Difference between revisions of "f11Stat946presentation"

From statwiki
Jump to: navigation, search
m (Conversion script moved page F11Stat946presentation to f11Stat946presentation: Converting page titles to lowercase)
 
(15 intermediate revisions by 8 users not shown)
Line 23: Line 23:
 
|-
 
|-
 
|-
 
|-
|Nov 17 (Presentation 1)|| Venkata Manem || Quantifying cancer progression with conjunctive Bayesian networks.|| [http://bioinformatics.oxfordjournals.org/content/25/21/2809.full.pdf]
+
|Nov 17 (Presentation 1)|| Venkata Manem || Quantifying cancer progression with conjunctive Bayesian networks.|| [http://bioinformatics.oxfordjournals.org/content/25/21/2809.full.pdf] || [[Quantifying cancer progression with conjunctive Bayesian networks.|Summary]]
 
|-
 
|-
 
|-
 
|-
|Nov 17 (Presentation 2)||  Mohammad Rostami ||Compressed Sensing Reconstruction via Belief Propagation ||[http://dsp.rice.edu/sites/dsp.rice.edu/files/cs/csbpTR07142006.pdf]
+
|Nov 17 (Presentation 2)||  Mohammad Rostami ||Compressed Sensing Reconstruction via Belief Propagation ||[http://dsp.rice.edu/sites/dsp.rice.edu/files/cs/csbpTR07142006.pdf]|| [[Compressed Sensing Reconstruction via Belief Propagation|Summary]]
 
|-
 
|-
 
|-
 
|-
 
|Nov 22 (Presentation 1)|| Mazen A. Melibari ||An HDP-HMM for Systems with State Persistence|| [http://www.cs.brown.edu/~sudderth/papers/icml08.pdf]
 
|Nov 22 (Presentation 1)|| Mazen A. Melibari ||An HDP-HMM for Systems with State Persistence|| [http://www.cs.brown.edu/~sudderth/papers/icml08.pdf]
 +
|| [[An HDP-HMM for Systems with State Persistence|Summary]]
 
|-
 
|-
 
|-
 
|-
|Nov 22 (Presentation 2)||Tameem Adel|| Graphical Models for Structured Classification, with an Application to Interpreting Images of Protein Sub-cellular Location Patterns || [http://jmlr.csail.mit.edu/papers/volume9/chen08a/chen08a.pdf]
+
|Nov 22 (Presentation 2)||Tameem Adel|| Graphical Models for Structured Classification, with an Application to Interpreting Images of Protein Sub-cellular Location Patterns || [http://jmlr.csail.mit.edu/papers/volume9/chen08a/chen08a.pdf] || [[Graphical models for structured classification, with an application to interpreting images of protein subcellular location patterns|Summary]]
 
|-
 
|-
 
|-
 
|-
|Nov 24 (Presentation 1)|| Pouria Fewzee || Context Adaptive Training with Factorized Decision Trees for HMM-Based Speech Synthesis || [http://mi.eng.cam.ac.uk/~ky219/papers/yu-is10.pdf]
+
|Nov 24 (Presentation 1)|| Pouria Fewzee || Context Adaptive Training with Factorized Decision Trees for HMM-Based Speech Synthesis || [http://mi.eng.cam.ac.uk/~ky219/papers/yu-is10.pdf] || [[Context Adaptive Training with Factorized Decision Trees for HMM-Based Speech Synthesis|Summary]]
 
|-
 
|-
 
|-
 
|-
|Nov 24 (Presentation 2)|| Ali-Akbar Samadani || ||
+
|Nov 24 (Presentation 2)|| Ali-Akbar Samadani ||Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains || [http://ijr.sagepub.com/content/27/7/761.abstract]||[[Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains(Summary)|Summary]]
 
|-
 
|-
 
|-
 
|-
|Nov 29 (Presentation 1)||Hojatollah Yeganeh || Learning low-level vision ||[http://people.csail.mit.edu/billf/papers/TR2000-05.pdf]
+
|Nov 29 (Presentation 1)||Hojatollah Yeganeh ||Markov Random Fields for Super-Resolution ||[http://www.merl.com/reports/docs/TR2000-08.pdf]||[[Markov Random Fields for Super-Resolution|Summary]]
 
|-
 
|-
 
|-
 
|-
|Nov 29 (Presentation 2)||Areej Alhothali || Video-based face recognition using adaptive hidden markov models||[http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1211373]
+
|Nov 29 (Presentation 2)||Areej Alhothali || Video-based face recognition using adaptive hidden markov models||[http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1211373]||[[Video-based face recognition using Adaptive HMM|Summary]]
 
|}
 
|}
 
|}
 
|}

Latest revision as of 09:45, 30 August 2017

Sign up for your presentation in the following table. Chose a date between Nov 15 and Dec 1 (inclusive). You just need to sign up your name at the moment. When you chose the paper that you would like to present, add its title and a link to the paper.


Date Speaker Title Link Summary
Nov 15 (Presentation 1) Azin Ashkan A Dynamic Bayesian Network Click Model for Web Search Ranking [1] Summary
Nov 15 (Presentation 2) Keyvan Golestan Decentralised Data Fusion: A Graphical Model Approach [2] Summary
Nov 17 (Presentation 1) Venkata Manem Quantifying cancer progression with conjunctive Bayesian networks. [3] Summary
Nov 17 (Presentation 2) Mohammad Rostami Compressed Sensing Reconstruction via Belief Propagation [4] Summary
Nov 22 (Presentation 1) Mazen A. Melibari An HDP-HMM for Systems with State Persistence [5] Summary
Nov 22 (Presentation 2) Tameem Adel Graphical Models for Structured Classification, with an Application to Interpreting Images of Protein Sub-cellular Location Patterns [6] Summary
Nov 24 (Presentation 1) Pouria Fewzee Context Adaptive Training with Factorized Decision Trees for HMM-Based Speech Synthesis [7] Summary
Nov 24 (Presentation 2) Ali-Akbar Samadani Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains [8] Summary
Nov 29 (Presentation 1) Hojatollah Yeganeh Markov Random Fields for Super-Resolution [9] Summary
Nov 29 (Presentation 2) Areej Alhothali Video-based face recognition using adaptive hidden markov models [10] Summary