'Inference algorithm' diaporamas de présentation

Using Active Learning to Synthesize Models of Applications That Access Databases

Using Active Learning to Synthesize Models of Applications That Access Databases. Jiasi Shen , Martin Rinard MIT EECS & CSAIL. Mo tivation. Synthesized program. I/O examples often underspecify the program behavior I/O examples not necessarily easier to write than the program.

599 views • 59 slides

CS3243: Introduction to Artificial Intelligence

CS3243: Introduction to Artificial Intelligence. Semester 2, 2017/2018. Admin: Midterm Exam (5 Mar). During lecture time: 2-4pm: start at 2:05 exactly! Restricted Open Book: AIMA 3rd edition textbook Lecture notes (minimally annotated) Tutorial questions & solutions

555 views • 55 slides

Discovering Geographical Topics In The Twitter Stream

Discovering Geographical Topics In The Twitter Stream. PRESENTED BY TEAM-9. Liu,Zhi Karthik Kumar Rangineni. Discovering Geographical Topics In The Twitter Stream. Content. Introduction Related Work Model Experiment Conclusion.

642 views • 46 slides

CS 2710, ISSP 2610

CS 2710, ISSP 2610. Chapter 7 Propositional Logic Reasoning. Knowledge Based Agents. Central component: knowledge base , or KB . A set of sentences in a knowledge representation language Generic Functions TELL (add a fact to the knowledge base)

515 views • 39 slides

CS 2710, ISSP 2610

CS 2710, ISSP 2610. Chapter 7 Propositional Logic Reasoning. Knowledge Based Agents. Central component: knowledge base , or KB . A set of sentences in a knowledge representation language Generic Functions TELL (add a fact to the knowledge base)

406 views • 39 slides

Type Inference

Type Inference. September 26, 2006. Type Inference. Type inference is typically presented in two different forms: Type inference rules: Rules define the type of each expression Clean and concise; needed to study the semantic properties, i.e., soundeness, of the type system

315 views • 28 slides

QuASI: Question Answering using Statistics, Semantics, and Inference

QuASI: Question Answering using Statistics, Semantics, and Inference. Marti Hearst, Jerry Feldman, Chris Manning, Srini Narayanan Univ. of California-Berkeley / ICSI / Stanford University. Dynamic Probabilistic Inference for event structure. Srini Narayanan Jerry Feldman

335 views • 24 slides

Automated Refinement Checking of Concurrent Systems

313 views • 19 slides

Points-to Analysis in Almost Linear Time

Points-to Analysis in Almost Linear Time. paper by Bjarne Steensgaard 23rd ACM Symposium on Principles of Programming Languages (POPL'96) Microsoft Research Technical Report MSR-TR-95-08. presented by Jeff Blank CMSC 858Z Spring 2004. Outline of Talk. Background Source Language

255 views • 13 slides

Logical inference

230 views • 13 slides

Design Challenges and Misconceptions in Named Entity Recognition Lev Ratinov and Dan Roth

148 views • 1 slides