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structure of deep networks to adapt to new tasks. ... We present a novel Bayesian approach to continual learning based on learning the structure of deep neural networks, addressing the shortcomings of both these approaches. ... A Nonparametric Bayesian Approach to Continual Learning We present a nonparametric Bayesian model for continual learning that can potentially grow and adapt its structure as more and more tasks are observed ...arXiv:1912.03624v2 fatcat:bk4nd7dazjhizhaj2dbzogo23i
For instance, novel theoretical models and optimization techniques developed in machine learning are largely unknown within the social and biological sciences, which have instead emphasized model interpretability ... Despite this diversity, many of these models share a common underlying structure: pairwise interactions (edges) are generated with probability conditional on latent vertex attributes. ... For example, how can we understand what is being traded off when we use a latent block model under a Bayesian nonparametric learning paradigm versus a general latent space model under a frequentist learning ...arXiv:1411.4070v1 fatcat:6z2nwwdxlnhb5cxflpprgosmza
A particular focus was on high dimensional statistics, semiparametrics, adaptation, nonparametric bayesian statistics, shape constraint estimation and statistical inverse problems. ... The goal of this workshop was to discuss recent developments of nonparametric statistical inference. ... Nonparametric Bayesian Inference Nonparametric Bayesian analysis has been recently proved to be a very powerful and necessary tool for understanding widely but frequently used ad hoc Bayesian computational ...doi:10.4171/owr/2012/14 fatcat:ndg63i4x4vaqnpjitstzxrsh6a
To address the challenge, this work presents a novel nonparametric Bayesian formulation for the task. ... For the task of relation extraction, distant supervision is an efficient approach to generate labeled data by aligning knowledge base with free texts. ... learning with intrinsic clustering structures. ...doi:10.18653/v1/d17-1192 dblp:conf/emnlp/ZhangW17 fatcat:jn77hhhvlrbspnnolac5aciarq
Over the past decade, artificial intelligence (AI) and machine learning (ML) have become the breakthrough technology most anticipated to have a transformative effect on pharmaceutical research and development ...  proposed nonparametric Bayesian learning-based designs for adaptive dose finding with multiple populations. ... Nonparametric Bayesian learning has emerged as a powerful tool in modern ML framework due to its flexibility, providing a Bayesian framework for model selection using a nonparametric approach. ...doi:10.1208/s12248-021-00644-3 pmid:34984579 pmcid:PMC8726514 fatcat:kqkvymwqtvbi7htvrz2szjsqpe
" applied Bayesian nonparametrics to machine learning  . ... Lastly, Bayesian learning has inherent interpretability thanks to its clear and meaningful probabilistic structure. ... An alternative definition for BNL follows. Definition 3 (Bayesian nonparametric learning). ...doi:10.1145/3291044 fatcat:aytdnsnrfvfnti5i64ne4icenu
We introduce a Bayesian nonparametric ensemble (BNE) approach that augments an existing ensemble model to account for different sources of model uncertainty. ... BNE augments a model's prediction and distribution functions using Bayesian nonparametric machinery. ... Acknowledgement Authors would like to thank Lorenzo Trippa, Jeff Miller, Boyu Ren at Harvard Biostatistics, Yoon Kim at Harvard CS and Ge Liu at MIT EECS for the insightful comments and fruitful discussion ...arXiv:1911.04061v1 fatcat:wviitjqfyzhlpnaiwf3wkjnbuq
In this paper, we propose a method over a learned dictionary based on nonparametric methods for this problem. The structure follows the two steps of normal dictionary learning procedure. ... In one step we fix the dictionary and learn the sparse coefficient vector based on Bayesian nonparametric variable selection, while in the other step we minimize the objective based on the dictionary with ... CONCLUSION This work has presented Bayesian nonparametric method for sparse dictionary learning, whose results have a state-of-the-art performance in image denoising. ...doi:10.7763/ijcee.2012.v4.554 fatcat:tfa3z5sbcvdspoikud5mf52cau
Thereby, our method combines the flexibility of nonparametric Bayesian learning with epistemological guarantees on the expected closed-loop trajectory. ... In contrast to previous work that has used stochastic processes for identification, we leverage the structural knowledge afforded by Lagrangian mechanics and learn the drift and control input matrix functions ... This property has led to a surge of interest in Bayesian nonparametrics; particularly benefiting their algorithmic advancement and application to a plethora of learning problems. ...arXiv:1311.4468v3 fatcat:77bhtdfozbawdkdhlmpygssbeu
Gittins, Dynamic allocation indices for Bayesian bandits (pp. 50-67); K. D. ... El-Fattah, Learning automaton for finite semi-Markov decision processes (pp. 33-42); Vaclav Fabian, A local asymptotic minimax optimality of an adaptive Robbins-Monro stochastic approximation procedure ...
In this paper, we propose the Bayesian nonparametric tensor decomposition (BNPTD) to achieve incomplete traffic data imputation and similarity pattern discovery simultaneously. ... Furthermore, we develop an efficient variational inference algorithm to learn the model. ... DPMM is a nonparametric Bayesian mixture model that has shown great promise for data clustering and allows for the automatic determination of an appropriate number of clusters. ese two components are combined ...doi:10.1155/2020/8810753 fatcat:osj2i6ukinezvicl2zyrsqvoue
The Indian buffet process (IBP)  is a nonparametric model which can be used for latent feature modelling, learning overlapping clusters, sparse matrix factorisation, or to nonparametrically learn the ... It's worth noting that computational techniques are one area where Bayesian machine learning differs from much of the rest of machine learning: for Bayesians the main computational problem is integration ... In continuous spaces, most Bayesian optimisation methods use Gaussian processes (as described in the section on nonparametrics) to model the unknown function. ...doi:10.1038/nature14541 pmid:26017444 fatcat:sw42v3vzcraj3mhimxr4w2g6du
Mark Berliner, A decision theoretic structure for robust Bayesian analysis with applications to the estimation of a multi- variate normal mean (pp. 619-628); G. Consonni and A. P. ... Poirier, Bayesian hypothesis testing in linear models with continuously induced conjugate priors across hypotheses (pp. 711-722); Wolfgang Polasek, Hierarchical models for seasonal time series (pp. 723 ...
This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including nonparametric Bayesian methods for adaptively inferring model complexity ... Bayesian methods represent one important class of statistic methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. ... Nonparametric Bayesian (NPB) methods provide an elegant solution to such needs on automatic adaptation of model capacity when learning a single model. ...arXiv:1411.6370v2 fatcat:zmxse4kkqjgffkricevyumaoiu
a Bayesian nonparametric topic model that discovers meaningful latent patterns within discrete data. ... We apply this model to the motivating application of high-level scene understanding and mission summarization for exploratory marine robots. ... Bayesian nonparametric models are well suited for the lifelong learning required in streaming and robotics applications; this is one compelling reason to use them over purely neural models. ...doi:10.1109/iros.2017.8202130 dblp:conf/iros/FlaspohlerRG17 fatcat:w7deizz6hvcexozreqpu4vy3fe
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