What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
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.
His most cited work include:
- AutoRec: Autoencoders Meet Collaborative Filtering (495 citations)
- Improving LDA topic models for microblogs via tweet pooling and automatic labeling (293 citations)
- Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity (94 citations)
What are the main themes of his work throughout his whole career to date?
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
- Probabilistic logic that intertwine with fields like Domain,
- Set together with Relevance.
His Dynamic programming research is multidisciplinary, incorporating elements of Nonlinear programming and State.
He most often published in these fields:
- Artificial intelligence (33.33%)
- Mathematical optimization (31.61%)
- Machine learning (21.26%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (33.33%)
- Machine learning (21.26%)
- Task (8.62%)
In recent papers he was focusing on the following fields of study:
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.
Between 2018 and 2021, his most popular works were:
- Deep Learning with Microfluidics for Biotechnology. (62 citations)
- Comparison of machine learning models for occupancy prediction in residential buildings using connected thermostat data (21 citations)
- Noise Contrastive Estimation for One-Class Collaborative Filtering (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
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.
