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Compiling Graphical Models Adnan Darwiche University of California, Los Angeles UAI06 Tutorial

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Compilation: Historical Motivation Separate inference into two phases: Offline: Compile model into a structure Online: Use structure to answer queries Goal: Push as much work into offline phase to optimize online inference time Best initial example: Offline: Compile a Bayesian network into a jointree Online: Use jointree to answer multiple queries efficiently

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Compilation: Modern Motivation Exploit model structure in inference: Global structure: Exhibited in model topology Measured by treewidth Exploited by most (non-compilation) algorithms Local structure: Exhibited in model parameters Type 1: Determinism Type 2: Context-specific independence Local structure is best exploited in the context of compilation: main theme

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Compilation: Theoretical Implications Unifies inference paradigms Variable elimination Jointree (Tree clustering) Conditioning Compilation as a trace of classical inference

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Bayesian Networks Battery Age Alternator Fan Belt Battery Charge Delivered Battery Power Starter Radio LightsEngine Turn Over Gas Gauge Gas Fuel Pump Fuel Line Distributor Spark Plugs Engine Start Local Knowledge

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Bayesian Networks Battery Age Alternator Fan Belt Battery Charge Delivered Battery Power Starter Radio LightsEngine Turn Over Gas Gauge Gas Fuel Pump Fuel Line Distributor Spark Plugs Engine Start ON OFF OK WEAK DEAD Lights Battery Power.99.01.20.80 01 If Battery Power = OK, then Lights = ON (99%) ….

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Bayesian Networks Battery Age Alternator Fan Belt Battery Charge Delivered Battery Power Starter Radio LightsEngine Turn Over Gas Gauge Gas Fuel Pump Fuel Line Distributor Spark Plugs Engine Start

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Global Structure: Treewidth w

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Battery Age Alternator Fan Belt Battery Charge Delivered Battery Power Starter Radio LightsEngine Turn Over Gas Gauge Gas Fuel Pump Fuel Line Distributor Spark Plugs Engine Start Local Structure: CSI and Determinism

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Battery Age Alternator Fan Belt Battery Charge Delivered Battery Power Starter Radio LightsEngine Turn Over Gas Gauge Gas Fuel Pump Fuel Line Distributor Spark Plugs Engine Start Context Specific Independence (CSI) Local Structure: CSI and Determinism

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Battery Age Alternator Fan Belt Battery Charge Delivered Battery Power Starter Radio LightsEngine Turn Over Gas Gauge Gas Fuel Pump Fuel Line Distributor Spark Plugs Engine Start ON OFF OK WEAK DEAD Lights Battery Power.99.01.20.80 01 If Battery Power = Dead, then Lights = OFF Determinism

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Todays Models … Characterized by: Richness in local structure (determinism, CSI) Massiveness in size (100,000s variables not uncommon) High connectivity (treewidth > 50, > 100) Enabled by: High level modeling tools: relational, first order New application areas (synthesis): Bioinformatics (e.g. linkage analysis) Sensor networks Exploiting local structure a must!

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High Order Specifications: Relational Models… burglary(v)=0.005; alarm(v)=(burglary(v):0.95,0.01); calls(v,w)= (neighbor(v,w): (prankster(v)): (alarm(w):0.9,0.05), (alarm(w):0.9,0)),0); alarmed(v)= n-or{calls(w,v)|w:neighbor(w,v)} burglary(v)=0.005; alarm(v)=(burglary(v):0.95,0.01); calls(v,w)= (neighbor(v,w): (prankster(v)): (alarm(w):0.9,0.05), (alarm(w):0.9,0)),0); alarmed(v)= n-or{calls(w,v)|w:neighbor(w,v)} Primula

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Friends and Smokers ( Richardson & Domingos, 2004) M individuals Relations such as smokes(p), cancer(p), friend(p 1,p 2 ) Logical constraints such as: if one of p's friends smokes, then p smokes. Sample Query: probability that given person has cancer

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Students (Pasula & Russell, 2001) P professors S students Various relations, such as famous(p), well- funded(p), success(s), advises(p,s) Sample Query: probability a professor is well-funded given success of advised students 12.9663 3.2120 1.8439 0.2885 0.0930 0.0530 Online Time (sec) 3336,450,23164,32517,69323362,30206-24 1416,936,50433,3539,20917633,45406-12 75,236,25738,88910,73414838,16805-20 32,531,23020,2795,62412820,68805-10 3815,46121,1155,85910121,07004-16 2445,41011,0993,0997211,56604-08 Offline Time (min) AC Edges Cnf Clauses CNF Vars Treewidth w Network params Students- Profs

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Ordering genes on a chromosome and determining distance between them Useful for predicting and detecting diseases Associating functionality of genes with their location on the chromosome Gene 1 Gene 2 Gene 3 Genetic Linkage Analysis

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Pedigrees + Phenotype + Genotype

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DBNs from Speech Applications

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Coding Networks

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Tutorial Outline Theoretical foundations Online query answering algorithms Offline compilation algorithms Applications Concluding remarks

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Theoretical Foundations Graphical Model (Bayesian, Markov Networks): Is a Multi-Linear Function (MLF) Compiled Model: Is an Arithmetic Circuit (AC) Compilation process: Factoring MLF into AC

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Multi-Linear Functions Arithmetic Circuits AB * * ** + ++ * ** * Factoring A Differential Approach to Inference in Bayesian Networks JACM-03 (Darwiche)

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Factoring Multi-linear Functions (MLFs) a + ad + abd + abcd MLF: * + * abdc1 + Arithmetic Circuit (AC) An MLF has an exponential number of terms, yet it may be represented by an AC with polynomial size! A graphical model defines an MLF Evaluating the MLF for a given evidence gives the probability of evidence The inference problem can be formulated as factoring the MLF of a graphical model Circuit Complexity: Size of smallest AC that computes the MLF

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Pr(a) =.03 +.27 =.3 false B.03.27 A.56.14 true false Pr(.) false true Graphical Models as MLFs

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Pr(~b) =.27 +.14 =.41 false B.03.27 A.56.14 true false true Pr(.) Graphical Models as MLFs

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.03λ a λ b +.27λ a λ ~b +.56λ ~a λ b +.14λ ~a λ ~b false B.03 A true false true.27.14.56 λ a* λ b *.03 λ a* λ ~ b *.27 λ ~ a* λ b *.56 λ ~ a* λ ~ b*.14 F (λ ~a, λ ~b, λ a, λ b ) = Pr(.) Graphical Models as MLFs

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=.03λ a λ b +.27λ a λ ~b +.56λ ~a λ b +.14λ ~a λ ~b F (λ ~a, λ ~b, λ a, λ b ) Pr(a,~b) = F (λ ~a :0, λ ~b :1, λ a :1, λ b :0) =.27 Pr(a) = F (λ ~a :0, λ ~b :1, λ a :1, λ b :1) =.03+.27

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A B C θ b|a θaθa θ c|a ABCPr(.) abcθ a θ b|a θ c|a ab~cθ a θ b|a θ ~c|a a~bcθ a θ ~b|a θ c|a a~b~cθ a θ ~b|a θ ~c|a...…

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A B C θ b|a θaθa θ c|a ABCPr(.) abc λ a λ b λ c θ a θ b|a θ c|a ab~c λ a λ b λ ~c θ a θ b|a θ ~c|a a~bc λ a λ ~b λ c θ a θ ~b|a θ c|a a~b~c λ a λ ~b λ ~c θ a θ ~b|a θ ~c|a...…

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F = λ a λ b λ c θ a θ b|a θ c|a + λ a λ b λ ~c θ a θ b|a θ ~c|a + λ a λ ~b λ c θ a θ ~b|a θ c|a + λ a λ ~b λ ~c θ a θ ~b|a θ ~c|a …. A B C

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F = λ a λ b λ c λ d θ a θ b|a θ c|a θ d|bc + λ a λ b λ c λ ~d θ a θ b|a θ c|a θ ~d|bc + …. A B C D Each term has 2n variables (n indicators, n parameters) Each variable has degree one (multi-linear function) θaθa θ b|a θ c|a θ d|bc

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Multi-Linear Functions Arithmetic Circuits AB * * ** + ++ * ** * Factoring

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Online Query Answering Complexity: Time and space linear in the AC size Queries: Probability of evidence, with evidence flipping/fast retraction Variable and family marginals MPE: most probable explanation Sensitivity analysis (derivatives)

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Evaluating the Polynomial

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PR: Probability of Evidence Battery Age Alternator Fan Belt Battery Charge Delivered Battery Power Starter Radio LightsEngine Turn Over Gas Gauge Gas Fuel Pump Fuel Line Distributor Spark Plugs Engine Start Pr(e)

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The Partial Derivatives

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PR: Probability of Evidence Flips Battery Age Alternator Fan Belt Battery Charge Delivered Battery Power Starter Radio LightsEngine Turn Over Gas Gauge Gas Fuel Pump Fuel Line Distributor Spark Plugs Engine Start Pr(e) X

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PR: Probability of Evidence Flips Battery Age Alternator Fan Belt Battery Charge Delivered Battery Power Starter Radio LightsEngine Turn Over Gas Gauge Gas Fuel Pump Fuel Line Distributor Spark Plugs Engine Start Pr(e-X,x) X

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The Partial Derivatives

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PR: Family Marginals Battery Age Alternator Fan Belt Battery Charge Delivered Battery Power Starter Radio LightsEngine Turn Over Gas Gauge Gas Fuel Pump Fuel Line Distributor Spark Plugs Engine Start U X Pr(e,x,u)

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Multi-Linear Functions Arithmetic Circuits AB * * ** + ++ * ** * Factoring

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* * * * + + + * * * * Circuit Evaluation and Differentiation: Marginals.3 1.1 1.9.8 1.2 0.7.3.3.1.9.8.2 0 1 1.3 0 1 1 1 1.3.3 0 0 1 1.3.03.30.270.70 Two passes only: probability of evidence (with evidence flipping) Node marginals Family marginals Sensitivity

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Efficient Eval/Diff Schemes Assume alternating levels of +/* nodes, with one parent per *node Method A: Two registers per +node (no registers for *nodes) Method B: One register per node (use for values in upward pass, then override with derivatives in downward pass) Method C: One register per node, one bit per *node

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.3 1.1 1.9.8 1.2 0.7 ** ** m mm ** * *.27.3.1.9.8.2 0.9.8.27 0 Circuit Optimization: MPE * **

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m * m **

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Custom Hardware for Evaluating ACs Adharapurapu, Ercegovac (2004)

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Offline Compilation Factoring MLFs into ACs: Jointree: Embeds AC Variable Elimination: Trace is an AC Recursive Conditioning: Trace is an AC Reduction to Logic: CNF to d-DNNF compilation

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Compiling using Jointrees Classical Jointree Algorithm: Convert model into jointree Jointree propagation (two-passes) Modern interpretation: Jointree embeds an AC that factors MLF Jointree propagation is evaluating/differentiating embedded AC

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AB A AB root A Jointree Embeds an AC… ACAD AE AB A AB Inward-pass evaluates circuit Outward-pass differentiates circuit [Hugin, Shenoy Shafer,…] A Differential Semantics to Jointree Algorithms AIJ-04 (with James Park)

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Efficient Eval/Diff Schemes Assume alternating levels of +/* nodes, with one parent per *node Method A: Two registers per +node (no registers for *nodes) Method B: One register per node (use for values in upward pass, then override with derivatives in downward pass) Method C: One register per node, one bit per *node

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Jointree Flavors Shenoy-Shafer: Method A Hugin: Method B (looses information) Zero-Conscious Hugin (new): Method C (best of A,B)

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Compiling using Variable Elimination (VE) VE operates on factors: Mappings from variable instantiations to real numbers VE performs two operations on factors: Multiply two factors Sum-Out a variable from factor Factors have different representations: Tables More structured representations (decision trees/graphs) Overhead problem for structured factors

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A B true false A.3.7 TATA Tabular Factors false B.1.9 A.8.2 true false TBTB true

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X Z.1.9 Y.5 Z Structured Factors: Algebraic Decision Diagrams (ADDs)

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NetworkMax ClustVarsCardTotal Parms%Det%Distinct alarm7.2372...47520.924.6 bm2010052...2697299.6100 diabetes17.24133...2146106978.217.6 hailfinder11.7562...11374115.726.9 mildew21.4353...10054715893.225.1 mm2312202...2832698.775 munin126.81891...211946666.561.2 munin218.610032...218392063.369.5 munin317.810441...218585563.171.3 munin421.410411...219818364.565.3 pathfinder151092...639785156.15.1 pigs17.44413...3842756.223.9 students223762...2261690.779.3 tcc4f101052...232360.435.6 water19.9323...4134845457 Networks with Local Structure

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VE: Tabular vs ADD Representations of Factors TabularADD NetworkTime (ms) Improvement alarm313600.086 barley30714,0490.022 bm-5-34,8926587.435 diabetes94933,2200.029 hailfinder485150.093 link1,6882,6580.635 mm-3-8-32,1668432.569 mildew7292,6020.001 munin11551,2550.124 munin22043,1700.064 munin33505,0490.069 munin44064,3610.093 pathfinder515,2130.01 pigs695970.116 st-3-21863620.514 tcc4f291530.19 water761,0150.075

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Compiling using Variable Elimination (VE) By using symbolic factors and corresponding operations: VE compiles out an AC VE with tabular factors: Generates ACs similar to those embedded in jointree VE with structured factors: Generates much smaller ACs Overhead pushed into offline phase

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A B true false A.3.7 TATA Factors false B.1.9 A.8.2 true false TBTB true

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A B false ATATA θ a * λ a θ ~a * λ ~a false BA true false TBTB true θ ~b|a * λ ~b θ b|~a * λ b θ b|a * λ b θ ~b|~a * λ ~b Symbolic Factors

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true false ATBTB θ b|a * λ b + θ ~b|a * λ ~b θ b|~a * λ b + θ ~b|~a * λ ~b false BA true false TBTB true θ ~b|a * λ ~b θ b|~a * λ b θ b|a * λ b θ ~b|~a * λ ~b Summing out B Summing out Variable B

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*= Multiplying Factors true false AT A T B θ a * λ a *( θ b|a * λ b + θ ~b|a * λ ~b ) θ ~ a * λ ~ a *( θ b|~a * λ b + θ ~b|~a * λ ~b ) true false ATBTB θ b|a * λ b + θ ~b|a * λ ~b θ b|~a * λ b + θ ~b|~a * λ ~b true false A TATA θ a *λ a θ ~a *λ ~a

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θ a * λ a * ( θ b|a * λ b + θ ~b|a * λ ~b ) + θ ~ a * λ ~ a ( θ b|~a * λ b + θ ~b|~a * λ ~b ) true false AT A T B θ a * λ a * ( θ b|a * λ b + θ ~b|a * λ ~b ) θ ~ a * λ ~ a * ( θ b|~a * λ b + θ ~b|~a * λ ~b ) Summing out Variable A

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VE factors MLF into AC (Bottom up Construction) AB * * ** + ++ * ** * Factoring Time and space complexity of generating AC is similar to Variable Elimination: Exponential only in treewidth Generated ACs similar to those embedded in Jointree Recall: AC can be used to answer multiple queries!

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X Z.1.9 Y.5 Z Structured Factors: Algebraic Decision Diagrams (ADDs)

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X ZY Z Symbolic ADD Modify standard ADD operations (multiply, sum-out) to operate on symbolic ADDs Run variable elimination with symbolic ADDs Compile out an AC Asymptotic complexity is no worse than variable elimination Overhead of ADDs is pushed into offline phase Generated AC can be much smaller Online inference can be much faster

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NetworkMax ClustVarsCardTotal Parms%Det%Distinct alarm7.2372...47520.924.6 bm2010052...2697299.6100 diabetes17.24133...2146106978.217.6 hailfinder11.7562...11374115.726.9 mildew21.4353...10054715893.225.1 mm2312202...2832698.775 munin126.81891...211946666.561.2 munin218.610032...218392063.369.5 munin317.810441...218585563.171.3 munin421.410411...219818364.565.3 pathfinder151092...639785156.15.1 pigs17.44413...3842756.223.9 students223762...2261690.779.3 tcc4f101052...232360.435.6 water19.9323...4134845457 Networks with Local Structure

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TabularADD NetworkTime (ms) Improvement alarm313600.086 barley30714,0490.022 bm-5-34,8926587.435 diabetes94933,2200.029 hailfinder485150.093 link1,6882,6580.635 mm-3-8-32,1668432.569 mildew7292,6020.001 munin11551,2550.124 munin22043,1700.064 munin33505,0490.069 munin44064,3610.093 pathfinder515,2130.01 pigs695970.116 st-3-21863620.514 tcc4f291530.19 water761,0150.075 Tabular vs ADD: Standard VE

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Time (s)AC size NetworkAceADD-VEImprov.Tabular-VEADD-VEImprov. alarm0.33.90.13,5343,0301.2 barley8,190.20122.866.766,467,77724,653,7442.7 bm-5-30.860.175,591,75014,8365095.2 diabetes1,710.00110.315.534,728,95717,219,0422 hailfinder0.71.20.572,75525,9922.8 link-699.7-127,262,77789,097,4501.4 mildew3,125.20218.914.316,094,5923,352,3304.8 mm-3-8-31.511.90.136,635,566108,428337.9 munin11,005.10316.73.2 1,260,407,123 31,409,97040.1 munin2198.431.76.320,295,4265,662,2183.6 munin3188.417.610.716,987,0883,503,2424.8 munin420537.85.476,028,5326,869,76011.1 pathfinder4.95.80.9796,58844,46817.9 pigs23.1102.34,925,3882,558,6801.9 st-3-20.52.40.219,374,93422,070877.9 tcc4f0.91.10.833,40822,6121.5 water320.70.115,996,054170,42893.9 Tabular vs ADD: VE Compilations

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NetworkJointreeADD-VEImprov. alarm166325.2 barley65,22635,2091.9 bm-5-389,593831079.4 diabetes29,31620,4211.4 hailfinder245703.5 link223,542175,7691.3 mildew10,0774,5222.2 mm-3-8-334,001198171.7 munin1669,91537,45117.9 munin217,8577,1802.5 munin313,3514,9452.7 munin442,7548,6834.9 pathfinder1,33210213.1 pigs3,0202,8141.1 st-3-217,53682213.9 tcc4f281733.8 water16,67625166.4 ADD-VE vs Jointree: Online Inference Time (ms) Computing all marginals, for 16 pieces of random evidence Work on structured representations of factors is now much more relevant and practical.

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Compiling by Reduction to Logic Algebraic: MLFs / ACs Logical: CNF / d-DNNF Factoring MLF into AC can be reduced to factoring CNF into d-DNNF CNF to d-DNNF compilers are very powerful (natural for exploiting determinism and CSI)

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Compiler: http://reasoning.cs.ucla.edu/c2d d-DNNF CNF Multi-Linear Function Arithmetic Circuit Encode Decode Reduction to Logic

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a c + a b c + c Multi-linear function:Propositional theory: c ^ (a b) Encode c b 1 a 1 Arithmetic Circuit Decode c b b a a Smooth d-DNNF Compile MLFs ACs CNFs d-DNNF

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or and A A or and B C or and D E or BD and, Deterministic, Decomposable NNF

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or and A A or and B C or and D E or BD and, Deterministic, Decomposable NNF Deterministic: Disjuncts are logically disjoint

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or and A A or and B C or and D E or BD and Deterministic, Decomposable NNF B C B D E D Decomposable: Conjuncts share no variables Compiling CNFs into d-DNNFs AAAI-02, ECAI-04 Compiler at http://reasoning.cs.ucla.edu/c2dhttp://reasoning.cs.ucla.edu/c2d

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A B C A D E Recursive Conditioning for Compilation or B C D E B C A and A B C D E B C D E

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and 0 A B 1 DB C D E And-Or Graphs

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Propositional Encoding of Multi-Linear Functions Propositional theory: Δ = c ^ (a b) Encodes: F = a c + a b c + c abcEncodes TTT abc TTFab TFT ac TFFa FTTbc FTFb FFT c FFF1

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A B F = λ a λ b θ a θ b|a + λ a λ ~b θ a θ ~b|a + λ ~a λ b θ ~a θ b|~a + λ ~a λ ~b θ ~a θ ~b|~a λ a λ ~a ¬ λ a ¬ λ ~a λ b λ ~b ¬ λ b ¬ λ ~b λ a θ a λ ~a θ ~a λ a ^ λ b θ b|a λ a ^ λ ~ b θ ~ b|a λ ~ a ^ λ b θ b|~a λ ~ a ^ λ ~ b θ ~ b|~a Encoding Model as CNF

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Why Logic? Encoding local structure is easy: Determinism encoded by adding clauses: CSI encoded by collapsing variables: A natural environment to exploit local structure: DD-backtracking, clause learning, … Non-structural decomposition Non-structural (formula) caching

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ABC S 0.95 c abc A Pr(S|A,B,C) BC a a a a a a a b b b b b b b c c c c c c 0.95 0.20 0.05 0.00 Tabular CPT -Functional constraints -Context-specific independence s|abe Local Structure

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0.95 c abc A Pr(S|A,B,E) BC a a a a a a a b b b b b b b c c c c c c 0.95 0.20 0.05 0.00 Tabular CPT λ ~a λ b λ c λ s θ s|~abc ¬ λ ~a ¬ λ b ¬ λ c ¬ λ s Determinism

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0.95 c abc A Pr(S|A,B,C) BC a a a a a a a b b b b b b b c c c c c c 0.95 0.20 0.05 0.00 Tabular CPT λ a λ b λ s θ s|ab λ a λ b λ c λ s θ s|abc λ a λ b λ ~c λ s θ s|ab~c Context-Specific Independence

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X Y Belief network x x x y x|y …. CNF Smooth d-DNNF x y x|y x x y x|y Arithmetic Circuit The Ace System: http://reasoning.cs.ucla.edu/ace

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Time (s)AC size NetworkAceADD-VEImprov.Tabular-VEADD-VEImprov. alarm0.33.90.13,5343,0301.2 barley8,190.20122.866.766,467,77724,653,7442.7 bm-5-30.860.175,591,75014,8365095.2 diabetes1,710.00110.315.534,728,95717,219,0422 hailfinder0.71.20.572,75525,9922.8 link-699.7-127,262,77789,097,4501.4 mildew3,125.20218.914.316,094,5923,352,3304.8 mm-3-8-31.511.90.136,635,566108,428337.9 munin11,005.10316.73.2 1,260,407,123 31,409,97040.1 munin2198.431.76.320,295,4265,662,2183.6 munin3188.417.610.716,987,0883,503,2424.8 munin420537.85.476,028,5326,869,76011.1 pathfinder4.95.80.9796,58844,46817.9 pigs23.1102.34,925,3882,558,6801.9 st-3-20.52.40.219,374,93422,070877.9 tcc4f0.91.10.833,40822,6121.5 water320.70.115,996,054170,42893.9 ADD-VE vs Logic (Ace): Compile Times

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NetworkNodesParametersMax Cluster mastermind_04_08_031418980226 mastermind_06_08_0318141275437 mastermind_10_08_0326061865854 mastermind_03_08_0422881600831 mastermind_04_08_0426161848839 mastermind_03_08_0536922618640 students_03_02376261625 students_03_121346985659 students_04_16282721070101 students_05_20506438168148 students_06_24820162302233 blockmap_05_031005697223 blockmap_10_0368484875852 blockmap_15_031878713243668 blockmap_20_034335630722092 blockmap_22_0359404423452104 ADD-VE vs Logic (Ace)

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Network Offline Time (min) AC NodesAC Edges Online Inference Time (s) mastermind_04_08_03171,666541,3560.05 mastermind_06_08_031258,2281,523,8880.15 mastermind_10_08_0331,293,3234,315,5660.68 mastermind_03_08_042186,3514,859,2010.3 mastermind_04_08_045932,35519,457,3081.73 mastermind_03_08_05101,359,39155,417,6394.33 students_03_0217,92737,2810.01 students_03_12124,219113,8760.02 students_04_163181,166815,4610.09 students_05_2071,319,8345,236,2571.84 students_06_24339,922,23336,450,23112.97 blockmap_05_0312,83320,6360.01 blockmap_10_03217,749974,8170.06 blockmap_15_03647,4757,643,3070.38 blockmap_20_0330105,60240,172,4342.45 blockmap_22_0361144,13676,649,3024.67 ADD-VE vs Logic (Ace)

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Effect of Local Structure Local Structure Encoded PathfinderWaterMunin4 None981,17813,777,166116,136,985 Det + CSI42,810 (4%) 134,140 (1%) 5,762,690 (5%) Det130,380 (13%) 138,501 (1%) 9,997,267 (9%) CSI200,787 (20%) 11,111,104 (81%) 17,612,036 (15%)

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Compilation vs Direct Inference Grid problems here…

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Compilation vs Direct Inference Grid size Treewidth w DetCachet (sec) Ace offline (sec) Ace online (sec) Offline/ Online 16x162550%22362202.0721079 22x223675%27573492.1782024 34x346090%1584790.4193783 Average over 10 random instances for each grid Ace available at http://reasoning.cs.ucla.edu/acehttp://reasoning.cs.ucla.edu/ace

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Applications Relational Models Diagnosis Genetic Linkage Analysis

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burglary(v)=0.005; alarm(v)=(burglary(v):0.95,0.01); calls(v,w)= (neighbor(v,w): (prankster(v)): (alarm(w):0.9,0.05), (alarm(w):0.9,0)),0); alarmed(v)= n-or{calls(w,v)|w:neighbor(w,v)} burglary(v)=0.005; alarm(v)=(burglary(v):0.95,0.01); calls(v,w)= (neighbor(v,w): (prankster(v)): (alarm(w):0.9,0.05), (alarm(w):0.9,0)),0); alarmed(v)= n-or{calls(w,v)|w:neighbor(w,v)} Primula/Ace: Upcoming Release

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Friends and Smokers ( Richardson & Domingos, 2004) M individuals Relations such as smokes(p), cancer(p), friend(p 1,p 2 ) Logical constraints such as: if one of p's friends smokes, then p smokes. Sample Query: probability that given person has cancer

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Friends & Smokers MNetwork params Treewidth w CNF Vars Cnf Clauses AC Edges Online Time (sec) Offline Time (sec) 134312311800.03 41,552134141,3902930.0030.44 77,714361,9956,9161,2950.0061.92 1021,760705,56519,5253,5120.0056.66 1346,93011811,93442,1337,4300.01312.8 1686,46417221,91277,65613,5350.02221.68 19143,60224436,309129,01022,3130.03538.36 22221,58431655,935199,11134,2500.05890.67 25323,65041281,600290,87549,8320.079162.45 28453,040528114,114407,21869,5450.114274.2 29502,802560126,614451,96577,1180.119275.17

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Students (Pasula & Russell, 2001) P professors S students Various relataios, such as famous(p), well- funded(p), success(s), advises(p,s) Sample Query: probability a professor is well-funded given success of advised students

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Students Students- Profs Network params Treewidth w CNF Vars Cnf Clauses AC Edges Online Time (sec) Offline Time (min) 04-0811,566723,09911,099445,4100.05302 04-1621,0701015,85921,115815,4610.09303 05-1020,6881285,62420,2792,531,2300.28853 05-2038,16814810,73438,8895,236,2571.84397 06-1233,4541769,20933,35316,936,5043.212014 06-2462,30223317,69364,32536,450,23112.966333

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Diagnosis QMR-like: Effect of Encoding Evidence 600 diseases (D) and 4100 features (F) Feature F j is a noisy-or of parent diseases D i (11 parents chosen randomly) Sample Query: probability of disease given partial evidence on features. D1D1 D2D2 D3D3 DmDm … F1F1 F2F2 FnFn …

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Treewidth: 586-589 CNF variables: 94,900 CNF clauses: 188,600 No. True Features AC Edges Online Time (sec) Offline Time (sec) 048,1000.0523.73 352,8300.0523.86 657,6380.0523.81 962,5470.0523.82 1267,6320.0524.19 1573,3210.0423.6 1881,6290.0524.95 21109,3350.0530.95 25434,4450.08155.12 271,141,6740.17469.7 281,691,8330.23728.52 292,352,8200.31,046.93 Diagnosis QMR-like: Effect of Encoding Evidence

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Ordering genes on a chromosome and determining distance between them Useful for predicting and detecting diseases Associating functionality of genes with their location on the chromosome Gene 1 Gene 2 Gene 3 Genetic Linkage Analysis

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Pedigrees + Phenotypes + Genotypes

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Arithmetic Circuit Gene 1 Gene 2

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State of the Art Linkage PedigreeOffline (sec) AC EdgesOnline (sec) Superlink 1.4 (sec) EE3325.332,070,7070.591,046.72 EE3761.291,855,4100.391,381.61 EE30376.7827,997,6868.37815.33 EE2389.473,986,8161.08502.02 EE18283.9623,632,2006.63248.11

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Model Compilation: Factoring MLFs into ACs Classical algorithms factor MLFs into ACs: Jointree embeds AC Variable elimination constructs AC bottom up Recursive conditioning constructs AC top down Factoring MLFs into ACs can be reduced to logical reasoning Exploiting local structure to build smaller ACs: Compiling models with very high treewidth is common place Boundary between exact and approximate inference is much changed Public systems now available!

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