arxiv. / This divergence leads to an upper bound of the model evidence (called CUBO) and overdispersed posterior approximations. B. Dieng, F. J. R. Ruiz, D. M. Blei, and M. Titsias. Statistical Methods for Finance - Spring 2016 / Avoiding Latent Variable Collapse with Generative Skip Models Slides In my research, I work on combining probabilistic graphical modeling and deep learning to design models for structured high-dimensional data such as text. B. Dieng, Y. Kim, A. M. Rush, and D. M. Blei. Alexander M. Rush, In an interview with Forbes, she … Her research bridges probabilistic graphical models and deep learning to discover meaningful structure from unlabelled data. /. Under Review at Journal of Machine Learning Research (JMLR). Jianfeng Gao, Edward: A Library for Probabilistic Modeling, Inference, and Criticism Adji Bousso Dieng; D. Jeff Dean (computer scientist) G. Ramanathan V. Guha; H. Urs Hölzle; S. Amit Singhal; Ramakrishnan Srikant; T. Sebastian Thrun This page was last edited on 25 August 2020, at 22:08 (UTC). / In this episode, i’m joined by Adji Bousso Dieng, PhD Student in the Department of Statistics at Columbia University. International Conference on Machine Learning (ICML), 2018 / The most used divergence is the Kullback-Leibler (KL) divergence. One wide parameterization of a categorical distribution is the softmax. Adji Bousso Dieng is a Senegalese Computer Scientist and Statistician working in the field of Artificial Intelligence. Sort by citations Sort by year Sort by title. arxiv  She received a scholarship to study abroad after winning this competition. Dieng is supported by a Dean Fellowship from Columbia University. CUBO can be used alongside the usual ELBO to sandwich-estimate the model evidence. This solution leads to the Skip-VAE--a deep generative model that avoids latent variable collapse.  The majority of people do not know about the rich history of STEM and AI developments made possible by Africans. Code /. The RNN component of the model captures syntax while the topic model component captures semantic. In 2013, Dieng accepted a position as a Junior Professional Associate at the World Bank working on risk modeling in the Department of Market and Counterparty Risk. The criterion for learning is a divergence measure. David M. Blei Adji Bousso Dieng is a Senegalese Computer Scientist and Statistician working in the field of Artificial Intelligence. https://cpsc.yale.edu/event/cs-colloquium-adji-bousso-dieng Michalis Titsias B. Dieng, R. Ranganath, J. Altosaar, and D. M. Blei. Turns out EM learns better deep generative models than VI as measured by predictive log-likelihood. The typical workaround is variational inference (VI) which maximizes a lower bound to the log marginal likelihood of the data. Recurrent neural networks are very effective at modeling sequential data. , While abroad, Dieng attended Lycée Henri IV, a public secondary school located in Paris.  Prior to her work at Google, Dieng interned at many major companies in AI such as Microsoft Research in Seattle, DeepMind in London, and she also worked with Yann LeCun at Facebook AI Research. Machine Learning Statistics Deep Learning. Artificial Intelligence and Statistics (AISTATS), 2019, A. The criterion for learning is a divergence measure. , Dieng is the founder of the non-profit called “ The Africa I Know”, with the mission to positively change the narrative about Africa and provide opportunities to young Africans. Francisco J. R. Ruiz, arxiv Come next September, Adji Bousso Dieng — an expert in artificial intelligence and machine learning — will join the faculty of the School of Engineering and Applied Science (SEAS) as a tenure-track assistant professor, becoming the first Black female faculty member in the history of SEAS and the first Black faculty member ever in the Department of Computer Science (COS).  She was also awarded a Master in Applied Statistics from Cornell University in Ithaca, New York. Cited by. , In 2021, Dieng will join the Department of Computer Science at Princeton University as a tenure-track Assistant Professor. Adji Bousso Dieng.  TAIK inspires young Africans to follow careers in STEM and AI, informs people about the contributions in STEM and AI by Africans, and educates about the rich history of Africa. arxiv / My work is funded by a Columbia Dean Fellowship and a Google PhD Fellowship in Machine Learning. Adji Bousso Dieng, D. Tran, R. Ranganath, J. Paisley, D. Blei 2017 Variational inference (VI) is widely used as an efficient alternative to Markov chain Monte Carlo. Email: abd2141 at columbia dot edu. Francisco R. J. Ruiz*, Variational inference is an efficient approach for estimating posterior distributions. Probability and Statistics for Data Science - Fall 2015 Adji B. Dieng, This AI Expert From Senegal Is Helping Showcase Africans In STEM. / Adji gave a talk on April, 28th about her work on TopicRNN and variational inference. Adji Bousso Dieng. Probability - Fall 2014, Science meets Engineering of Deep Learning (SEDL), Columbia's GSAS Student Successes Website, 2nd Symposium on Advances in Approximate Bayesian Inference, deep generative models and structured data, Women in Machine Learning Mentorship Roundtable, Carnegie Mellon University Machine Learning Seminar, IPAM Workshop on Interpretable Learning in Physical Systems, University of Maryland's Rising Stars in Machine Learning seminar, New York Machine Learning and Artificial Intelligence Meetup, South England Natural Language Processing Meetup, Dec 2019: Happy to be serving as advisor for the, Sep 2019: I was very glad to serve as Area Chair for the, Aug 2019: I will be giving a two-hour lecture on deep generative models at this year's, May 2019: I co-organized an ICLR workshop on, Sep 2018: I will be spending this Fall semester at, May 2018: I am excited to be interning with Yann LeCun at. , Dieng is currently working at Google Brain as a Research Scientist in Artificial Intelligence (AI). 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