Topic 9: Other Topics 3312


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Advanced Data Analysis from an Elementary Point of View Cosma Rohilla Shalizi 2019
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Sequence Modeling and Embeddings Yun-Nung Chen 2019
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Representations in Deep Reinforcement Learning Pieter Abbeel 2019
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Deep Learning for Ordinary Regression in PyTorch Sebastian Raschka 2019
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?? 2019
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Getting Closer to Human Intelligence through Robots Brandon Rohrer 2015
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Sequence Models Noah Smith 2019
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Introduction to recommender systems Walid Krichene 2019
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Mining Structures of Factual Knowledge from Text Xiang Ren 2019
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Uncertainty Estimation in Deep Learning Christian S. Perone 2019
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Data Driven Algorithm Design Maria-Florina Balcan 2019
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Advanced Optimization Hasma Fawzi 2019
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Variational Inference for Machine Learning Shakir Mohamed 2019
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Deep Learning Timeline NONE 2019
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Reinforcement Learning: An Introduction Vukosi Marivate 2019
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Geometry in Computer Vision Natalia Neverova 2019
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Semantic Data Management in Graph Databases Alejandro Flores 2019
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An Introduction to Statistical Learning Gareth James 2019
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Deep Adversarial Learning for NLP William Wang, Sameer Singh 2019
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NONE NONE 2019
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Lessons Learned from building Deep Learning systems Xavier Amatriain 2019
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Fast code with just enough effort Pashmina Cameron 2019
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Introduction to optimization Walid Krichene 2019
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Individual and Collective Graph Mining Danai Koutra 2019
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L11: Sequential Feature Selection Ricardo Gutierrez-Osuna 2019
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A Geometric View of Optimal Transportation and Generative Adversarial Networks Shing-Tung Yau 2019
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Recurrent Neural Networks Ahmed Kachkach 2019
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Fast Start Advanced Calculus Daniel Ashlock 2019
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Predicting Structures in NLP: Constrained Conditional Models and Integer Linear Programming Dan Goldwasser 2019
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How Neural Networks Work Brandon Rohrer 2019
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Computer Vision at Scale Manolar Paluri 2019
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Optimization for Machine Learning John Duchi 2019
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Support Vector Machines Amdrew Ng 2019
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Probabilistic Reasoning and Variational Inference Shakir Mohamed 2019
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Natural Language Representations and Challenges Slav Petrov 2019
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Mathematics of Machine Learning Hasma Fawzi 2019
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Phrase Mining from Massive Text and Its Applications Jialu Liu 2019
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A Survey of Information Retrieval and Filtering Methods Christos Faloutsos 2019
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How neural networks work Brandon Rohrer 2016
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Convexity A La Carte Leon Bottou 2019
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Recommender Systems Ahmed Kachkach 2019
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Fast Start Integral Calculus Daniel Ashlock 2019
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The Handbook of Computational Linguistics and Natural Language Processing Alexander Clark 2019
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The Fundamentals of Machine Learning Willie Brink 2019
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Convolutional Models James Allingham 2019
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How to Write a Great Research Paper Arthur Gretton 2019
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Unsupervised Learning Alta de Waal 2019
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Linear Classifiers Andre Martins 2019
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Introduction to Machine Learning Alhussein Fazwi 2019
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Never Ending Learning Tom Mitchell 2019
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Introduction to Deep Learning Tess Terrandez 2019
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How Long Short-Term Memory (LSTM) And Recurrent Neural Networks (RNNs) work Brandon Rohrer 2016
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Spoilers The Elements of Statistical Learning AI Institute 2019
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Sequence Learning with Recurrent Neural Networks Alex 2019
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Cross-Lingual Word Embeddings Anders Sogaard 2019
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Learning Deep Generative Models of Graphs Yujia Li 2019
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Deep Generative Models Ulrich Paquet 2019
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Characterizing optimization bias in terms of optimization geometry Suriya Gunasekar 2019
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Generative Adversarial Networks Sebastien Nowozin 2019
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Success Stories of Deep RL David Silver 2019
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Introduction to Neural Networks Bhiksha Raj 2019
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Applications of ML to Healthcare Bernadino Romera-Paredes 2019
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Casual Inference: What If Miguel A. Hernan 2019
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Introduction to Deep Learning with TensorFlow Sebastian Raschka 2019
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Python Machine Learning Sebastian Raschka, Vahid Mirjalili 2016
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The other stuff: Turning machine learning into data science Brandon Rohrer 2014
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Benign Overfitting Peter Bartlett 2019
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Statistics for Machine Learning Saad Benjelloun 2019
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Bayesian Analysis in Natural Language Processing Shay Cohen 2019
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Applications of Machine Learning in Healthcare Danielle Belgrave 2019
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Interpretable Machine Learning: The Fuss, the Concrete, and the Questions Been Kim 2019
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Geometry of Data: Sparse Metric Repair Lalit Jain 2019
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Transfer Learning in NLP Daniel Pressel 2019
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Socially Responsible Natural Language Processing Yulia Tsvetkov 2019
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Language learning and processing in people and machines Aida Nematzadeh 2019
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Probabilistic Reasoning in Deep Learning Konstantina Palla 2019
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Learning Program Representations: Symbols to Vectors to Semantics Charles Sutton 2019
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Advanced ML: Unsupervised learning with autoregressive and latent variable models Jan Chorowski 2018
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Making Better Use of the Crowd Jenn Wortman Vaughan 2017
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Foundations of Machine Learning Kernel Methods Mehryar Mohri 2018
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Deep Learning for Dialogue Systems Vivian Chen 2019
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Statistical machine learning and convex optimization Francis Bach 2019
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Fairness & Bias Nihar Shah, Zachary Lipton 2020
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Summary & Looking Forward Yuxiao Dong and Iris Shen 2019
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Full Stack Deep Learning Introduction to the Text Recognizer Lab Josh Tobin 2019
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Vector Calculus Michael Corral 2019
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Reinforcement Learning, Imitation Learning & Applications to Learning to Optimize Yisong Yue 2020
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Deep Learning: Algorithms and Applications Witold Pedrycz and Shyi-Ming Chen 2020
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Beyond Incrementality Improving Fluidity Arash Eshghi and Julian Hough 2019
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Model-based Deep Reinforcement Learning Chelsea Finn 2019
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Deep Learning II Unsupervised Learning Russ Salakhutdinov 2019
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Deep Natural Language Processing for Search and Recommender Systems Weiwei Guo, et al. 2019
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How to Write a Great Research Paper Nando de Freitas, Claire Monteleoni, David Lopez-Paz, and Martin Arjovsky 2019
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The Deep Learning - Applied Math Connection Yann LeCun 2019
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How Meta-Learning Could Help Us Accomplish Our Grandest AI Ambitions, and Early, Exotic Steps in that Direction Jeff Clune 2019
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Foundations of Machine Learning On-line Learning Mehryar Mohri 2018
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How to write a great research paper Nando de Freitas 2019
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Neural Vector Representations beyond Words: Phrases Gerard de Melo 2019
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Deep Learning for Natural Language Processing Erik Arakelyan 2018
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Graph Properties and Applications Yuxiao Dong and Iris Shen 2019
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Mathematics of Deep Learning Lecture 10 Joan Bruna 2019
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Deep Learning for AI Yoshua Bengio 2019
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Integer Linear Programming Formulations in Natural Language Processing Vivek Srikumar 2017
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Data for Learning How to Teach and Learn Cecilia Aguerrebere 2019
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Harmonic Analysis of Deep Convolutional Neural Networks Helmut Bolcskei 2017
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Combinatory Categorical Grammar Mark Steedman 2019
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Web Data Integration Types of Structured Data on the Web Bizer 2019
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Practical concerns in predicting structures 2017
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Tutorial: Deep Learning for Healthcare: From Disease Detection to Drug Discovery Cao (Danica) Xiao, Jimeng Sun 2019
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Data-dependent Regularization and Generalization Bounds of Neural Networks Tengyu Ma 2019
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Pyro: Deep Universal Probabilistic Programming 2019
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Warm-up: Proability Theory Mario A. T. Figueriredo 2019
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Machine Learning for Natural Language Processing Fundamentals 2 Erik Arakelyan, Adam Bittlingmayer 2018
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Foundations of Machine Learning Support Vector Machines Mehryar Mohri 2018
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A Survey on Lexical Simplification Gustavo H. Paetzold 2019
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How to give a great research talk Simon Peyton Jones 2019
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Language and Linguistics Winston Wu 2019
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The Surprising Simplicity of Overparameterized Deep Neural Networks Jeffrey Pennington 2019
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Sample-Based Approximations and Function Fitting Yan Duan 2019
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Wikification and Beyond Dan Roth 2014
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Speech Reports Day 1 Pranav Anand and Natasha Korotkova 2019
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Compositional Semantics Jacob Andreas 2019
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A Biologically Plausible Learning Algorithm for Neural Networks Dimitry Krotov 2019
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Beyond Sequence to Sequence with Augmented RNNs Ed Grefenstette 2019
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Neurosymbolic AI David Cox 2020
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Graph exploration Davide Mottin, Emmanuel Müller 2018
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Foundations of Machine Learning Introduction to ML Mehryar Mohri 2018
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Deep Learning Ian Goodfellow 2019
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Graph Neural Networks in NLP Ivan Titov 2020
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Deep Learning for Open-Domain Dialogues Yun-Nung (Vivian) Chen 2017
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Knowledge Graph Inference and Applications Yuxiao Dong and Iris Shen 2019
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Full Stack Deep Learning Setting Up Deep Learning Projects Josh Tobin 2019
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Derivation of Backpropagation 2019
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Learning for Discrete Optimization Yisong Yue 2020
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The Emergence Theory of Representation Learning Stefano Soatto 2019
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Introduction to Dynamic Syntax & the DyLan parser Arash Eshghi and Julian Hough 2019
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Deep RL Boot Camp: SVG and SCG John Schulman 2019
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Deep Learning I Supervised Learning Russ Salakhutdinov 2019
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Fairness-Aware Machine Learning Practical Challenges and Lessons Learned Sarah Bird, et al. 2019
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Machine Learning for Robots Chelsea Finn 2019
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Seven Lectures on Statistical Parsing Christopher Manning 2019
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NLP support for indexing of biomedical literature with Medical Subject Headings Dina Demner-Fushman 2019
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Deep Learning Yann LeCun 2020
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Foundations of Machine Learning Multi-class classification Mehryar Mohri 2018
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Deep Learning for Conversational AI Pei-Hao Su 2019
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Neural Vector Representations beyond Words: Embeddings Gerard de Melo 2019
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More on Transformers: BERT and Friends Jorge Perez 2019
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Introduction and Overview Yuxiao Dong and Iris Shen 2019
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A Survey of Paraphrasing and Textual Entailment Methods Ion Androutsopoulos 2019
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Deep Recontextualized Word Representations Matthew E Peters 2019
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Part 3 Modeling: Inference methods and Constraints Vivek Srikumar 2017
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Integrating Compositional Semantics and Event Semantics Lucas Champollion and Maria Esipova 2018
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Learning Incremental Systems Arash Eshghi 2019
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Information Retrieval Dragomir Radev 2019
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Obliterate Silos with Knowledge Graphs Michael Uschold 2019
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Machine Learning Handbook Predrag Radivojac, Martha White 2019
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Tutorial: Deep Learning for Healthcare: From Disease Detection to Drug Discovery Cao (Danica) Xiao, Jimeng Sun 2019
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Analyses of Deep Learning David Donoho, Vardan Papyan, and Yiqiao Zhong 2019
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Graph Powering and Spectral Robustness Emmanuel Abe 2019
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All of Statistics: A Concise Course in Statistical Inference Larry Wasserman 2019
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Machine Learning for Natural Language Processing Erik Arakelyan, Adam Bittlingmayer 2018
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Machine Learning A Robustness Perspective Aleksander Madry 2019
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Computational Complexity of Natural Languages: A Reasoned Overview Antonio Branco 2019
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Gradient Notes Kevin Clark 2019
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Cross-Lingual Embeddings Winston Wu 2019
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Summary & Looking Forward Yuxiao Dong and Iris Shen 2019
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Inverse Reinforcement Learning Chelsea Finn 2019
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Whirlwind Tour of NLP Tasks Winston Wu 2019
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Multiword Expressions in a Nutshell Day 5 Carlos Ramisch, Agata Savary, and Aline Villavicencio 2018
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Never-Ending Learning Tom Mitchell and Partha Talukdar 2019
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Deep Learning for Robotics Pieter Abbeel 2019
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Limitations and New Frontier Ava Soleimany 2020
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Learning Representations of Large-Scale Networks Jian Tang, Cheng Li, Qiaozhu Mei 2017
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Foundations of Machine Learning Convex Optimization Mehryar Mohri 2018
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Deep Learning for Dialogue Systems Vivian Chen 2019
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2019
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Processing Spoken Language Mari Ostendorf 2020
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Open-Domain Neural Dialogue Systems Yun-Nung (Vivian) Chen 2018
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Knowledge Graph Fundamentals and Construction Yuxiao Dong and Iris Shen 2019
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Full Stack Deep Learning Intro Josh Tobin 2019
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Vector/Matrix Calculus 2019
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Learning for Continuous Optimization Yisong Yue 2020
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When can Deep Networks avoid the curse of dimensionality and other theoretical puzzles Tomaso Poggio 2017
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Advanced NLP with Python Lecture 3 Anna Rogers 2019
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Intro-HLT: Speaker Recognition Najim Dehak 2020
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Nuts and Bolts of Deep RL Research John Schulman 2019
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CS520 Knowledge Graphs Philip Rathle 2020
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Speech Reports Day 5 Pranav Anand and Natasha Korotkova 2019
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Privacy-preserving Data Mining in Industry Krishnaram Kenthapadi, Ilya Mironov, and Abhradeep Thakurta 2018
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How Could Machines Learn Like Animals and Humans? Yann LeCun 2019
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A casual tour of causality David Lopez-Paz 2019
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The Functions of Deep Learning Gilbert Strang 2019
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Frontiers of Natural Language Processing Sebastian Ruder, Herman Kamper 2019
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Spectral Learning on Matrices and Tensors Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar 2019
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Foundations of Machine Learning Maxent Models Mehryar Mohri 2018
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How Much Linguistics is Needed for NLP Ed Grefenstette 2019
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Gaussian Elimination Roth & Srikumar 2019
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CS 384: Ethical and Social Issues in NLP - Where does the data come from? Participants, Data & Labelers in NLP Dan Jurafsky 2020
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Fact Checking Truth Seeking in the Age of Disinformation Preslav Nakov 2019
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Text Classification Algorithms: A Survey Kamran Kowsari 2019
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Introduction to Deep Neural Networks Asifullah Khan 2019
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Part 7: Final Words Vivek Srikumar 2017
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Graph Formalisms for Meaning Representations Sorcha Gilroy and Adam Lopez 2018
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Fundamentals of Dialogue Processing Pragmatics Arash Eshghi and Julian Hough 2019
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Language Models Jordan Boyd-Graber 2019
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Unsupervsied Cross-lingual Representation Learning Sebastian Ruder 2019
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Notes on Backpropagation Peter Sadowski 2019
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Tutorial: Deep Learning for Healthcare: From Disease Detection to Drug Discovery Cao (Danica) Xiao, Jimeng Sun 2019
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Convolutional Neural Networks in View of Sparse Coding Jeremias Sulam, Yaniv Romano, and Michael Elad 2017
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Research Fast and Slow Min-Yen Kan 2019
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Linguistic Fundamentals for Natural Language Processing II Emily M. Bender, Alex Lascarides 2019
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Deep Learning for Natural Language Processing Erik Arakelyan 2018
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Foundations of Machine Learning Reinforcement Learning Mehryar Mohri 2018
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Neural Text Generation: A Practical Guide Ziang Xie 2019
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Neural Vector Representations beyond Words: Applications Gerard de Melo 2019
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Deep Learning Approaches to Text Production Shashi Narayan, Claire Gardent 2020
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Knowledge Graph Inference and Applications Yuxiao Dong and Iris Shen 2019
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Deep Reinforcement Learning Pieter Abbeel 2019
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Transfer Learning in Natural Language Processing Thomas Wolf 2019
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Multiword Expressions in a Nutshell Day 3 Carlos Ramisch, Agata Savary, and Aline Villavicencio 2018
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Multiword Expressions in a Nutshell Day 4 Carlos Ramisch, Agata Savary, and Aline Villavicencio 2018
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Research at the Speech Processing Group Agustín Gravano 2019
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Deep Learning Based Chatbot Models Richard Csaky 2019
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Deep Reinforcement Learning Alexander Amini 2020
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Semi-supervised Methods for Graph Representation Bryan Perozzi 2018
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Foundations of Machine Learning Boosting Mehryar Mohri 2018
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Algorithms Robert Sedgewick 2019
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Linear Classifiers Andre Martins 2020
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Open-Domain Neural Dialogue Systems Yun-Nung (Vivian) Chen 2017
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Graph Representation Learning Yuxiao Dong and Iris Shen 2019
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Full Stack Deep Learning Testing and Deployment Josh Tobin 2019
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Modeling Roth & Srikumar 2019
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Algorithm Configuration Yisong Yue 2020
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Deep Learning Challenge Is there Theory? David Donoho 2017
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Distributivity Debates, advances, questions Lelia Glass 2019
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Advanced Policy Gradient Methods John Schulman 2019
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Probability Elad Hazan 2003
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Speech Reports Day 4 Pranav Anand and Natasha Korotkova 2019
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Knowledge Extraction and Inference from Text Part 2 Partha Talukdar 2018
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Imitation Learning Yisong Yue 2019
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Integrating Doman-Knowledge into Deep Learning Russ Salakhutdinov 2019
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All of Statistics: A Concise Course in Statistical Inference Larry Wasserman 2019
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Large-Scale Graph Algorithmics: Theory and Practice Silvio Lattanzi, Vahab Mirrokni 2018
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Foundations of Machine Learning Learning guarantees for infinite hypothesis sets Mehryar Mohri 2018
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Computational Complexity in Natural Language Ian Pratt-Hartman 2019
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Linear Algebra Done Right Sheldon Axler 2015
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Neural Tangent Kernel Convergence and Generalization of DNNs Arthur Jacot 2019
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Full Stack Deep Learning Data Management Josh Tobin 2019
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Deep Learning: State of the Art (2019) 2019
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Part 2 Applications of ILP Formulations in Natural Language Processing Vivek Srikumar 2017
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Data Collection and End-to-End Learning for Conversational AI Pawel Budzianowki, Inigo Casanueva, and Ivan Vulic 2019
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Incremental Disfluency Processing for Dialogue Systems Arash Eshghi and Julian Hough 2019
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CS294-158 Deep Unsupervised Learning: Latent Variable Models Pieter Abbeel 2019
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Deep Learning IV Model Evaluation and Open Questions Russ Salakhutdinov 2019
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AI in the Department of Computer Science - UCSP José Eduardo Ochoa Luna 2019
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Basics of Machine Learning Martha White 2019
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Understanding and Improving Deep Learning with Random Matrix Theory Jeffrey Pennington 2017
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What's Missing From Deep Learning? Bruce Olshausen 2017
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Foundations: How to design experiments in NLU Sam Bowman 2019
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Neural Networks from Scratch Ankit Gupta 2019
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Deep Learning for Search Bhaskar Mitra 2019
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Foundations of Machine Learning Regression Mehryar Mohri 2018
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Transfer Learning Irene Li 2019
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Neural Vector Representations beyond Words: Documents Gerard de Melo 2019
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Defining and evaluating diversity in generation Tatsunori Hashimoto 2019
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Knowledge Graph Fundamentals and Construction Yuxiao Dong and Iris Shen 2019
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CS442/542b: Artificial Intelligence II Olga Veksler 2019
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Part 5: Constraints Driven Learning Vivek Srikumar 2017
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Multiword Expressions in a Nutshell Day 2 Carlos Ramisch, Agata Savary, and Aline Villavicencio 2018
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Fact Checking Tutorial and Practice Xin Luna Dong, Christos Faloutsos, Xian Li, Subhabrata Mukherjee, Prashant Shiralkar 2018
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Pong From Pixels Andrej Karpathy 2019
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Reinforcement Learning and Optimal Control - Approximation in Value Space Dimitri P. Bertsekas 2019
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Modeling Data With Networks + Network Embedding: Problems, Methodologies and Frontiers Ivan Brugere, Peng Cui, Bryan Perozzi, Wenwu Zhu, Tanya Berger-Wolf, Jian Pei 2018
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Mean Field Theory and Tangent Kernel Theory of Neural Networks Song Mei 2019
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The Handbook of Computational Linguistics and Natural Language Processing Alexander Clark 2019
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Mathematical Tools: Probability Theory, Algebra Mario A. T. Figueiredo 2019
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Analyzing Optimization and Generalization in Deep Learning via Trajectories of Gradient Descent Nadav Cohen 2019
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Graph Properties and Applications Yuxiao Dong and Iris Shen 2019
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Full Stack Deep Learning Troubleshooting Deep Neural Networks Pieter Abbeel 2019
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Spectral Graph Theory Normalized Adjacency and Laplacian Matrices 2019
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Bayesian Optimization Yisong Yue 2020
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Topology and Geometry of Half-Rectified Network Optimization Joan Bruna 2017
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Hybrid Logic Lecture 1 Patrick Blackburn 2018
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Pong From Pixels Andrej Karpathy 2019
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Algebra, Topology, Differential Calculus and Optimization Theory For Computer Science and Machine Learning Jean Gallier, Jocelyn Quaintance 2020
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Speech Reports Day 3 Pranav Anand and Natasha Korotkova 2019
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Knowledge Extraction and Inference from Text Part 1 Partha Talukdar 2018
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Practical Neural Networks for NLP Chris Dyer 2019
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Reasoning, Attention, and Memory Sumit Chopra 2019
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Part of Speech Tagging Colin Cherry 2019
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Building a Large-scale, Accurate and Fresh Knowledge Graph Yuqing Gao, Jisheng Liang, Benjamin Han, Mohamed Yakout, Ahmed Mohamed 2018
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Foundations of Machine Learning Learning guarantees for finite hypothesis sets Mehryar Mohri 2018
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Graph Algorithms Mark Needham 2019
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Optimization for Machine Learning John Duchi 2019
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Machine Learning with Kernel Methods Julien Mairal, Jean-Philippe Vert 2020
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NSF Proposal Checklist and Outline N/A 2019
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Full Stack Deep Learning Infrastructure and Tooling Josh Tobin 2019
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A Very Short Introduction to Combinatory Categorical Grammar Mark Steedman 2019
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Advanced Bayesian Optimization Settings Yisong Yue 2020
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Transferring NLP models across languages and domains Barbara Plank 2019
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Incremental Dialogue Systems Arash Eshghi and Julian Hough 2019
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CS294-158 Deep Unsupervised Learning: Autoregressive Models Pieter Abbeel 2019
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Deep Learning III Unsupervised Learning Russ Salakhutdinov 2019
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Reinforcement Learning Nando de Freitas 2019
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Theories of Deep Learning David Donoho, Hatef Monajemi, and Vardan Papyan 2017
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NetworkX: Network Analysis with Python Petko Georgiev 2019
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Defining and evaluating diversity in generation Tatsunori Hashimoto 2019
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From System 1 Deep Learning to System 2 Deep Learning Yoshua Bengio 2019
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Foundations of Machine Learning Ranking Mehryar Mohri 2018
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Neural Text Generation: A Practical Guide Ziang Xie 2019
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Neural Vector Representations beyond Words: Sentences Gerard de Melo 2019
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Transfer Learning in NLP INRIA - ALMAnaCH Thomas Wolf 2020
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Graph Representation Learning Yuxiao Dong and Iris Shen 2019
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Mathematics of Deep Learning Lecture 11 Joan Bruna 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding Jacob Devlin 2019
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Part 4: Learning Paradigms Vivek Srikumar 2017
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Multiword Expressions in a Nutshell Day 1 Carlos Ramisch, Agata Savary, and Aline Villavicencio 2018
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140 Machine Learning Formulas Rubens Zimbres 2017
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Deep Recontextualized Word Representations Matthew E Peters 2019
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Neural Networks Vivek Srikumar 2019
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Reinforcement Learning and Optimal Control - Exact Dynamic Programming Dimitri P. Bertsekas 2019
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Inferring Graphs From Data Ivan Brugere 2018
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Views of Deep Networks from Reproducing Kernel Hilbert Spaces Zaid Harchaoui 2017
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A Selective Overview of Deep Learning Jianqing Fan 2019
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Explainability for NLP Isabelle Augestein 2019
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Automatic Text Summarization: New Approaches and Applications Juan-Manuel Torres-Moreno 2014
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Introduction and Overview Yuxiao Dong and Iris Shen 2019
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What It Takes to Control Societal Bias in Natural Language Processing Kai-Wei Chang 2019
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Math Background for ML Geoff Gordon 2019
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Data-Driven Algorithm Design Yisong Yue 2020
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