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John Urschel : John Cameron Urschel (born June 24, 1991) is a Canadian mathematician and former professional football guard. He played college football at Penn State and was drafted by the Baltimore Ravens in the fifth round of the 2014 NFL draft. Urschel played his entire NFL career with Baltimore before announcing hi... |
John Urschel : Urschel was born in Winnipeg, Manitoba, Canada. His parents, John Urschel and Venita Parker, were a surgeon and attorney, respectively. He grew up in Buffalo, New York where he graduated from Canisius High School in 2009. At Pennsylvania State University, Urschel earned both a bachelor's degree in 2012 a... |
John Urschel : Urschel was selected by the Baltimore Ravens in the fifth round of the 2014 NFL draft. He played in 11 games, starting three, for the Ravens in 2014. He appeared in 16 games, starting seven, for the team in 2015. He played in 13 games, starting three, his final season in 2016. On July 27, 2017, Urschel a... |
John Urschel : While doing his master's at Penn State, Urschel was involved in teaching vector calculus, trigonometry and analytic geometry, and introduction to econometrics. In 2014, Urschel was named Arthur Ashe, Jr. Sports Scholar by Diverse: Issues In Higher Education. In 2015, Urschel co-authored a paper in the Jo... |
John Urschel : Urschel competed in the 2015 Pittsburgh Open, finishing in 12th place (tied for 9th) with 3.0 points (+2-1=2) in the Under 1700 rating section. Urschel competes in competitive online chess on Chess.com, and he has commentated for Chess.com's BlitzChamps event, a rapid tournament for NFL players. |
John Urschel : Urschel is married to writer Louisa Thomas, whom he met when she was profiling him for Grantland. They have two children. Urschel's autobiography, Mind and Matter: A Life in Math and Football, was co-written by Thomas and published in 2019. |
John Urschel : Frank Ryan – former NFL player and mathematician, who maintained an academic career while playing in the league |
John Urschel : John Urschel on Twitter John Urschel's MIT mathematics website John Urschel publications indexed by Google Scholar Penn State biography |
Mihaela van der Schaar : Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, AI, and Medicine at the University of Cambridge, where she is director of the Cambridge Centre for AI in Medicine (CCAIM), and a Chancellor's Professor of Electrical and Computer Engineering at the University of ... |
Mihaela van der Schaar : Van der Schaar received a joint Bachelor's/Master's (1996) degree and a Doctoral (2001) degree from the Eindhoven University of Technology in the Netherlands. Upon starting her studies she was the only woman in a class of over 200. She completed her PhD while simultaneously working as a researc... |
Mihaela van der Schaar : van der Schaar was elected as a Fellow of the IEEE in 2009, and she has held a fellowship with the Alan Turing Institute since 2016. She has also received a National Science Foundation CAREER Award (2004), the IEEE Darlington Award (2011), and the Oon Prize on Preventative Medicine from the Uni... |
Vladimir Vapnik : Vladimir Naumovich Vapnik (Russian: Владимир Наумович Вапник; born 6 December 1936) is a statistician, researcher, and academic. He is one of the main developers of the Vapnik–Chervonenkis theory of statistical learning and the co-inventor of the support-vector machine method and support-vector cluste... |
Vladimir Vapnik : Vladimir Vapnik was born to a Jewish family in the Soviet Union. He received his master's degree in mathematics from the Uzbek State University, Samarkand, Uzbek SSR in 1958 and Ph.D in statistics at the Institute of Control Sciences, Moscow in 1964. He worked at this institute from 1961 to 1990 and b... |
Vladimir Vapnik : At the end of 1990, Vladimir Vapnik moved to the USA and joined the Adaptive Systems Research Department at AT&T Bell Labs in Holmdel, New Jersey. While at AT&T, Vapnik and his colleagues did work on the support-vector machine (SVM), which he also worked on much earlier before moving to the USA. They ... |
Vladimir Vapnik : Vladimir Vapnik was inducted into the U.S. National Academy of Engineering in 2006. He received the 2005 Gabor Award from the International Neural Network Society, the 2008 Paris Kanellakis Award, the 2010 Neural Networks Pioneer Award, the 2012 IEEE Frank Rosenblatt Award, the 2012 Benjamin Franklin ... |
Vladimir Vapnik : On the uniform convergence of relative frequencies of events to their probabilities, co-author A. Y. Chervonenkis, 1971 Necessary and sufficient conditions for the uniform convergence of means to their expectations, co-author A. Y. Chervonenkis, 1981 Estimation of Dependences Based on Empirical Data, ... |
Vladimir Vapnik : Alexey Chervonenkis |
Vladimir Vapnik : Photograph of Professor Vapnik Vapnik's brief biography from the Computer Learning Research Centre, Royal Holloway Interview by Lex Fridman |
Guillaume Verdon : Guillaume Verdon-Akzam, also known as Guillaume Verdon, or Gill Verdon is a Canadian mathematical physicist, quantum computing researcher, serial entrepreneur, and writer who is a key contributor of Google's quantum machine learning software, Tensorflow Quantum. He is also a co-founder of the effecti... |
Guillaume Verdon : Verdon attended McGill University as an undergraduate and graduated with honors with a double major in Mathematics & Physics. He attended University of Waterloo for graduate studies where he completed Master's work in 2017 at the Institute for Quantum Computing and continued with Achim Kempf as his P... |
Guillaume Verdon : Verdon wrote under the pseudonym BasedBeffJezos and was a co-founders of the effective accelerationism (e/acc) movement. The origin of the movement can be traced back to a May 2022 newsletter published by him and 3 other authors. In its coverage of the movement Forbes outed Verdon as the author behin... |
Guillaume Verdon : Lex Fridman Podcast #407 – Guillaume Verdon: Beff Jezos, E/acc Movement, Physics, Computation & AGI Extropic AI - Official Website Google Scholar Profile |
Oriol Vinyals : Oriol Vinyals (born 1983) is a Spanish machine learning researcher at DeepMind. He is currently technical lead on Gemini, along with Noam Shazeer and Jeff Dean. |
Oriol Vinyals : Vinyals was born in Barcelona, Catalonia, Spain. He studied mathematics and telecommunication engineering at the Universitat Politècnica de Catalunya. He then moved to the US and studied for a Master's degree in computer science at University of California, San Diego, and at University of California, Be... |
Oriol Vinyals : Ilya Sutskever == References == |
Paul Viola : Paul Viola is a computer vision researcher, and Distinguished Engineer at Zoox. He is a former MIT professor, a former vice president of science for Amazon Prime Air and a former Distinguished Engineer at Microsoft. He is best known for his seminal work in facial recognition and machine learning. He is the... |
Paul Viola : Detecting Faces (Viola Jones Algorithm) – Computerphile |
Vladimir Vovk : Vladimir Vovk is a British computer scientist, and professor at Royal Holloway University of London. He is the co-inventor of Conformal prediction and is known for his contributions to the concept of E-values. He is the co-director of the Centre for Machine Learning at Royal Holloway University of Londo... |
Vladimir Vovk : Vovk started working as a researcher in the Russian Academy of Sciences, then became a Fellow in the Center for Advanced Study in the Behavioral Sciences at Stanford University. He was appointed as a professor of Computer Science at Royal Holloway and Bedford New College, where he currently serves as co... |
Vladimir Vovk : Game-theoretic foundations for probability and finance (2019), Wiley, ISBN 0470903058. Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications (2014), Morgan Kaufmann, ISBN 0123985374. Algorithmic Learning in a Random World (2005), Springer, ISBN 0387001522. Probability ... |
Vladimir Vovk : Vovk's Personal Website Vovk's University Website |
Grace Wahba : Grace Goldsmith Wahba (born August 3, 1934) is an American statistician and retired I. J. Schoenberg-Hilldale Professor of Statistics at the University of Wisconsin–Madison. She is a pioneer in methods for smoothing noisy data. Best known for the development of generalized cross-validation and "Wahba's pr... |
Grace Wahba : Wahba had an interest in science from an early age, when she was in junior high she was given a chemistry set. At this time she was also interested in becoming an engineer. Wahba studied at Cornell University for her undergraduate degree; in 1952, Cornell and Brown University were the only Ivy League univ... |
Grace Wahba : Wahba was elected to the American Academy of Arts and Sciences in 1997 and to the National Academy of Sciences in 2000. She is also a fellow of several academic societies including the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathemati... |
Grace Wahba : Grace Wahba at the Mathematics Genealogy Project Grace Wahba's University of Wisconsin website Home page |
Sumio Watanabe : Sumio Watanabe (渡辺 澄夫, Watanabe Sumio, born 1959) is a Japanese mathematician and engineer working in probability theory, applied algebraic geometry and Bayesian statistics. He is currently a professor at Tokyo Institute of Technology in the Department of Computational Intelligence and Systems Science.... |
Sumio Watanabe : Mathematical Theory of Bayesian Statistics, CRC Press, 2018, ISBN 9781482238068 Algebraic Geometry and Statistical Learning Theory, Cambridge University Press, 2009. |
Sumio Watanabe : Algebraic Geometrical Method in Singular Statistical Estimation. Presentation at Algebraic Statistics Seminar, MSRI, December 17, 2008 (video) |
Max Welling : Max Welling (born 1968) is a Dutch computer scientist in machine learning at the University of Amsterdam. In August 2017, the university spin-off Scyfer BV, co-founded by Welling, was acquired by Qualcomm. He has since then served as a Vice President of Technology at Qualcomm Netherlands. He is also a Dis... |
Halbert White : Halbert Lynn White Jr. (November 19, 1950 – March 31, 2012) was the Chancellor’s Associates Distinguished Professor of Economics at the University of California, San Diego, and a Fellow of the Econometric Society and the American Academy of Arts and Sciences. |
Halbert White : White, a native of Kansas City, Missouri, graduated salutatorian from Southwest High School in 1968. He went on to study at Princeton University, receiving his BA in economics in 1972. He earned his PhD in economics at the Massachusetts Institute of Technology in 1976, under the supervision of Jerry A. ... |
Halbert White : White was well known in the field of econometrics for his 1980 paper on robust standard errors (which is among the most-cited paper in economics since 1970), and for the heteroscedasticity-consistent estimator and the test for heteroskedasticity that are named after him. A 1982 paper by White contribute... |
Halbert White : Faculty profile at the University of California, San Diego's website Halbert Lynn White Jr. at the Mathematics Genealogy Project Robbins, Gary (October 9–10, 2011). "UCSD doesn't get Nobel Prize in economics". U-T San Diego. Retrieved March 5, 2019. |
Christopher K. I. Williams : Christopher Kenneth Ingle Williams (born 1960) is a professor at the School of Informatics, University of Edinburgh, working in Artificial intelligence, and particularly the areas of Machine learning and Computer vision. |
Christopher K. I. Williams : Williams received a BA in Physics and Theoretical Physics from the University of Cambridge in 1982, followed by Part III Mathematics (1983). He did a MSc in Water Resources at the University of Newcastle-Upon-Tyne, then worked in Lesotho on low-cost sanitation. In 1988, he studied at the De... |
Christopher K. I. Williams : In 1994, Williams moved to Aston University as a Research Fellow. He became a Lecturer in August 1995. He moved to the University of Edinburgh in July 1998 and became Reader in 2000. He obtained a Personal Chair in Machine Learning in 2005 in the School of Informatics. Williams has been a F... |
Christopher K. I. Williams : In 2021 Williams was elected a Fellow of the Royal Society of Edinburgh (FRSE). |
Stephen Wolfram : Stephen Wolfram ( WUUL-frəm; born 29 August 1959) is a British-American computer scientist, physicist, and businessman. He is known for his work in computer algebra and theoretical physics. In 2012, he was named a fellow of the American Mathematical Society. As a businessman, he is the founder and CEO... |
Stephen Wolfram : Wolfram, at the age of 15, began research in applied quantum field theory and particle physics and published scientific papers in peer-reviewed scientific journals; by the time he left Oxford, he had published ten such papers. Following his PhD, Wolfram joined the faculty at Caltech and became the you... |
Stephen Wolfram : Wolfram has a log of personal analytics, including emails received and sent, keystrokes made, meetings and events attended, recordings of phone calls, and even physical movement dating back to the 1980s. In the preface of A New Kind of Science, he noted that he recorded over 100 million keystrokes and... |
Stephen Wolfram : Metamathematics: Foundations & Physicalization, (2022), Wolfram Media, Inc, ASIN:B0BPN7SHN3 Combinators: A Centennial View (2021) A Project to Find the Fundamental Theory of Physics (2020), Publisher: Wolfram Media, ISBN 978-1-57955-035-6 Adventures of a Computational Explorer (2019) Idea Makers: Pers... |
Stephen Wolfram : Official website Wolfram Foundation Stephen Wolfram at the Mathematics Genealogy Project Stephen Wolfram at IMDb Stephen Wolfram at TED Stephen Wolfram on Charlie Rose Works by Stephen Wolfram at Open Library Interview of Stephen Wolfram by David Zierler on March 18 and April 17, 2021, Niels Bohr Libr... |
Eric Xing : Eric Poe Xing is an American computer scientist whose research spans machine learning, computational biology, and statistical methodology. Xing is founding President of the world’s first artificial intelligence university, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) and a Co-Founder and... |
Eric Xing : Xing received a B.Sc. in physics at Tsinghua University in 1993, and a Ph.D. in molecular biology at Rutgers University in 1999 and a Ph.D. in computer science at the University of California, Berkeley in 2004. Xing became a faculty member at Carnegie Mellon University in 2004, directing the SAILING Lab, wh... |
Eric Xing : Xing is a recipient of the National Science Foundation (NSF) Career Award and the Alfred P. Sloan Research Fellowship. In 2016, he was elected a Fellow of the Association of Advancement of Artificial Intelligence (AAAI). In 2019, he was elected a Fellow of the Institute of Electrical and Electronics Enginee... |
Eric Xing : Probabilistic graphical model |
Eric Xing : Eric Xing (at Carnegie Mellon University ) |
Xu Li (computer scientist) : Xu Li is a Chinese computer scientist and co-founder and current CEO of SenseTime, an artificial intelligence (AI) company. Xu has led SenseTime since the company's incorporation and helped it independently develop its proprietary deep learning platform. |
Xu Li (computer scientist) : Xu obtained both his bachelor's and master's degrees in computer science from Shanghai Jiao Tong University. He received his doctorate in computer science from the Chinese University of Hong Kong. Xu has published more than 50 papers at international conferences and in journals in the field... |
Xu Li (computer scientist) : Previously, Xu worked at Lenovo Corporate Research & Development. He was also a visiting researcher at Motorola China R&D Institute, Omron Research Institute, and Microsoft Research. |
Xu Li (computer scientist) : Jimmy Ren, Xiaohao Chen, Jianbo Liu, Wenxiu Sun, Li Xu, Jiahao Pang, Qiong Yan, Yu-wing Tai, "Accurate Single Stage Detector Using Recurrent Rolling Convolution", (CVPR), 2017. Jimmy SJ. Ren, Yongtao Hu, Yu-Wing Tai, Chuan Wang, Li Xu, Wenxiu Sun, Qiong Yan, "Look, Listen and Learn – A Mult... |
Xu Li (computer scientist) : Xu was ranked 7th in Fortune magazine's 2018 edition of its 40 Under 40. He was also named "China's Outstanding AI Industry Leader" by The Economic Observer, received the "Innovative Business Leader" Award under NetEase's "Future Technology Talent Awards", and was honored as Sina's "2017 To... |
Xu Li (computer scientist) : Homepage of Dr. Xu Li SenseTime |
Yi Zeng (AI researcher) : Yi Zeng (Chinese: 曾毅) is a Chinese artificial intelligence researcher and professor at the Chinese Academy of Sciences, who also serves as the founding director of Center for Long-term AI, and as a member of the United Nations Advisory Body on AI. |
Yi Zeng (AI researcher) : On May 25, 2019, Zeng led the team that published the Beijing Artificial Intelligence Principles, proposed as an initiative for the long-term research, governance and planning of AI, and the "realization of beneficial AI for mankind and nature". He was named on the Time 100 AI list, a list fea... |
Bruno Zamborlin : Bruno Zamborlin (born 1983 in Vicenza) is an AI researcher, entrepreneur and artist based in London, working in the field of human-computer interaction. His work focuses on converting physical objects into touch-sensitive, interactive surfaces using vibration sensors and artificial intelligence. In 20... |
Bruno Zamborlin : From 2008-2011, Zamborlin worked at the IRCAM (Institute for Research and Coordination Acoustic Musical) – Centre Pompidou as a member of the Sound Music Movement Interaction team. Under the supervision of Frederic Bevilacqua, he started experimenting with the use of artificial intelligence and human ... |
Bruno Zamborlin : Zamborlin founded Mogees Limited in 2013 in London, with IRCAM being amongst the early partners. Mogees transform physical objects into musical instruments and games using a vibration sensor and a series of apps for smartphones and desktop. After a campaign on Kickstarter in 2014, Mogees was used both... |
Bruno Zamborlin : IRISA Prix Jeune Chercheur, 13 October 2012 NeMoDe, New Economic Models in the Digital Economy, 25 October 2012 |
Bruno Zamborlin : United States pending US10817798B2, Bruno Zamborlin & Carmine Emanuele Cella, "Method to recognize a gesture and corresponding device", published 2016-04-27, assigned to Mogees Limited GB Pending WO/2019/086862, Bruno Zamborlin; Conor Barry & Alessandro Saccoia et al., "A user interface for vehicles",... |
Bruno Zamborlin : Mogees official website HyperSurfaces official website |
Richard Zemel : Richard Stanley Zemel (born 1963) is a Canadian-American computer scientist and professor at Columbia University, Department of Computer Science, and a leading figure in the field of machine learning and computer vision. Zemel studied the history of science at Harvard University and obtained his B.A. in... |
Richard Zemel : Helmholtz machine |
Richard Zemel : Richard Zemel publications indexed by Google Scholar |
Arthur Zimek : Arthur Zimek is a professor in data mining, data science and machine learning at the University of Southern Denmark in Odense, Denmark. He graduated from the Ludwig Maximilian University of Munich in Munich, Germany, where he worked with Prof. Hans-Peter Kriegel. His dissertation on "Correlation Clusteri... |
Arthur Zimek : University homepage Publications in the Digital Bibliography & Library Project Google Scholar profile |
Jacek M. Zurada : Jacek M. Zurada is a Polish-American computer scientist who serves as a Professor of Electrical and Computer Engineering Department at the University of Louisville, Kentucky. His M.S. and Ph.D. degrees are from Politechnika Gdaṅska (Gdansk University of Technology, Poland) ranked as #1 among Polish un... |
Jacek M. Zurada : Dr. Zurada research contributions cover neural networks, deep learning, data mining with emphasis on data and feature understanding, rule extraction from semantic and visual information, machine learning, decomposition methods for salient feature extraction, and lambda learning rule for neural network... |
Jacek M. Zurada : Dr. Zurada has served the engineering profession as a long-time volunteer of IEEE: as 2014 IEEE Vice-President-Technical Activities (TAB Chair), as President of IEEE Computational Intelligence Society in 2004–05 and the ADCOM member in 2009–14, 2016–21 and earlier years. He chaired the IEEE TAB Strate... |
Jacek M. Zurada : He has received a number of awards for distinction in research, teaching, and service including the 1993 UofL's Presidential Award for Research, Scholarship and Creative Activity, 1999 IEEE Circuits and Systems Society Golden Jubilee Medal, and the 2001 and 2014 UofL's Presidential Distinguished Servi... |
Jacek M. Zurada : 2023 Membership in European Academy of Sciences and Arts, elected as a Member 2023 Plaque of Special Appreciation, awarded by IEEE Hyderabad, India Section, for remarkable partnership in publishing and presentation 2023 Memorial Medal, awarded by IEEE Poland Section and Poznan University of Technology... |
Jacek M. Zurada : Home page of Dr. Jacek M. Zurada. Accessed June 14, 2008. |
Reinforcement learning : Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms... |
Reinforcement learning : Due to its generality, reinforcement learning is studied in many disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, and statistics. In the operations research and control literature, ... |
Reinforcement learning : The exploration vs. exploitation trade-off has been most thoroughly studied through the multi-armed bandit problem and for finite state space Markov decision processes in Burnetas and Katehakis (1997). Reinforcement learning requires clever exploration mechanisms; randomly selecting actions, wi... |
Reinforcement learning : Even if the issue of exploration is disregarded and even if the state was observable (assumed hereafter), the problem remains to use past experience to find out which actions lead to higher cumulative rewards. |
Reinforcement learning : Both the asymptotic and finite-sample behaviors of most algorithms are well understood. Algorithms with provably good online performance (addressing the exploration issue) are known. Efficient exploration of Markov decision processes is given in Burnetas and Katehakis (1997). Finite-time perfor... |
Reinforcement learning : Research topics include: actor-critic architecture actor-critic-scenery architecture adaptive methods that work with fewer (or no) parameters under a large number of conditions bug detection in software projects continuous learning combinations with logic-based frameworks exploration in large M... |
Reinforcement learning : The following table lists the key algorithms for learning a policy depending on several criteria: The algorithm can be on-policy (it performs policy updates using trajectories sampled via the current policy) or off-policy. The action space may be discrete (e.g. the action space could be "going ... |
Reinforcement learning : Efficient comparison of RL algorithms is essential for research, deployment and monitoring of RL systems. To compare different algorithms on a given environment, an agent can be trained for each algorithm. Since the performance is sensitive to implementation details, all algorithms should be im... |
Reinforcement learning : Annaswamy, Anuradha M. (3 May 2023). "Adaptive Control and Intersections with Reinforcement Learning". Annual Review of Control, Robotics, and Autonomous Systems. 6 (1): 65–93. doi:10.1146/annurev-control-062922-090153. ISSN 2573-5144. S2CID 255702873. Auer, Peter; Jaksch, Thomas; Ortner, Ronal... |
Reinforcement learning : Dissecting Reinforcement Learning Series of blog post on reinforcement learning with Python code A (Long) Peek into Reinforcement Learning |
Distributional Soft Actor Critic : Distributional Soft Actor Critic (DSAC) is a suite of model-free off-policy reinforcement learning algorithms, tailored for learning decision-making or control policies in complex systems with continuous action spaces. Distinct from traditional methods that focus solely on expected re... |
Model-free (reinforcement learning) : In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the transition probability distribution (and the reward function) associated with the Markov decision process (MDP), which, in RL, represents the problem to be solved. The transition prob... |
Model-free (reinforcement learning) : Model-free RL algorithms can start from a blank policy candidate and achieve superhuman performance in many complex tasks, including Atari games, StarCraft and Go. Deep neural networks are responsible for recent artificial intelligence breakthroughs, and they can be combined with R... |
Policy gradient method : Policy gradient methods are a class of reinforcement learning algorithms. Policy gradient methods are a sub-class of policy optimization methods. Unlike value-based methods which learn a value function to derive a policy, policy optimization methods directly learn a policy function π that sele... |
Policy gradient method : In policy-based RL, the actor is a parameterized policy function π θ , where θ are the parameters of the actor. The actor takes as argument the state of the environment s and produces a probability distribution π θ ( ⋅ ∣ s ) (\cdot \mid s) . If the action space is discrete, then ∑ a π θ ( a ... |
Policy gradient method : REINFORCE is an on-policy algorithm, meaning that the trajectories used for the update must be sampled from the current policy π θ . This can lead to high variance in the updates, as the returns R ( τ ) can vary significantly between trajectories. Many variants of REINFORCE has been introduce... |
Policy gradient method : The natural policy gradient method is a variant of the policy gradient method, proposed by Sham Kakade in 2001. Unlike standard policy gradient methods, which depend on the choice of parameters θ (making updates coordinate-dependent), the natural policy gradient aims to provide a coordinate-fr... |
Policy gradient method : Trust Region Policy Optimization (TRPO) is a policy gradient method that extends the natural policy gradient approach by enforcing a trust region constraint on policy updates. Developed by Schulman et al. in 2015, TRPO ensures stable policy improvements by limiting the KL divergence between suc... |
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