Eric B. Baum

4.1k total citations · 1 hit paper
41 papers, 2.5k citations indexed

About

Eric B. Baum is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Signal Processing. According to data from OpenAlex, Eric B. Baum has authored 41 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 12 papers in Computational Theory and Mathematics and 5 papers in Signal Processing. Recurrent topics in Eric B. Baum's work include Machine Learning and Algorithms (8 papers), Neural Networks and Applications (7 papers) and Computability, Logic, AI Algorithms (7 papers). Eric B. Baum is often cited by papers focused on Machine Learning and Algorithms (8 papers), Neural Networks and Applications (7 papers) and Computability, Logic, AI Algorithms (7 papers). Eric B. Baum collaborates with scholars based in United States and Hong Kong. Eric B. Baum's co-authors include David Haussler, Frank Wilczek, Dan Boneh, Charles Garrett, John Moody, Kevin Lang, Richard B. Lipton, Gary William Flake, Warren D. Smith and Yuh‐Dauh Lyuu and has published in prestigious journals such as Physical Review Letters, Nuclear Physics B and Physics Letters B.

In The Last Decade

Eric B. Baum

40 papers receiving 2.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

What Size Net Gives Valid Generalization?

1989 1.1k citations Eric B. Baum et al. Neural Computation profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Eric B. Baum United States	19	1.6k	408	281	247	204	41	2.5k
G. Tesauro United States	15	1.1k 0.7×	230 0.6×	129 0.5×	159 0.6×	160 0.8×	22	2.2k
Barnabás Póczos United States	29	1.5k 0.9×	736 1.8×	181 0.6×	144 0.6×	240 1.2×	120	3.3k
David Barber United Kingdom	22	1.5k 0.9×	477 1.2×	127 0.5×	336 1.4×	153 0.8×	87	2.8k
Witold Kinsner Canada	24	1.2k 0.8×	536 1.3×	302 1.1×	417 1.7×	302 1.5×	295	2.4k
P. A. Estévez Chile	28	1.4k 0.9×	923 2.3×	177 0.6×	243 1.0×	358 1.8×	131	3.5k
David Saad United Kingdom	25	1.1k 0.7×	229 0.6×	252 0.9×	149 0.6×	378 1.9×	141	2.2k
Marcus Frean New Zealand	22	1.2k 0.7×	389 1.0×	147 0.5×	155 0.6×	117 0.6×	70	2.4k
Peter Cheeseman United States	16	1.6k 1.0×	1.2k 2.9×	335 1.2×	232 0.9×	603 3.0×	40	3.8k
Joan Bruna United States	17	1.4k 0.9×	1.4k 3.4×	219 0.8×	144 0.6×	204 1.0×	47	3.7k
Karol Gregor United States	13	1.5k 0.9×	1.6k 3.8×	114 0.4×	209 0.8×	259 1.3×	21	3.8k

Countries citing papers authored by Eric B. Baum

Since Specialization

Citations

This map shows the geographic impact of Eric B. Baum'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 Eric B. Baum with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric B. Baum more than expected).

Fields of papers citing papers by Eric B. Baum

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eric B. Baum. 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 Eric B. Baum. The network helps show where Eric B. Baum may publish in the future.

Co-authorship network of co-authors of Eric B. Baum

This figure shows the co-authorship network connecting the top 25 collaborators of Eric B. Baum. A scholar is included among the top collaborators of Eric B. Baum 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 Eric B. Baum. Eric B. Baum is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Baum, Eric B., Marcus Hütter, & Emanuel Kitzelmann. (2010). Artificial general intelligence: Proceedings of the Third Conference on Artificial General Intelligence, AGI 2010, Lugano, Switzerland, March 5-8, 2010. 2 indexed citations

2.

Baum, Eric B.. (2007). A Working Hypothesis for General Intelligence. 55–74. 2 indexed citations

3.

Flake, Gary William & Eric B. Baum. (2002). Rush Hour is PSPACE-complete, or “Why you should generously tip parking lot attendants”. Theoretical Computer Science. 270(1-2). 895–911. 40 indexed citations

4.

Landweber, Laura F., et al.. (1999). DNA based computers II : DIMACS workshop, June 10-12, 1996. American Mathematical Society eBooks. 2 indexed citations

5.

Baum, Eric B. & Warren D. Smith. (1997). A Bayesian approach to relevance in game playing. Artificial Intelligence. 97(1-2). 195–242. 31 indexed citations

6.

Baum, Eric B.. (1996). Toward a model of mind as a laissez-faire economy of idiots. International Conference on Machine Learning. 28–36. 5 indexed citations

7.

Lipton, Richard J., et al.. (1996). DNA based computers : proceedings of a DIMACS workshop, April 4, 1995, Princeton University. American Mathematical Society eBooks. 6 indexed citations

8.

Lipton, Richard B. & Eric B. Baum. (1996). DNA Based Computers. 50 indexed citations

9.

Baum, Eric B.. (1992). On optimal game tree propagation for imperfect players. National Conference on Artificial Intelligence. 507–512. 5 indexed citations

10.

Baum, Eric B. & Yuh‐Dauh Lyuu. (1991). The Transition to Perfect Generalization in Perceptrons. Neural Computation. 3(3). 386–401. 23 indexed citations

11.

Baum, Eric B.. (1991). Neural net algorithms that learn in polynomial time from examples and queries. IEEE Transactions on Neural Networks. 2(1). 5–19. 109 indexed citations

12.

Baum, Eric B. & Kevin Lang. (1990). Constructing Hidden Units using Examples and Queries. Neural Information Processing Systems. 3. 904–910. 52 indexed citations

13.

Baum, Eric B.. (1990). The Perceptron Algorithm is Fast for Nonmalicious Distributions. Neural Computation. 2(2). 248–260. 49 indexed citations

14.

Baum, Eric B.. (1990). On learning a union of half spaces. Journal of Complexity. 6(1). 67–101. 51 indexed citations

15.

Baum, Eric B., John Moody, & Frank Wilczek. (1988). Internal representations for associative memory. Biological Cybernetics. 59(4-5). 217–228. 91 indexed citations

16.

Baum, Eric B.. (1988). On the capabilities of multilayer perceptrons. Journal of Complexity. 4(3). 193–215. 243 indexed citations

17.

Baum, Eric B. & Frank Wilczek. (1987). Supervised Learning of Probability Distributions by Neural Networks. Neural Information Processing Systems. 52–61. 99 indexed citations

18.

Aschbacher, Michael, Pierre Baldi, Eric B. Baum, & Richard M. Wilson. (1987). Embeddings of Ultrametric Spaces in Finite Dimensional Structures. SIAM Journal on Algebraic and Discrete Methods. 8(4). 564–577. 9 indexed citations

19.

Baum, Eric B.. (1986). Towards practical ‘neural’ computation for combinatorial optimization problems. AIP conference proceedings. 151. 53–58. 26 indexed citations

20.

Baum, Eric B.. (1982). Zeta Function Renormalization and Quantum Gravity.. PhDT. 1 indexed citations

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.

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