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This article was written in collaboration with Bohan Zhang and originally appeared on the OtterTune website. There are a lot of choices in databases (897 as of April 2023). With so many systems, it’s hard to know what to pick! But there is an interesting phenomenon where the Internet collectively decides on the default choice for new applications. In the 2000s, the conventional wisdom selected MyS
www.cs.cmu.edu/~pix2pix3D
We propose a 3D-aware conditional generative model for controllable photorealistic image synthesis. Given a 2D label map, such as a segmentation or edge map, our model synthesizes a photo from different viewpoints. Existing approaches fail to either synthesize images based on a conditional input or suffer from noticeable viewpoint inconsistency. Moreover, many of them lack explicit user control of
Another year has gone by, and I’m still alive. As such, it is an excellent time to reflect on what happened in the world of databases last year. It was quiet in the streets as the benchmark wars between DBMS vendors have quieted down. I had fun writing last year’s retrospective, so I am excited to share with you the things that stand out from 2022 and my thoughts on them. Big Database Funding Has
Databases in 2023: A Year in Review Posted on January 04, 2024 Andy recounts the rise of vector databases to SQL:2023 to MariaDB troubles and the FAA outage in 2023. [READ] Yes, PostgreSQL Has Problems. But We’re Sticking With It! Posted on June 07, 2023 Andy explores ways to optimize PostgreSQL for each of the problems caused by the implementation of multi-version concurrency control in PostgreSQ
It was a wild year for the database industry, with newcomers overtaking the old guard, vendors fighting over benchmark numbers, and eye-popping funding rounds. We also had to say goodbye to some of our database friends through acquisitions, bankruptcies, or retractions. As the end of the year draws near, it’s worth reflecting and taking stock as we move into 2022. Here are some of the highlights a
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A Behavioral Notion of Subtyping BARBARAH. LISKOV MIT Laboratory for Computer Science and JEAN NETTE M. WING Carnegie Mellon University The use of hierarchy is unimportant component of object-oriented design. Hierarchy allows the use oft ype families, in which higher level supert ypes capt ure the behavior that all of their subt ypes havein common. Forthis methodology to beeffective, itisnecessary
So I ended up doing what I promised myself I would not do. And that is be a professor who has a blog that they never update. I know that it's been a year since my last post and that I still need to write part 3 in my series on the open research problems for transaction processing database systems. A lot of has happened in the last year at CMU and I plan to discuss them here once the projects are m
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I am a research scientist at DeepMind. Before that, I was the Laplace Postdoctoral Chair in Data Science at École normale supérieure in Paris, where I worked with Emmanuel Dupoux and the CoML team. Before that, I was a PhD student at Carnegie Mellon University in Graham Neubig's NeuLab. In the past, I have also interned at Facebook and DeepMind. You can find my full CV here and the list of my publ
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Common Lisp the Language, 2nd Edition Next: Introduction Up: Common Lisp the Language Previous: Identity Function By Jon L White PREFACE: X3J13 voted in January 1989 (LOOP-FACILITY) to adopt an extended definition of the loop macro as a part of the forthcoming draft Common Lisp standard. This chapter presents the bulk of the Common Lisp Loop Facility proposal, written by Jon L White. I have edited
The School of Computer Science will launch a bachelor of science program in artificial intelligence this fall.Carnegie Mellon University's School of Computer Science will offer a new undergraduate degree in artificial intelligence beginning this fall, providing students with in-depth knowledge of how to transform large amounts of data into actionable decisions. SCS has created the new AI degree, t
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People of Programming Languages An interview project in conjunction with POPL 2018 Interview with Simon Peyton-Jones Simon Peyton-Jones (Microsoft Research Cambridge) researches the implementations and applications of functional programming languages. He was heavily involved in the design of the Haskell programming language and the development of the Glasgow Haskell Compiler (GHC). We talk about s
People of Programming Languages An interview project in conjunction with POPL 2018 Simon Peyton-Jones Simon Peyton-Jones (Microsoft Research Cambridge), one of the key people behind the development of Haskell, talks about seeing functional programming go from intellectual revolution to practical reality and the importance of investing in computing education. Hongseok Yang Hongseok Yang is a Profes
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Implementing Malloc: Students and Systems Programming Brian P. Railing Carnegie Mellon University Pittsburgh, PA bpr@cs.cmu.edu Randal E. Bryant Carnegie Mellon University Pittsburgh, PA randy.bryant@cs.cmu.edu ABSTRACT This work describes our experience in revising one of the major programming assignments for the second-year course Introduction to Computer Systems, in which students implement a v
People of Programming Languages An interview project in conjunction with POPL 2018 Interview with Xavier Leroy Xavier Leroy is a senior scientist at INRIA interested in the scientific aspects of programming. He leads the Gallium team on the design, formalization, and implementation of programming languages and systems. He is well known for being the primary developer of the OCaml programming langu
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A Library of Parallel Algorithms This is the toplevel page for accessing code for a collection of parallel algorithms. The algorithms are implemented in the parallel programming language NESL and developed by the Scandal project. For each algorithm we give a brief description along with its complexity (in terms of asymptotic work and parallel depth). In many cases the NESL code is set up so you ca
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Kernel Methods Barnabás Póczos 2 Outline • Quick Introduction • Feature space • Perceptron in the feature space • Kernels • Mercer’s theorem • Finite domain • Arbitrary domain • Kernel families • Constructing new kernels from kernels • Constructing feature maps from kernels • Reproducing Kernel Hilbert Spaces (RKHS) • The Representer Theorem 3 Ralf Herbrich: Learning Kernel Classifiers Chapter 2 Q
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Aayush Bansal I received a PhD in Robotics from Carnegie Mellon University for my work on unsupervised learning of the 4D audio-visual world from sparse unconstrained real-world samples. I am really fortunate to have spent the wonderful graduate school days under the tutelage of Deva Ramanan and Yaser Sheikh. During the course of my graduate studies, I received an Uber Presidential Fellowship for
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On the Spectral Properties of Symmetric Functions with Omar Fawzi, Raghav Kulkarni Arxiv 2017 Spectral Norm of Symmetric Functions with Omar Fawzi, Hamed Hatami International Workshop on Randomization and Computation (RANDOM), 2012 The NOF Multiparty Communication Complexity of Composed Functions with Arkadev Chattopadhyay, Omar Fawzi, Phuong Nguyen International Conference on Automata, Languages
www.cs.cmu.edu/~tsimon
Abstract We present an approach that uses a multi-camera system to train fine-grained detectors for keypoints that are prone to occlusion, such as the joints of a hand. We call this procedure multiview bootstrapping: first, an initial keypoint detector is used to produce noisy labels in multiple views of the hand. The noisy detections are then triangulated in 3D using multiview geometry or marked
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Jun-Yan Zhu Assistant Professor School of Computer Science Carnegie Mellon University Email: junyanz at cs dot cmu dot edu I am an Assistant Professor with The Robotics Institute in the School of Computer Science of Carnegie Mellon University. I also hold affiliated faculty appointments in the Computer Science Department and Machine Learning Department. I study computer vision, graphics, computati
15-213/18-213/15-513: Intro to Computer Systems (ICS) Fall 2015 15-213/18-213: Lecture TR, 1:30-2:50, DH 2210 12 units 15-513: Videotaped lectures and recitations (These will appear within 24 hours guaranteed, but typically within a couple of hours.) 6 or 12 units The ICS course provides a programmer's view of how computer systems execute programs, store information, and communicate. It enables st
Currently, I am an Associate Professor at the School of Intelligent Systems Engineering, Sun Yat-sen University. Before that, I was a Project Scientist in Machine Learning Department, Carnegie Mellon University, working with Prof. Eric P. Xing. I obtained my Ph.D. degree in the School of Data and Computer Science at Sun Yat-sen University in June 2016, advised by Prof. Liang Lin. I was a visiting
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15-816 Linear Logic Schedule Lectures are Monday and Wednesday, 12:00-1:20 EST, GHC 4303. We provide live streaming video during lecture The class notes provide additional reading material. They complement, but do not replace the lecture. The schedule is subject to change throughout the semester. Date Lecture Notes Additional Reading Video Due
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Scalable, High Performance Ethernet Forwarding with CUCKOOSWITCH Dong Zhou, Bin Fan, Hyeontaek Lim, Michael Kaminsky†, David G. Andersen Carnegie Mellon University, †Intel Labs {dongz,binfan,hl,dga}@cs.cmu.edu, michael.e.kaminsky@intel.com ABSTRACT Several emerging network trends and new architectural ideas are placing increasing demand on forwarding table sizes. From massive- scale datacenter net
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This book, with minor revisions, is back in print from Dover Publications and can be purchased in paperback form at Amazon.com, Barnes & Noble, etc. An e-book version will be released in late February, 2013. Free software accompanying the book is also available. This 1990 edition may be distributed in hardcopy form, for non-profit educational purposes, provided that no fee is charged to the recipi
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Mandatory: Linear Algebra. Basic Probability Theory. Coding Skills: This course will require significant programming form the students. Students must be able to program fluently in at least one language (C, C++, Java, Python, LISP, Matlab are all acceptable). Voice recognition systems invoke concepts from a variety of fields including speech production, algebra, probability and statistics, infor
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