Matt Hoffman

1.2k citations
14 papers · 310 · h-index 7

Impact in

Papers in

Matt Hoffman

14 papers receiving 282 citations

Peers

Matt Hoffman
Comparison fields: 5 of 56
  • Signal Processing 155
  • Computer Vision and Pattern Recognition 138
  • Artificial Intelligence 140
  • Cognitive Neuroscience 65
  • Computational Mathematics 2
Replace Dominik Roblek with:
Dominik Roblek United States
Donghong Han China
Raphaël Marinier United States
Graham E. Poliner United States
Nagendra Kumar United States
Matt Sharifi United States
Fang-Fei Kuo Taiwan
Yossi Adi Israel
Xiaoou Chen China
Nada Matic United States
Matt Hoffman relative to Dominik Roblek United States Dominik Roblek's profile →
Citations per field
00.5×10×14×
Dominik Roblek · 1×
Citations per year

Countries citing papers authored by Matt Hoffman

Since Specialization
Citations

This map shows the geographic impact of Matt Hoffman's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matt Hoffman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matt Hoffman more than expected).

Fields of papers citing papers by Matt Hoffman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matt Hoffman. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matt Hoffman. The network helps show where Matt Hoffman may publish in the future.

Co-authors

The 25 scholars most cited alongside Matt Hoffman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Matt Hoffman Line = papers co-authored together Matt Hoffman links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1
Music Transformer: Generating Music with Long-Term Structure
2019146
2
Sparse stochastic inference for latent Dirichlet allocation
201251
3 201640
4 201219
5
New inference strategies for solving Markov decision processes using reversible jump MCMC
200918
6 20159
7
Trans-dimensional MCMC for Bayesian policy learning
20078
8 20196
9
Scalable nonparametric Bayesian multilevel clustering
20163
10 20213
11 20093
12
Feature-Based Synthesis for Sonification and Psychoacoustic Research
20062
13 20071
14 20071

About Matt Hoffman

Matt Hoffman is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Statistics and Probability and Control and Systems Engineering, having authored 14 papers that have together received 310 indexed citations. Recurring topics across this work include Music and Audio Processing (4 papers), Music Technology and Sound Studies (4 papers), Gaussian Processes and Bayesian Inference (4 papers), Bayesian Methods and Mixture Models (3 papers), Speech and Audio Processing (3 papers), Markov Chains and Monte Carlo Methods (2 papers), Machine Learning and Algorithms (2 papers) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Signal Processing (155 citations), Computer Vision and Pattern Recognition (138 citations), Artificial Intelligence (140 citations), Cognitive Neuroscience (65 citations) and Computational Mathematics (2 citations). Matt Hoffman has collaborated with scholars based in United States, Canada and Australia. Frequent co-authors include David M. Blei, David Mimno, Noam Shazeer, Ashish Vaswani, Jakob Uszkoreit, Ian Simon, Cheng-Zhi Anna Huang, Monica Dinculescu, Curtis Hawthorne and Douglas Eck. Their work appears in journals such as International Journal of Sports Science & Coaching, Journal of the Association for Information Systems, Lecture notes in computer science, SMARTech Repository (Georgia Institute of Technology) and International Conference on Learning Representations.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact