Alexander Ihler
About
In The Last Decade
Alexander Ihler
104 papers receiving 3.5k citations
Hit Papers
Peers
Comparison fields: 5 of 158
- Artificial Intelligence 1.6k
- Computer Networks and Communications 921
- Electrical and Electronic Engineering 631
- Computer Vision and Pattern Recognition 529
- Signal Processing 298
Countries citing papers authored by Alexander Ihler
This map shows the geographic impact of Alexander Ihler'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 Alexander Ihler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander Ihler more than expected).
Fields of papers citing papers by Alexander Ihler
This network shows the impact of papers produced by Alexander Ihler. 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 Alexander Ihler. The network helps show where Alexander Ihler may publish in the future.
Co-authorship network of co-authors of Alexander Ihler
This figure shows the co-authorship network connecting the top 25 collaborators of Alexander Ihler. A scholar is included among the top collaborators of Alexander Ihler based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Alexander Ihler. Alexander Ihler is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 10 | |
| 4 | Empirical Study of MC-Dropout in Various Astronomical Observing Conditions. | 2 |
| 5 | Join Graph Decomposition Bounds for Influence Diagrams | 2 |
| 6 | ContextNet: Deep learning for Star Galaxy Classification | 3 |
| 7 | Finite-sample Bounds for Marginal MAP | 0 |
| 8 | Dynamic Importance Sampling for Anytime Bounds of the Partition Function | 7 |
| 9 | Pushing forward marginal map with best-first search | 6 |
| 10 | Incremental region selection for mini-bucket elimination bounds | 1 |
| 11 | Probabilistic variational bounds for graphical models | 8 |
| 12 | Variational Planning for Graph-based MDPs | 11 |
| 13 | 185 | |
| 14 | A cluster-cumulant expansion at the fixed points of belief propagation | 3 |
| 15 | Variational algorithms for marginal MAP | 21 |
| 16 | Particle Filtered MCMC-MLE with Connections to Contrastive Divergence | 11 |
| 17 | Learning with Blocks: Composite Likelihood and Contrastive Divergence | 24 |
| 18 | Particle Belief Propagation | 74 |
| 19 | Accuracy bounds for belief propagation | 15 |
| 20 | 188 |
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.