His primary areas of study are Artificial intelligence, Regional science, Recommender system, Collaborative filtering and Operations research. His Artificial intelligence study frequently draws parallels with other fields, such as Machine learning. His Machine learning research is multidisciplinary, incorporating perspectives in Latent Dirichlet allocation, Aggregate, Information retrieval and Bayesian probability.
His work on MovieLens as part of general Recommender system study is frequently linked to Matrix decomposition, therefore connecting diverse disciplines of science. His work on Cold start as part of his general Collaborative filtering study is frequently connected to Predictive power and Task, thereby bridging the divide between different branches of science. Scott Sanner combines subjects such as Applications of artificial intelligence and Music and artificial intelligence with his study of Operations research.
The scientist’s investigation covers issues in Artificial intelligence, Mathematical optimization, Machine learning, Markov decision process and Dynamic programming. Scott Sanner has included themes like Collaborative filtering and Information retrieval in his Artificial intelligence study. The Bellman equation and Linear programming research Scott Sanner does as part of his general Mathematical optimization study is frequently linked to other disciplines of science, such as Influence diagram and Piecewise linear function, therefore creating a link between diverse domains of science.
Scott Sanner has researched Machine learning in several fields, including Class and Bayesian probability. His research on Markov decision process also deals with topics like
Scott Sanner mainly focuses on Artificial intelligence, Machine learning, Task, Deep learning and Artificial neural network. His Artificial intelligence study integrates concerns from other disciplines, such as Collaborative filtering and Recommender system. His studies in Machine learning integrate themes in fields like Class, State, Class, Mixture model and Data stream.
His work deals with themes such as Linear programming, Mathematical optimization, Set and Overhead, which intersect with Artificial neural network. His Mathematical optimization study combines topics in areas such as Bayesian inference and Nonlinear system. His Autoencoder research includes themes of Social media, Similarity and Data science.
Scott Sanner spends much of his time researching Artificial intelligence, Deep learning, Artificial neural network, Recommender system and Constraint. The Artificial intelligence study combines topics in areas such as Machine learning, Key and Collaborative filtering. His work on Classifier as part of general Machine learning research is often related to Contextual image classification, thus linking different fields of science.
The study incorporates disciplines such as Stability, Continual learning and Software engineering in addition to Deep learning. Many of his research projects under Artificial neural network are closely connected to Graphics, Lab-on-a-chip and Biotechnology with Graphics, Lab-on-a-chip and Biotechnology, tying the diverse disciplines of science together. Information retrieval covers he research in Recommender system.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
AutoRec: Autoencoders Meet Collaborative Filtering
Suvash Sedhain;Aditya Krishna Menon;Scott Sanner;Lexing Xie.
the web conference (2015)
Improving LDA topic models for microblogs via tweet pooling and automatic labeling
Rishabh Mehrotra;Scott Sanner;Wray Buntine;Lexing Xie.
international acm sigir conference on research and development in information retrieval (2013)
Towards object mapping in non-stationary environments with mobile robots
R. Biswas;B. Limketkai;S. Sanner;S. Thrun.
intelligent robots and systems (2002)
The 2014 International Planning Competition: Progress and Trends
Mauro Vallati;Lukás Chrpa;Marek Grzes;Thomas Leo McCluskey.
Ai Magazine (2015)
A Survey of the Seventh International Planning Competition
Amanda Coles;Andrew Coles;Angel García Olaya;Sergio Jiménez.
Ai Magazine (2012)
Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity
Marian-Andrei Rizoiu;Lexing Xie;Scott Sanner;Manuel Cebrian.
the web conference (2017)
Practical solution techniques for first-order MDPs
Scott Sanner;Craig Boutilier.
Artificial Intelligence (2009)
Algorithms for Direct 01 Loss Optimization in Binary Classification
Tan Nguyen;Scott Sanner.
international conference on machine learning (2013)
Social collaborative filtering for cold-start recommendations
Suvash Sedhain;Scott Sanner;Darius Braziunas;Lexing Xie.
conference on recommender systems (2014)
Affine algebraic decision diagrams (AADDs) and their application to structured probabilistic inference
Scott Sanner;David McAllester.
international joint conference on artificial intelligence (2005)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: