Mapping the Multiverse of Latent Representations

• URL: http://arxiv.org/abs/2402.01514v2
• Date: Sat, 1 Jun 2024 09:48:48 GMT
• Title: Mapping the Multiverse of Latent Representations
• Authors: Jeremy Wayland, Corinna Coupette, Bastian Rieck,
• Abstract summary: PRESTO is a principled framework for mapping the multiverse of machine-learning models that rely on latent representations. Our framework uses persistent homology to characterize the latent spaces arising from different combinations of diverse machine-learning methods.
• Score: 17.2089620240192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Echoing recent calls to counter reliability and robustness concerns in machine learning via multiverse analysis, we present PRESTO, a principled framework for mapping the multiverse of machine-learning models that rely on latent representations. Although such models enjoy widespread adoption, the variability in their embeddings remains poorly understood, resulting in unnecessary complexity and untrustworthy representations. Our framework uses persistent homology to characterize the latent spaces arising from different combinations of diverse machine-learning methods, (hyper)parameter configurations, and datasets, allowing us to measure their pairwise (dis)similarity and statistically reason about their distributions. As we demonstrate both theoretically and empirically, our pipeline preserves desirable properties of collections of latent representations, and it can be leveraged to perform sensitivity analysis, detect anomalous embeddings, or efficiently and effectively navigate hyperparameter search spaces.

Related papers

• Probabilistic Lexical Manifold Construction in Large Language Models via Hierarchical Vector Field Interpolation [0.0]
  The proposed methodology constructs a probabilistic function space where word representations adhere to topological consistency. Probability constraints enhance lexical coherence by refining contextual relationships, leading to improvements in semantic stability across multiple linguistic distributions. An assessment of computational efficiency reveals that while representations introduces minor processing overhead, the structured representation learning approach remains scalable for practical deployment.
  arXiv  Detail & Related papers  (2025-02-14T08:47:10Z)
• Identifiable Representation and Model Learning for Latent Dynamic Systems [0.0]
  We study the problem of identifiable representation and model learning for latent dynamic systems. We prove that, for linear and affine nonlinear latent dynamic systems with sparse input matrices, it is possible to identify the latent variables up to scaling.
  arXiv  Detail & Related papers  (2024-10-23T13:55:42Z)
• The Common Stability Mechanism behind most Self-Supervised Learning Approaches [64.40701218561921]
  We provide a framework to explain the stability mechanism of different self-supervised learning techniques. We discuss the working mechanism of contrastive techniques like SimCLR, non-contrastive techniques like BYOL, SWAV, SimSiam, Barlow Twins, and DINO. We formulate different hypotheses and test them using the Imagenet100 dataset.
  arXiv  Detail & Related papers  (2024-02-22T20:36:24Z)
• Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models [85.67870425656368]
  We introduce a unified causal model specifically designed for multimodal data. We show that multimodal contrastive representation learning excels at identifying latent coupled variables. Experiments demonstrate the robustness of our findings, even when the assumptions are violated.
  arXiv  Detail & Related papers  (2024-02-09T07:18:06Z)
• Leveraging Diffusion Disentangled Representations to Mitigate Shortcuts in Underspecified Visual Tasks [92.32670915472099]
  We propose an ensemble diversification framework exploiting the generation of synthetic counterfactuals using Diffusion Probabilistic Models (DPMs) We show that diffusion-guided diversification can lead models to avert attention from shortcut cues, achieving ensemble diversity performance comparable to previous methods requiring additional data collection.
  arXiv  Detail & Related papers  (2023-10-03T17:37:52Z)
• Learning minimal representations of stochastic processes with variational autoencoders [52.99137594502433]
  We introduce an unsupervised machine learning approach to determine the minimal set of parameters required to describe a process. Our approach enables for the autonomous discovery of unknown parameters describing processes.
  arXiv  Detail & Related papers  (2023-07-21T14:25:06Z)
• Adversarial Robustness of Supervised Sparse Coding [34.94566482399662]
  We consider a model that involves learning a representation while at the same time giving a precise generalization bound and a robustness certificate. We focus on the hypothesis class obtained by combining a sparsity-promoting encoder coupled with a linear encoder. We provide a robustness certificate for end-to-end classification.
  arXiv  Detail & Related papers  (2020-10-22T22:05:21Z)
• Generalization Properties of Optimal Transport GANs with Latent Distribution Learning [52.25145141639159]
  We study how the interplay between the latent distribution and the complexity of the pushforward map affects performance. Motivated by our analysis, we advocate learning the latent distribution as well as the pushforward map within the GAN paradigm.
  arXiv  Detail & Related papers  (2020-07-29T07:31:33Z)
• Learning Disentangled Representations with Latent Variation Predictability [102.4163768995288]
  This paper defines the variation predictability of latent disentangled representations. Within an adversarial generation process, we encourage variation predictability by maximizing the mutual information between latent variations and corresponding image pairs. We develop an evaluation metric that does not rely on the ground-truth generative factors to measure the disentanglement of latent representations.
  arXiv  Detail & Related papers  (2020-07-25T08:54:26Z)
• Modal Uncertainty Estimation via Discrete Latent Representation [4.246061945756033]
  We introduce a deep learning framework that learns the one-to-many mappings between the inputs and outputs, together with faithful uncertainty measures. Our framework demonstrates significantly more accurate uncertainty estimation than the current state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-25T05:29:34Z)

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.