PPT-Probabilistic Inference Modulo Theories

Rodrigo de Salvo Braz Ciaran OReilly Artificial Intelligence Center SRI International Vibhav Gogate University of Texas at Dallas Rina Dechter University of California Irvine IJCAI16 . However subjects make discrete responses and report the phenomenal contents of their mind to be allornone states rather than graded probabilities How can these 2 positions be reconciled Selective attention tasks such as those used to study crowding This paper presents a novel framework to extend the dynamic range of images called Unbounded High Dynamic Range (UHDR) photography with a modulo camera. A modulo camera could theoretically take unbounded radiance levels by keeping only the least significant bits. We show that with limited bit depth, very high radiance levels can be recovered from a single modulus image with our newly proposed unwrapping algorithm for natural images. We can also obtain an HDR image with details equally well preserved for all radiance levels by merging the least number of modulus images. Synthetic experiment and experiment with a real modulo camera show the effectiveness of the proposed approach. (goal-oriented). Action. Probabilistic. Outcome. Time 1. Time 2. Goal State. 1. Action. State. Maximize Goal Achievement. Dead End. A1. A2. I. A1. A2. A1. A2. A1. A2. A1. A2. Left Outcomes are more likely. How the Quest for the Ultimate Learning Machine Will Remake Our World. Pedro Domingos. University of Washington. Machine Learning. Traditional Programming. Machine Learning. Computer. Data. Algorithm. Shou-pon. Lin. Advisor: Nicholas F. . Maxemchuk. Department. . of. . Electrical. . Engineering,. . Columbia. . University,. . New. . York,. . NY. . 10027. . Problem: . Markov decision process or Markov chain with exceedingly large state space. Machine Learning @ CU. Intro courses. CSCI 5622: Machine Learning. CSCI 5352: Network Analysis and Modeling. CSCI 7222: Probabilistic Models. Other courses. cs.colorado.edu/~mozer/Teaching/Machine_Learning_Courses. Learning Revealed. . Pedro Domingos. . University of Washington. Where Does Knowledge Come From?. Evolution. Experience. Culture. Where Does Knowledge Come From?. Evolution. Experience. Culture. Computers. . – What Next?. Martin Theobald. University of Antwerp. Joint work with . Maximilian Dylla, Sairam Gurajada, Angelika . Kimmig. , Andre . Melo. , Iris Miliaraki, . Luc de . Raedt. , Mauro . Sozio. Satisfiability. Modulo Theories . Frontiers . of . Computational Reasoning . 2009 . –. MSR Cambridge. Leonardo de Moura. Microsoft Research. Symbolic Reasoning. Quantifiers in . Satisfiability. Chapter 3: Probabilistic Query Answering (1). 2. Objectives. In this chapter, you will:. Learn the challenge of probabilistic query answering on uncertain data. Become familiar with the . framework for probabilistic . Dewayne E Perry. ARiSE. , ECE, UT Austin. perry@ece.utexas.edu. Theories D & E. I begin with two simple theories:. A theory about design – D. A theory about empirical evaluation – E. And a theory about how to model theories. Chapter 7: Probabilistic Query Answering (5). 2. Objectives. In this chapter, you will:. Explore the definitions of more probabilistic query types. Probabilistic skyline query. Probabilistic reverse skyline query. CS772A: Probabilistic Machine Learning. Piyush Rai. Course Logistics. Course Name: Probabilistic Machine Learning – . CS772A. 2 classes each week. Mon/. Thur. 18:00-19:30. Venue: KD-101. All material (readings etc) will be posted on course webpage (internal access). Nathan Clement. Computational Sciences Laboratory. Brigham Young University. Provo, Utah, USA. Next-Generation Sequencing. Problem Statement . Map next-generation sequence reads with variable nucleotide confidence to .