PDF-Learning Invariant Representations with Local Transformations Kihyuk Sohn kihyuksumich

edu Honglak Lee honglakeecsumichedu Dept of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109 USA Abstract Learning invariant representations is an im portant problem in machine learning and pattern recognition In. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 15. 14. A Chessboard Problem. ?. A . Bishop . can only move along a diagonal. Can a . bishop . move from its current position to the question mark?. and calculus of shapes. © Alexander & Michael Bronstein, 2006-2010. tosca.cs.technion.ac.il/book. VIPS Advanced School on. Numerical Geometry of Non-Rigid Shapes . University of Verona, April 2010. Lecture 3. Jitendra. Malik. Pose and Shape. Rotations and reflections are examples. of orthogonal transformations . Rigid body motions. (Euclidean transformations / . isometries. ). Theorem:. Any rigid body motion can be expressed as an orthogonal transformation followed by a translation.. Rishabh. Singh and . Sumit. . Gulwani. FlashFill. Transformations. Syntactic Transformations . Concatenation of regular expression based substring. “VLDB2012” .  “VLDB”. Semantic Transformations. Maurice J. . Chacron. and Kathleen E. Cullen. Outline. Lecture 1: . - Introduction to sensorimotor . . transformations. - . The case of “linear” sensorimotor . transformations: . Affine transformations . preserve. affine combinations of points. . . Affine transformations preserve lines and planes.. . Parallelism of lines and planes is preserved. . The columns of the matrix reveal the transformed coordinate frame.. Natural Language Processing. Tomas Mikolov, Facebook. ML Prague 2016. Structure of this talk. Motivation. Word2vec. Architecture. Evaluation. Examples. Discussion. Motivation. Representation of text is very important for performance of many real-world applications: search, ads recommendation, ranking, spam filtering, …. Learning Targets: 8.G.2,8.G.3, 8.G.4. Follow the slides to learn more about transformations. Students should have paper and a pencil for notes at their desk while going through this presentation.. Transformation: a transformation is a change in position, shape or size.. Seminar 9: Alternative Currencies and Current Alternatives. Dr Harry Pitts. Money and the capitalist schema. Lotz gives a Kantian interpretation of the law of value. This states that the money form works along schematic lines. . What is a Parent Function. A parent function is the most basic version of an algebraic function.. Types of Parent Functions. Linear f(x) = mx b. Quadratic f(x) = x. 2. Square Root f(x) = √x. Exponential f(x) = . Rahul Sharma and Alex Aiken (Stanford University). 1. Randomized Search. x. = . i. ;. y = j;. while . y!=0 . do. . x = x-1;. . y = y-1;. if( . i. ==j ). assert x==0. No!. Yes!.  . 2. Invariants. Use . adversarial learning . to suppress the effects of . domain variability. (e.g., environment, speaker, language, dialect variability) in acoustic modeling (AM).. Deficiency: domain classifier treats deep features uniformly without discrimination.. . 16-385 Computer Vision. Spring 2019, . Lecture 7. http://www.cs.cmu.edu/~16385/. Course announcements. Homework 2 is posted on the course website.. - It is due on February 27. th. at 23:59 pm.. - Start early because it is much larger and more difficult than homework 1.. CS5670: Computer Vision. Reading. Szeliski. : Chapter 3.6. Announcements. Project 2 out, due Thursday, March 3 by 8pm. Do be done in groups of 2 – if you need help finding a partner, try Ed Discussions or let us know.