This solution leads to the Skip-VAE--a deep generative model that avoids latent variable collapse. Google Scholar  /  Adji B. Dieng, VI introduces an undesirable amortization gap and often causes latent variable collapse. My second goal is to develop efficient, scalable, and generic algorithms for learning with these models. [2] She won one of the prizes for the Senegalese Olympiad ("Concours Général") in Philosophy, was selected to participate in the 2005 Excellence camp organized by the Pathfinder Foundation for Education and Development, a non-profit founded by Cheick Modibo Diarra, and was subsequently selected to participate in a competitive exam organized for African girls in partnership between the Central Bank for West African States and the Pathfinder Foundation. Our paper proposes the Chi-divergence for variational inference. Adji B. Dieng*, Form a generative model of documents that defines the likelihood of a word as a Categorical whose natural parameter is the dot product between the word embedding and its assigned topic's embedding. Noisin significantly outperforms Dropout on both the Penn TreeBank and the Wikitext-2 datasets on a language modeling task. Adji Bousso Dieng is a Senegalese Computer Scientist and Statistician working in the field of Artificial Intelligence.Her research bridges probabilistic graphical models and deep learning to discover meaningful structure from unlabelled data. Home; Random; Nearby; Log in; Settings; Donate; About Wikipedia; Disclaimers; Subcategories. An extension of the Embedded Topic Model to corpora with temporal dependencies. In natural language these long-term dependencies come in the form of semantic dependencies. My first goal is to combine deep learning and probabilistic graphical modeling to design models that are expressive and powerful enough to capture meaningful representations of high-dimensional structured data. B. Dieng, R. Ranganath, J. Altosaar, and D. M. Blei. / 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). Title. Dustin Tran, /. International Conference on Machine Learning (ICML), 2018, A. arxiv / Adji Bousso Dieng will be Princeton's School of Engineering's first Black female faculty. In 2021, she will start her tenure-track faculty position at Princeton University becoming the first Black female faculty member in the School of Engineering and Applied Science as well as the first Black faculty member ever in the Department of Computer Science. … Read the rest. [4], Dieng has authored/co-authored several papers published in AI venues such as NeurIPS, ICML, ICLR, AISTATS, and TACL. Verified email at columbia.edu - Homepage. [7] She will be the first Black faculty in Computer Science in Princeton's history, the first Black woman tenure-track faculty in Princeton's School of Engineering, and the second Black woman tenure-track faculty in Computer Science across the Ivy League. In my research, I work on combining probabilistic graphical modeling and deep learning to design models for structured high-dimensional data such as text. Adji B. Dieng, John Paisley Text is available under the Creative Commons Attribution-ShareAlike License; additional terms … Artificial Intelligence and Statistics (AISTATS), 2019, A. Prescribed Generative Adversarial Networks. CUBO can be used alongside the usual ELBO to sandwich-estimate the model evidence. B. Dieng and J. Paisley. The Dynamic Embedded Topic Model Not only has Adji Bousso Dieng, an AI researcher from Senegal, contributed to the field of generative modeling and about to become one of the first black female faculty in Computer Science in the Ivy League, she is also helping Africans in STEM tell their own success stories. Achieving these two goals will benefit many applications. Rajesh Ranganath, International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 This AI Expert From Senegal Is Helping Showcase Africans In STEM. This two-step procedure shies away from the current VAE approach of bundling together model fitting and posterior inference. Dieng is supported by a Dean Fellowship from Columbia University. Adji B. Dieng, David M. Blei arxiv David M. Blei Journal of Machine Learning Research (JMLR) (Submitted) /. The topic model and the RNN parameters are learned jointly using amortized variational inference. A&R is built on two ideas: latent variable augmentation and stochastic variational expectation maximization. Research scientist Adji Bousso Dieng, from Senegal, launched the website "The Africa I Know" to highlight experts in STEM in Africa. Artificial Intelligence (AI) researcher Adji Bousso Dieng will become the first black woman faculty to join Princeton’s School of Engineering in its 100-year history. Adji B. Dieng, [2] Her father never attended school, and her mother started but did not complete high school. One challenge in modeling sequential data with RNNs is the inability to capture long-term dependencies. Our paper proposes a simple solution that relies on skip connections. Speaking by phone from her home in New York, Dieng said her mother had taught her to value education. Posterior inference is done after the model is fitted. Dustin Tran, I am fortunate to have been a teaching assistant for the following courses at Columbia University. [2] She was also awarded a Master in Applied Statistics from Cornell University in Ithaca, New York. Adji Bousso Dieng CV / Google Scholar / LinkedIn / Github / Twitter / Email: abd2141 at columbia dot edu I am a Ph.D candidate in the department of Statistics at Columbia University where I am jointly being advised by David Blei and John Paisley . Neural Information Processing Systems (NIPS), 2017. One of the current staples of unsupervised representation learning is variational autoencoders (VAEs). Chong Wang, Michalis Titsias, International Conference on Machine Learning (ICML), 2018 The DETM is fit using structured amortized variational inference with LSTMs. (2) How can we evaluate predictive log-likelihood for GANs to assess how they generalize to new data? CUBO can be used alongside the usual ELBO to sandwich-estimate the model evidence. Transactions of the Association for Computational Linguistics (TACL), 2020, A. Adji B. Dieng, Dec 2017: I am glad to be part of the mentors for this year's, Yahoo Research Seminar Series, New York, NY, July 2019, Microsoft Research Cambridge, Cambridge, UK, January 2019, Harvard University NLP Group Meeting, Cambridge, MA, April 2018, MSR AI, Microsoft Research, Redmond, WA, August 2017, SSLI Lab, University of Washington, Seattle, WA, August 2017, IBM TJ Watson Research, Yorktown Heights, NY, December 2016, Microsoft Research, Redmond, WA, August 2016. [5], In 2021, Dieng will join the Department of Computer Science at Princeton University as a tenure-track Assistant Professor. Augment and Reduce: Stochastic Inference for Large Categorical Distributions In 2013 she graduated from Télécom ParisTech, earning her Diplome d'ingenieur (a degree in Engineering from France's Grandes Ecoles system). Augment and Reduce: Stochastic Inference for Large Categorical Distributions. However minimizing the KL leads to approximations that underestimate posterior uncertainty. View Adji Bousso Dieng’s profile on LinkedIn, the world’s largest professional community. — Adji Bousso Dieng (@adjiboussodieng) August 30, 2020. Adji Bousso Dieng SCIENCE, TECH AND INNOVATION . Adji Bousso Dieng is a PhD student at Columbia University, supervised by Prof. David Blei and John Paisley. Code 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. [2] Dieng was one of 15 siblings, and to support the family, her parents owned a business selling fabric. Adji Bousso Dieng is currently a Research Scientist at Google AI, and will be starting as an assistant professor at Princeton University in 2021. Poster David M. Blei Reweighted Expectation Maximization. Under submission at Journal of Machine Learning Research (JMLR) B. Dieng, C. Wang, J. Gao, and J. W. Paisley. New website by Senegalese AI expert spotlights Africans in STEM. I am a Ph.D candidate in the department of Statistics [5] She left the World Bank the following summer, in 2014, after being awarded a Columbia University Dean Fellowship to start a PhD in Statistics. Francisco R. J. Ruiz, Github  /  Recurrent neural networks are very effective at modeling sequential data. [2], During high school, Dieng was recognized for her academic achievements. A. The decoder of a Skip-VAE is a neural network whose hidden states--at every layer--condition on the latent variables. [3], Dieng is currently working at Google Brain as a Research Scientist in Artificial Intelligence (AI). This divergence leads to an upper bound of the model evidence (called CUBO) and overdispersed posterior approximations. Noisin relies on the notion of "unbiased" noise injection. Alp Kucukelbir, Rajesh Ranganath, [9] Dieng noticed the inaccurate portrayal of Africa in the media, which was further accentuated during the COVID-19 global crisis. May 2018: I co-authored two papers that are appearing at this year's ICML: "Augment and Reduce: Stochastic Inference for Large Categorical Distributions" and "Noisin: Unbiased Regularization for Recurrent Neural Networks". Her research is in Artificial Intelligence and Statistics, bridging probabilistic graphical models and deep learning. arxiv Maximum likelihood in deep generative models is hard. I hold a Diplome d'Ingenieur from Telecom ParisTech and spent the third year of Telecom ParisTech's curriculum at Cornell University where I earned a Master in Statistics. 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. Our paper proposes the Chi-divergence for variational inference. wikipedia. [5] Dieng worked with David Blei and John Paisley to bridge Probabilistic Graphical Modeling and Deep Learning with the goal of discovering meaningful patterns from unlabelled data for applications in natural language processing, computer vision, and healthcare. B. Dieng, F. J. R. Ruiz, D. M. Blei, and M. Titsias. Topic Modeling in Embedding Spaces CV  /  However softmax does not scale well when there are many categories. Dawen Liang, / [3][4] Dieng's doctoral work has received various forms of recognition including the Google PhD Fellowship in Machine Learning[3] and a Rising Star in Machine Learning nomination by the University of Maryland. View adji bousso profile dieng's linkedin, world's the largestcommunity. / In this episode, i’m joined by Adji Bousso Dieng, PhD Student in the Department of Statistics at Columbia University. Key ingredients: noise, entropy regularization, and Hamiltonian Monte Carlo. Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David Blei Abstract Variational inference (VI) is widely used as an efficient alternative to Markov chain Monte Carlo. [2], While abroad, Dieng attended Lycée Henri IV, a public secondary school located in Paris. We propose a new regularization method called Noisin. TopicRNN is a deep generative model of language that marries RNNs and topic models to capture long-term dependencies. John Paisley, Code B. Dieng, F. J. R. Ruiz, and D. M. Blei. Adji dan agung - View adji bousso linkedin, on linkedin, the largestprofessional. arxiv / Statistical Machine Learning - Spring 2019 International Conference on Machine Learning (ICML) (Submitted) Adji B. Dieng, Her research bridges probabilistic graphical models and deep learning to discover meaningful structure from unlabelled data. Sort by citations Sort by year Sort by title. However they tend to have very high capacity and overfit very easily. arxiv It consists in positing a family of distributions and finding the distribution in this family that better approximates the true posterior. [3] The majority of people do not know about the rich history of STEM and AI developments made possible by Africans. Francisco R. J. Ruiz*, One wide parameterization of a categorical distribution is the softmax. A tensorflow-based library for probabilistic programming. International Conference on Learning Representations (ICLR), 2017 Her research is in Artificial Intelligence and Statistics, bridging probabilistic graphical models and deep learning. Code Alexander M. Rush, Columbia University. Dieng was born and raised in Kaolack, Senegal. Adji Bousso Dieng is a PhD Candidate at Columbia University where she is jointly advised by David Blei and John Paisley. B. Dieng, Y. Kim, A. M. Rush, and D. M. Blei. Her research focuses on combining probabilistic graphical modeling and deep learning to design models for structured high-dimensional data. and John Paisley. [10][11] Another goal of the initiative is to provide role models to young Africans, who often grow up without seeing role models that look like them due to a lack of visibility. I also work on variational methods as an inference framework for fitting these models. / Speaker Bio: Adji Bousso Dieng is a PhD candidate at Columbia University where she works with David Blei and John Paisley. The RNN component of the model captures syntax while the topic model component captures semantic. We propose to use expectation maximization (EM) instead. B. Dieng, D. Tran, R. Ranganath, J. W. Paisley and D. M. Blei. Bio: Adji Bousso Dieng is a PhD Candidate at Columbia University where she is jointly advised by David Blei and John Paisley. [4], After working at the World Bank for one year, Dieng started her PhD in Statistics at Columbia University. Adji Bousso Dieng. Code John Paisley, / / Importantly, we separate posterior inference and model fitting. LinkedIn  /  We propose a method called A&R that scales learning with categorical distributions. David M. Blei I did my undergraduate training in France where I attended Lycee Henri IV and Telecom ParisTech--France's Grandes Ecoles system. Poster 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. 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