1 code implementation • 22 Nov 2023 • Vinay Shukla, Zhe Zeng, Kareem Ahmed, Guy Van Den Broeck
In many cases, these weak labels dictate the frequency of each respective class over a set of instances.
1 code implementation • 3 Oct 2023 • Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van Den Broeck, Mathias Niepert, Christopher Morris
Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input.
1 code implementation • NeurIPS 2023 • Zhe Zeng, Guy Van Den Broeck
We tackle this challenge by revealing a previously unseen connection between inference on BNNs and volume computation problems.
1 code implementation • 4 Oct 2022 • Kareem Ahmed, Zhe Zeng, Mathias Niepert, Guy Van Den Broeck
$k$-subset sampling is ubiquitous in machine learning, enabling regularization and interpretability through sparsity.
no code implementations • CVPR 2023 • Yizhuo Chen, Kaizhao Liang, Zhe Zeng, Yifei Yang, Shuochao Yao, Huajie Shao
Moreover, our method achieves good performance for discriminative deep DGMs compression.
1 code implementation • 21 Feb 2021 • Wenzhe Li, Zhe Zeng, Antonio Vergari, Guy Van Den Broeck
Computing the expectation of kernel functions is a ubiquitous task in machine learning, with applications from classical support vector machines to exploiting kernel embeddings of distributions in probabilistic modeling, statistical inference, causal discovery, and deep learning.
no code implementations • NeurIPS 2020 • Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van Den Broeck
Weighted model integration (WMI) is a framework to perform advanced probabilistic inference on hybrid domains, i. e., on distributions over mixed continuous-discrete random variables and in presence of complex logical and arithmetic constraints.
1 code implementation • ICML 2020 • Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van Den Broeck
Weighted model integration (WMI) is a very appealing framework for probabilistic inference: it allows to express the complex dependencies of real-world problems where variables are both continuous and discrete, via the language of Satisfiability Modulo Theories (SMT), as well as to compute probabilistic queries with complex logical and arithmetic constraints.
no code implementations • 20 Sep 2019 • Zhe Zeng, Fanqi Yan, Paolo Morettin, Antonio Vergari, Guy Van Den Broeck
Weighted model integration (WMI) is a very appealing framework for probabilistic inference: it allows to express the complex dependencies of real-world hybrid scenarios where variables are heterogeneous in nature (both continuous and discrete) via the language of Satisfiability Modulo Theories (SMT); as well as computing probabilistic queries with arbitrarily complex logical constraints.
no code implementations • 13 Mar 2019 • Zhe Zeng, Guy Van Den Broeck
Weighted model integration (WMI) extends Weighted model counting (WMC) to the integration of functions over mixed discrete-continuous domains.
no code implementations • ICML 2018 • Dilin Wang, Zhe Zeng, Qiang Liu
We propose a novel distributed inference algorithm for continuous graphical models, by extending Stein variational gradient descent (SVGD) to leverage the Markov dependency structure of the distribution of interest.