Improving variational methods via pairwise linear response identities

2016-11-02 · Jack Raymond, Federico Ricci-Tersenghi

Inference methods are often formulated as variational approximations: these approximations allow easy evaluation of statistics by marginalization or linear response, but these estimates can be inconsistent. We show that by introducing constraints on covariance, one can ensure consistency of linear response with the variational parameters, and in so doing inference of marginal probability distributions is improved. For the Bethe approximation and its generalizations, improvements are achieved with simple choices of the constraints. The approximations are presented as variational frameworks; iterative procedures related to message passing are provided for finding the minima.

📄 PDF Abstract BibTeX arXiv:1611.00683

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Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization

2018-09-16 · NeurIPS 2018 12 · Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan 외

Responses generated by neural conversational models tend to lack informativeness and diversity. We present Adversarial Information Maximization (AIM), an adversarial learning strategy that addresses these two related but…

Conversational Response Generation Diversity Informativeness

Learning Linear Utility Functions From Pairwise Comparison Queries

2024-05-04 · Luise Ge, Brendan Juba, Yevgeniy Vorobeychik

We study learnability of linear utility functions from pairwise comparison queries. In particular, we consider two learning objectives. The first objective is to predict out-of-sample responses to pairwise comparisons, w…

Active Learning

Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes

2015-06-12 · NeurIPS 2015 12 · Ryan Giordano, Tamara Broderick, Michael Jordan

Mean field variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of MFVB is that it underestimates the uncer…

Form

Pairwise Relational Networks for Face Recognition

2018-08-15 · ECCV 2018 9 · Bong-Nam Kang, Yonghyun Kim, Daijin Kim

Existing face recognition using deep neural networks is difficult to know what kind of features are used to discriminate the identities of face images clearly. To investigate the effective features for face recognition, …

Face Identification Face Recognition Face Verification

RLINK: Deep Reinforcement Learning for User Identity Linkage

2019-10-31 · Xiaoxue Li, Yanan Cao, Yanmin Shang, Yangxi Li 외

User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise similarity between identities from diff…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)