simone Farci,
• Abstract summary: New data can act as forces stretching, compressing, or twisting the geometric relationships learned by a model. We propose a novel drift detection methodology for machine learning (ML) models based on the concept of ''deformation'' in the vector space representation of data.
• Score: 0.0
• License:
• Abstract: This research proposes a novel drift detection methodology for machine learning (ML) models based on the concept of ''deformation'' in the vector space representation of data. Recognizing that new data can act as forces stretching, compressing, or twisting the geometric relationships learned by a model, we explore various mathematical frameworks to quantify this deformation. We investigate measures such as eigenvalue analysis of covariance matrices to capture global shape changes, local density estimation using kernel density estimation (KDE), and Kullback-Leibler divergence to identify subtle shifts in data concentration. Additionally, we draw inspiration from continuum mechanics by proposing a ''strain tensor'' analogy to capture multi-faceted deformations across different data types. This requires careful estimation of the displacement field, and we delve into strategies ranging from density-based approaches to manifold learning and neural network methods. By continuously monitoring these deformation metrics and correlating them with model performance, we aim to provide a sensitive, interpretable, and adaptable drift detection system capable of distinguishing benign data evolution from true drift, enabling timely interventions and ensuring the reliability of machine learning systems in dynamic environments. Addressing the computational challenges of this methodology, we discuss mitigation strategies like dimensionality reduction, approximate algorithms, and parallelization for real-time and large-scale applications. The method's effectiveness is demonstrated through experiments on real-world text data, focusing on detecting context shifts in Generative AI. Our results, supported by publicly available code, highlight the benefits of this deformation-based approach in capturing subtle drifts that traditional statistical methods often miss. Furthermore, we present a detailed application example within the healthcare domain, showcasing the methodology's potential in diverse fields. Future work will focus on further improving computational efficiency and exploring additional applications across different ML domains.

Related papers

• Simultaneous Dimensionality Reduction for Extracting Useful Representations of Large Empirical Multimodal Datasets [0.0]
  We focus on the sciences of dimensionality reduction as a means to obtain low-dimensional descriptions from high-dimensional data. We address the challenges posed by real-world data that defy conventional assumptions, such as complex interactions within systems or high-dimensional dynamical systems.
  arXiv  Detail & Related papers  (2024-10-23T21:27:40Z)
• Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning [50.84938730450622]
  We propose a trajectory-based method TV score, which uses trajectory volatility for OOD detection in mathematical reasoning. Our method outperforms all traditional algorithms on GLMs under mathematical reasoning scenarios. Our method can be extended to more applications with high-density features in output spaces, such as multiple-choice questions.
  arXiv  Detail & Related papers  (2024-05-22T22:22:25Z)
• A stable deep adversarial learning approach for geological facies generation [32.97208255533144]
  Deep generative learning is a promising approach to overcome the limitations of traditional geostatistical simulation models. This research aims to investigate the application of generative adversarial networks and deep variational inference for conditionally meandering channels in underground volumes.
  arXiv  Detail & Related papers  (2023-05-12T14:21:14Z)
• VTAE: Variational Transformer Autoencoder with Manifolds Learning [144.0546653941249]
  Deep generative models have demonstrated successful applications in learning non-linear data distributions through a number of latent variables. The nonlinearity of the generator implies that the latent space shows an unsatisfactory projection of the data space, which results in poor representation learning. We show that geodesics and accurate computation can substantially improve the performance of deep generative models.
  arXiv  Detail & Related papers  (2023-04-03T13:13:19Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Consistency and Diversity induced Human Motion Segmentation [231.36289425663702]
  We propose a novel Consistency and Diversity induced human Motion (CDMS) algorithm. Our model factorizes the source and target data into distinct multi-layer feature spaces. A multi-mutual learning strategy is carried out to reduce the domain gap between the source and target data.
  arXiv  Detail & Related papers  (2022-02-10T06:23:56Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Learning Dynamics from Noisy Measurements using Deep Learning with a Runge-Kutta Constraint [9.36739413306697]
  We discuss a methodology to learn differential equation(s) using noisy and sparsely sampled measurements. In our methodology, the main innovation can be seen in of integration of deep neural networks with a classical numerical integration method.
  arXiv  Detail & Related papers  (2021-09-23T15:43:45Z)
• Learning Neural Models for Natural Language Processing in the Face of Distributional Shift [10.990447273771592]
  The dominating NLP paradigm of training a strong neural predictor to perform one task on a specific dataset has led to state-of-the-art performance in a variety of applications. It builds upon the assumption that the data distribution is stationary, ie. that the data is sampled from a fixed distribution both at training and test time. This way of training is inconsistent with how we as humans are able to learn from and operate within a constantly changing stream of information. It is ill-adapted to real-world use cases where the data distribution is expected to shift over the course of a model's lifetime
  arXiv  Detail & Related papers  (2021-09-03T14:29:20Z)
• Information Theory Measures via Multidimensional Gaussianization [7.788961560607993]
  Information theory is an outstanding framework to measure uncertainty, dependence and relevance in data and systems. It has several desirable properties for real world applications. However, obtaining information from multidimensional data is a challenging problem due to the curse of dimensionality.
  arXiv  Detail & Related papers  (2020-10-08T07:22:16Z)

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.