CREPO: An Open Repository to Benchmark Credal Network Algorithms

• URL: http://arxiv.org/abs/2105.04158v1
• Date: Mon, 10 May 2021 07:31:59 GMT
• Title: CREPO: An Open Repository to Benchmark Credal Network Algorithms
• Authors: Rafael Caba\~nas and Alessandro Antonucci
• Abstract summary: Credal networks are imprecise probabilistic graphical models based on, so-called credal, sets of probability mass functions. A Java library called CREMA has been recently released to model, process and query credal networks. We present CREPO, an open repository of synthetic credal networks, provided together with the exact results of inference tasks on these models.
• Score: 78.79752265884109
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Credal networks are a popular class of imprecise probabilistic graphical models obtained as a Bayesian network generalization based on, so-called credal, sets of probability mass functions. A Java library called CREMA has been recently released to model, process and query credal networks. Despite the NP-hardness of the (exact) task, a number of algorithms is available to approximate credal network inferences. In this paper we present CREPO, an open repository of synthetic credal networks, provided together with the exact results of inference tasks on these models. A Python tool is also delivered to load these data and interact with CREMA, thus making extremely easy to evaluate and compare existing and novel inference algorithms. To demonstrate such benchmarking scheme, we propose an approximate heuristic to be used inside variable elimination schemes to keep a bound on the maximum number of vertices generated during the combination step. A CREPO-based validation against approximate procedures based on linearization and exact techniques performed in CREMA is finally discussed.

Related papers

• SQLNet: Scale-Modulated Query and Localization Network for Few-Shot Class-Agnostic Counting [71.38754976584009]
  The class-agnostic counting (CAC) task has recently been proposed to solve the problem of counting all objects of an arbitrary class with several exemplars given in the input image. We propose a novel localization-based CAC approach, termed Scale-modulated Query and Localization Network (Net) It fully explores the scales of exemplars in both the query and localization stages and achieves effective counting by accurately locating each object and predicting its approximate size.
  arXiv  Detail & Related papers  (2023-11-16T16:50:56Z)
• Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression [1.4528189330418977]
  Uncertainty estimation in machine learning is paramount for enhancing the reliability and interpretability of predictive models. We present an adaptation of the Multiple-Input Multiple-Output (MIMO) framework for pixel-wise regression tasks.
  arXiv  Detail & Related papers  (2023-08-14T22:08:28Z)
• PRANC: Pseudo RAndom Networks for Compacting deep models [22.793523211040682]
  PRANC enables significant compaction of a deep model. In this study, we employ PRANC to condense image classification models and compress images by compacting their associated implicit neural networks.
  arXiv  Detail & Related papers  (2022-06-16T22:03:35Z)
• Bayesian Structure Learning with Generative Flow Networks [85.84396514570373]
  In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) from data. Recently, a class of probabilistic models, called Generative Flow Networks (GFlowNets), have been introduced as a general framework for generative modeling. We show that our approach, called DAG-GFlowNet, provides an accurate approximation of the posterior over DAGs.
  arXiv  Detail & Related papers  (2022-02-28T15:53:10Z)
• Attentive Gaussian processes for probabilistic time-series generation [4.94950858749529]
  We propose a computationally efficient attention-based network combined with the Gaussian process regression to generate real-valued sequence. We develop a block-wise training algorithm to allow mini-batch training of the network while the GP is trained using full-batch. The algorithm has been proved to converge and shows comparable, if not better, quality of the found solution.
  arXiv  Detail & Related papers  (2021-02-10T01:19:15Z)
• Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion [59.549664231655726]
  A case-based reasoning (CBR) system solves a new problem by retrieving cases' that are similar to the given problem. In this paper, we demonstrate that such a system is achievable for reasoning in knowledge-bases (KBs) Our approach predicts attributes for an entity by gathering reasoning paths from similar entities in the KB.
  arXiv  Detail & Related papers  (2020-10-07T17:48:12Z)
• Towards Sample Efficient Agents through Algorithmic Alignment [25.23741737716188]
  We propose and explore Deep Graph Value Network (DeepGV) as a promising method to work around sample complexity in deep reinforcement-learning agents. The main idea is that the agent should be guided by structured non-neural-network algorithms like dynamic programming.
  arXiv  Detail & Related papers  (2020-08-07T15:44:32Z)
• Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits [99.59941892183454]
  We propose Einsum Networks (EiNets), a novel implementation design for PCs. At their core, EiNets combine a large number of arithmetic operations in a single monolithic einsum-operation. We show that the implementation of Expectation-Maximization (EM) can be simplified for PCs, by leveraging automatic differentiation.
  arXiv  Detail & Related papers  (2020-04-13T23:09:15Z)
• Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio [101.84651388520584]
  This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs. Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-06T15:51:00Z)

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.