Demo Data from VLDB 2026 / reviewers in the wild / expert
Denver Dash
dblp:19/1167
· DBLP profile ↗
17ranked-venue papers
8as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 14 · 8 first-authorGraphics, computer vision, multimedia, augmented reality and games · 6 · 2 first-authorHuman-computer interaction and ubiquitous computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
5 papers |
3D vision · 60% Knowledge representation and reasoning · 23% Probabilistic and Bayesian machine learning · 17% | |
| Network and information security
1 paper |
Network security · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Distributed systems · 100% |
Topics — the 13 heaviest of 14, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
3d scene understanding |
0.4 | 1 | 2020 | SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans · ECCV (22) 2020 |
Computer vision › 3D vision › 3d reconstruction › multimodal 3d reconstruction
RGB-D reconstruction |
0.4 | 1 | 2020 | SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans · ECCV (22) 2020 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
causal reasoning |
0.2 | 2 | 2013 | Sequences of Mechanisms for Causal Reasoning in Artificial Intelligence · IJCAI 2013 A note on the correctness of the causal ordering algorithm · Artif. Intell. 2008 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › causal reasoning
causal ordering |
0.1 | 1 | 2008 | A note on the correctness of the causal ordering algorithm · Artif. Intell. 2008 |
Network security › intrusion detection and prevention
intrusion detection |
0.1 | 1 | 2006 | When Gossip is Good: Distributed Probabilistic Inference for Detection of Slow Network Intrusions · AAAI 2006 |
Network security › intrusion detection and prevention › intrusion detection
network intrusion detection |
0.1 | 1 | 2006 | When Gossip is Good: Distributed Probabilistic Inference for Detection of Slow Network Intrusions · AAAI 2006 |
Distributed systems › distributed machine learning
distributed inference |
0.1 | 1 | 2006 | When Gossip is Good: Distributed Probabilistic Inference for Detection of Slow Network Intrusions · AAAI 2006 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference › bayesian model selection › model averaging
bayesian model averaging |
0.0 | 1 | 2004 | Model Averaging for Prediction with Discrete Bayesian Networks · J. Mach. Learn. Res. 2004 |
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models
bayesian network |
0.0 | 1 | 2004 | Model Averaging for Prediction with Discrete Bayesian Networks · J. Mach. Learn. Res. 2004 |
Machine learning › Probabilistic and Bayesian machine learning › structured models
graphical models |
0.0 | 1 | 2004 | Model Averaging for Prediction with Discrete Bayesian Networks · J. Mach. Learn. Res. 2004 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference
bayesian classification |
0.0 | 1 | 2002 | Exact model averaging with naive Bayesian classifiers · ICML 2002 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference › bayesian model selection
model averaging |
0.0 | 1 | 2002 | Exact model averaging with naive Bayesian classifiers · ICML 2002 |
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models › bayesian network › bayesian network classifiers
naive bayes |
0.0 | 1 | 2002 | Exact model averaging with naive Bayesian classifiers · ICML 2002 |
Methods — techniques the papers use, named apart from their topics
object alignment prediction · 0.4layout prediction · 0.4causal ordering algorithm · 0.2probabilistic inference · 0.1gossip protocol · 0.1marginal likelihood · 0.0bayesian model averaging · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans
Armen Avetisyan, Tatiana Khanova, Christopher B. Choy, Denver Dash, Angela Dai, Matthias Nießner |
ECCV (22) | 4 |
| 2015 | Opportunities and Challenges of Industry-Academic Collaborations in Multimedia ResearchabstractThis ACM MM panel aims to redefine the state of research between Academia and Industry. Shih-Fu Chang, Matthew Cooper 0002, Denver Dash, Funda Kivran-Swaine, Li-Jia Li 0001, David A. Shamma |
ACM Multimedia | 3 |
| 2014 | Physical querying with multi-modal sensingabstractWe present Marvin, a system that can search physical objects using a mobile or wearable device. It integrates HOG-based object recognition, SURF-based localization information, automatic speech recognition, and user feedback information with a probabilistic model to recognize the “object of interest” at high accuracy and at interactive speeds. Once the object of interest is recognized, the information that the user is querying, e.g. reviews, options, etc., is displayed on the user's mobile or wearable device. We tested this prototype in a real-world retail store during business hours, with varied degree of background noise and clutter. We show that this multi-modal approach achieves superior recognition accuracy compared to using a vision system alone, especially in cluttered scenes where a vision system would be unable to distinguish which object is of interest to the user without additional input. It is computationally able to scale to large numbers of objects by focusing compute-intensive resources on the objects most likely to be of interest, inferred from user speech and implicit localization information. We present the system architecture, the probabilistic model that integrates the multi-modal information, and empirical results showing the benefits of multi-modal integration. Iljoo Baek, Taylor Stine, Denver Dash, Fanyi Xiao, Yaser Sheikh, Yair Movshovitz-Attias, Martial Hebert, Takeo Kanade |
WACV | 3 |
| 2013 | Sequences of Mechanisms for Causal Reasoning in Artificial Intelligence
Denver Dash, Mark Voortman, Martijn de Jongh |
IJCAI | 1 |
| 2010 | Learning Why Things Change: The Difference-Based Causality Learner
Mark Voortman, Denver Dash, Marek J. Druzdzel |
UAI | 2 |
| 2010 | Machine learning algorithms for event detection
Dragos D. Margineantu, Weng-Keen Wong, Denver Dash |
Mach. Learn. | 3 |
| 2008 | Efficient Inference in Persistent Dynamic Bayesian Networks
Tomás Singliar, Denver Dash |
UAI | 2 |
| 2008 | A note on the correctness of the causal ordering algorithm
Denver Dash, Marek J. Druzdzel |
Artif. Intell. | 1 |
| 2007 | COD: Online Temporal Clustering for Outbreak Detection
Tomás Singliar, Denver Dash |
AAAI | 2 |
| 2006 | When Gossip is Good: Distributed Probabilistic Inference for Detection of Slow Network Intrusions
Denver Dash, Branislav Kveton, John Mark Agosta, Eve M. Schooler, Jaideep Chandrashekar, Abraham Bachrach, Alex Newman |
AAAI | 1 |
| 2004 | Bayesian Biosurveillance of Disease Outbreaks
Gregory F. Cooper, Denver Dash, John D. Levander, Weng-Keen Wong, William R. Hogan, Michael M. Wagner 0001 |
UAI | 2 |
| 2004 | Model Averaging for Prediction with Discrete Bayesian Networks
Denver Dash, Gregory F. Cooper |
J. Mach. Learn. Res. | 1 |
| 2003 | Robust Independence Testing for Constraint-Based Learning of Causal Structure
Denver Dash, Marek J. Druzdzel |
UAI | 1 |
| 2002 | Exact model averaging with naive Bayesian classifiers
Denver Dash, Gregory F. Cooper |
ICML | 1 |
| 2002 | A method for evaluating elicitation schemes for probabilistic modelsabstractWe present an objective approach for evaluating probability and structure elicitation methods in probabilistic models. The main idea is to use the model derived from the experts' experience rather than the true model as the standard to compare the elicited model. We describe a general procedure by which it is possible to capture the data corresponding to the expert's beliefs, and we present a simple experiment in which we utilize this technique to compare three methods for eliciting discrete probabilities: 1) direct numerical assessment, 2) the probability wheel, and 3) the scaled probability bar. We show that for our domain, the scaled probability bar is the most effective tool for probability elicitation. Haiqin Wang, Denver Dash, Marek J. Druzdzel |
IEEE Trans. Syst. Man Cybern. Part B | 2 |
| 2001 | Caveats for Causal Reasoning with Equilibrium Models
Denver Dash, Marek J. Druzdzel |
ECSQARU | 1 |
| 1999 | A Hybrid Anytime Algorithm for the Construction of Causal Models From Sparse Data
Denver Dash, Marek J. Druzdzel |
UAI | 1 |