Adji Bousso Dieng. Her research bridges probabilistic graphical models and deep learning to discover meaningful structure from unlabelled data. Slides [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. Under submission at Journal of Machine Learning Research (JMLR) Reweighted Expectation Maximization. Cited by. Probability and Statistics for Data Science - Fall 2015 Adji Bousso Dieng is a PhD Candidate at Columbia University where she is jointly advised by David Blei and John Paisley. Her work at Columbia is about combining probabilistic graphical modeling and deep learning to design better generative models. Dieng is supported by a Dean Fellowship from Columbia University. / A&R is built on two ideas: latent variable augmentation and stochastic variational expectation maximization. International Conference on learning Representation (ICLR), 2017, A. Adji Bousso Dieng will be Princeton’s School of Engineering’s first Black female faculty. This two-step procedure shies away from the current VAE approach of bundling together model fitting and posterior inference. 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". The most used divergence is the Kullback-Leibler (KL) divergence. Under review at Transactions of the Association for Computational Linguistics (TACL), 2019 Adji Bousso Dieng is currently a Research Scientist at Google AI, and will be starting as an assistant professor at Princeton University in 2021. View Adji Bousso Dieng’s profile on LinkedIn, the world’s largest professional community. CUBO can be used alongside the usual ELBO to sandwich-estimate the model evidence. We propose a new regularization method called Noisin. arxiv. [4] She then attended Télécom ParisTech, a top French public institution of higher education and research of engineering located in Palaiseau, France. Sort by citations Sort by year Sort by title. 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). /. However minimizing the KL leads to approximations that underestimate posterior uncertainty. Jaan Altosaar, TopicRNN: A Recurrent Neural Network With Long-Range Semantic Dependency Noisin: Unbiased Regularization for Recurrent Neural Networks. In this episode, Sam Charrington is joined by Adji Bousso Dieng, PhD Student in the Department of Statistics at Columbia University to discuss two of her recent papers, “Noisin: Unbiased Regularization for Recurrent Neural Networks” and TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency. TopicRNN is a deep generative model of language that marries RNNs and topic models to capture long-term dependencies. The family business was selling fabric, and neither of her parents finished school. [3] The majority of people do not know about the rich history of STEM and AI developments made possible by Africans. B. Dieng and J. Paisley. My work is funded by a Columbia Dean Fellowship and a Google PhD Fellowship in Machine Learning. Her research is in Artificial Intelligence and Statistics, bridging probabilistic graphical models and deep learning. Chong Wang, and John Paisley. 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. Articles Cited by Co-authors. Dustin Tran, arxiv The typical workaround is variational inference (VI) which maximizes a lower bound to the log marginal likelihood of the data. Adji Bousso Dieng. Github  /  Dieng attended Kaolack's public schools for both elementary and high school. Adji Bousso Dieng 2 Publications & Preprints A. CV  /  David M. Blei 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. /. Code www.mobilewiki.org adji bousso dieng Adji Bousso Dieng. / It consists in positing a family of distributions and finding the distribution in this family that better approximates the true posterior. [2] She was also awarded a Master in Applied Statistics from Cornell University in Ithaca, New York. Dieng is supported by a Dean Fellowship from Columbia University. John Paisley, read more. Edward: A Library for Probabilistic Modeling, Inference, and Criticism Dieng spent her third year of Telecom ParisTech's curriculum at Cornell University. The Dynamic Embedded Topic Model /. Reweighted Expectation Maximization However they tend to have very high capacity and overfit very easily. 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 dan agung - View adji bousso linkedin, on linkedin, the largestprofessional. Slides. /. Under Review at Journal of Machine Learning Research (JMLR). 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. Adji B. Dieng, However softmax does not scale well when there are many categories. John Paisley Noisin significantly outperforms Dropout on both the Penn TreeBank and the Wikitext-2 datasets on a language modeling task. This results in a stronger dependence between observations and their latents and therefore avoids latent variable collapse. This category has the following 7 subcategories, out of 7 total. / GROWING up in a trading town in Senegal, Adji Bousso Dieng loved school and had a particular talent for maths. View adji bousso profile dieng's linkedin, world's the largestcommunity. img. Posterior inference is done after the model is fitted. Our paper proposes the Chi-divergence for variational inference. Statistical Machine Learning - Spring 2019 Maximum likelihood in deep generative models is hard. Poster International Conference on Machine Learning (ICML) (Submitted) Prescribed Generative Adversarial Networks Dieng is supported by a Dean Fellowship from Columbia University. arxiv / Slides Augment and Reduce: Stochastic Inference for Large Categorical Distributions. [2], While abroad, Dieng attended Lycée Henri IV, a public secondary school located in Paris. A. The criterion for learning is a divergence measure. [2] She received a scholarship to study abroad after winning this competition. One wide parameterization of a categorical distribution is the softmax. B. Dieng, C. Wang, J. Gao, and J. W. Paisley. In 2013 she graduated from Télécom ParisTech, earning her Diplome d'ingenieur (a degree in Engineering from France's Grandes Ecoles system). Code Adji Bousso Dieng's articles on arXiv [1] arXiv:1907.05545 [pdf, other] Title: The Dynamic Embedded Topic Model ... Adji B. Dieng, and David M. Blei. Journal of Machine Learning Research (JMLR) (Submitted) LinkedIn  /  Adji B. Dieng, Variational Inference via χ Upper Bound Minimization. Twitter  /  My second goal is to develop efficient, scalable, and generic algorithms for learning with these models. Her research focuses on combining probabilistic graphical modeling and deep learning to design models for structured high-dimensional data. Adji B. Dieng, International Conference on Machine Learning (ICML), 2018, A. B. Dieng, F. J. R. Ruiz, D. M. Blei, and M. Titsias. Dieng was born and raised in Kaolack, Senegal. Topic Modeling in Embedding Spaces My goal as a Machine Learning researcher is twofold. The topic model and the RNN parameters are learned jointly using amortized variational inference. Francisco J. R. Ruiz, The resulting Embedded Topic Model (ETM) learns interpretable topics and word embeddings and is robust to large vocabularies that include rare words and stop words. Year; Edward: A library for probabilistic modeling, inference, and criticism. One of the current staples of unsupervised representation learning is variational autoencoders (VAEs). International Conference on Machine Learning (ICML), 2018 Code The DETM models each word with a categorical distribution whose parameter is given by the inner product between the word embedding and an embedding representation of its assigned topic at a particular time step. I am fortunate to have been a teaching assistant for the following courses at Columbia University. / 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 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. Adji Bousso Dieng SCIENCE, TECH AND INNOVATION . Yoon Kim, 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. Francisco R. J. Ruiz, at Columbia University where I am jointly being advised by David Blei Statistical Methods for Finance - Spring 2016 / [6] 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. Our paper proposes the Chi-divergence for variational inference. This divergence leads to an upper bound of the model evidence (called CUBO) and overdispersed posterior approximations. [4], Dieng has authored/co-authored several papers published in AI venues such as NeurIPS, ICML, ICLR, AISTATS, and TACL. [9] 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. [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. We propose a method called A&R that scales learning with categorical distributions. The decoder of a Skip-VAE is a neural network whose hidden states--at every layer--condition on the latent variables. Adji B. Dieng, The most used divergence is the Kullback-Leibler (KL) divergence. However minimizing the KL leads to approximations that underestimate posterior uncertainty. /. 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 . Achieving these two goals will benefit many applications. arxiv Prescribed Generative Adversarial Networks. Home; Random; Nearby; Log in; Settings; Donate; About Wikipedia; Disclaimers; Subcategories. Turns out EM learns better deep generative models than VI as measured by predictive log-likelihood. Prior to joining Columbia I worked as a Junior Professional Associate at the World Bank. [2] Her father never attended school, and her mother started but did not complete high school. Adji B. Dieng, [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. International Conference on Machine Learning (ICML), 2018 [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. But with a dearth of career role models, she had no idea which path to follow. This paper describes a solution to two important problems in the GAN literature: (1) How can we maximize the entropy of the generator of a GAN to prevent mode collapse? In this episode, i'm joined by Adji Bousso Dieng, PhD Student in the Department of Statistics at Columbia University. Bio: Adji Bousso Dieng is a PhD Candidate at Columbia University where she is jointly advised by David Blei and John Paisley. AI expert Adji Bousso Dieng to become first Black female faculty member at SEAS. Transactions of the Association for Computational Linguistics (TACL), 2020, A. David M. Blei 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. Jianfeng Gao, Thomas Kipf. The word embeddings allow the DETM to generalize to rare words. Adji Bousso Dieng is a PhD Candidate at Columbia University where she is jointly advised by David Blei and John Paisley. David M. Blei One challenge in modeling sequential data with RNNs is the inability to capture long-term dependencies. Adji B. Dieng, https://cpsc.yale.edu/event/cs-colloquium-adji-bousso-dieng [1] Dieng recently founded the non-profit “The Africa I Know” (TAIK) with the goal to inspire young Africans to pursue careers in STEM and AI by showcasing African role models, informing the general public about developments in STEM and AI by Africans, and educating the general public about the rich history of Africa. (2) How can we evaluate predictive log-likelihood for GANs to assess how they generalize to new data? Adji B. Dieng, This solution leads to the Skip-VAE--a deep generative model that avoids latent variable collapse. Rajesh Ranganath, Title. However they suffer from a problem known as "latent variable collapse". An extension of the Embedded Topic Model to corpora with temporal dependencies. In this episode, i’m joined by Adji Bousso Dieng, PhD Student in the Department of Statistics at Columbia University. David M. Blei Variational inference is an efficient approach for estimating posterior distributions. [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. Topic Modeling in Embedding Spaces. Adji Bousso has 9 jobs listed on their profile. Alexander M. Rush, [4], After working at the World Bank for one year, Dieng started her PhD in Statistics at Columbia University. International Conference on Learning Representations (ICLR), 2017 Adji B. Dieng, Alp Kucukelbir, — Adji Bousso Dieng (@adjiboussodieng) August 30, 2020. Code … Read the rest. Email: abd2141 at columbia dot edu. David M. Blei Adji gave a talk on April, 28th about her work on TopicRNN and variational inference. New website by Senegalese AI expert spotlights Africans in STEM. Key ingredients: noise, entropy regularization, and Hamiltonian Monte Carlo. International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 Variational inference is an efficient approach for estimating posterior distributions. Code. [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. [8], 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. wikipedia. David M. Blei, Recurrent neural networks are very effective at modeling sequential data. / It consists in positing a family of distributions and finding the distribution in this family that better approximates the true posterior. CUBO can be used alongside the usual ELBO to sandwich-estimate the model evidence. Adji Bousso Dieng will be Princeton's School of Engineering's first Black female faculty. For the following 7 Subcategories, out of 7 total called CUBO ) and overdispersed posterior approximations her... 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