Philip Bachman

4.8k total citations · 2 hit papers
15 papers, 1.5k citations indexed

About

Philip Bachman is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Philip Bachman has authored 15 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 2 papers in Molecular Biology. Recurrent topics in Philip Bachman's work include Generative Adversarial Networks and Image Synthesis (4 papers), Topic Modeling (4 papers) and Multimodal Machine Learning Applications (4 papers). Philip Bachman is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (4 papers), Topic Modeling (4 papers) and Multimodal Machine Learning Applications (4 papers). Philip Bachman collaborates with scholars based in Canada, United States and United Kingdom. Philip Bachman's co-authors include Doina Precup, Peter Henderson, Riashat Islam, Joëlle Pineau, David Meger, Adam Trischler, Alessandro Sordoni, Xingdi Yuan, Kaheer Suleman and Tong Wang and has published in prestigious journals such as Bioinformatics, arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

Philip Bachman

15 papers receiving 1.4k 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.

Deep Reinforcement Learning That Matters

2018 814 citations Peter Henderson, Riashat Islam et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →
NewsQA: A Machine Comprehension Dataset

2017 374 citations Adam Trischler, Tong Wang et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Philip Bachman Canada	9	1.0k	464	198	159	148	15	1.5k
Olivier Pietquin France	20	1.2k 1.2×	284 0.6×	312 1.6×	130 0.8×	150 1.0×	61	1.8k
Shixiang Gu United States	13	1.0k 1.0×	389 0.8×	508 2.6×	170 1.1×	155 1.0×	26	1.6k
Ziyu Wang China	7	742 0.7×	372 0.8×	244 1.2×	245 1.5×	229 1.5×	18	1.5k
Stefano Rovetta Italy	17	791 0.8×	670 1.4×	212 1.1×	212 1.3×	100 0.7×	103	1.6k
Adi Botea Ireland	19	758 0.7×	612 1.3×	149 0.8×	67 0.4×	359 2.4×	79	1.3k
Jiangtao Cui China	22	479 0.5×	520 1.1×	122 0.6×	195 1.2×	180 1.2×	117	1.5k
Mohammad Gheshlaghi Azar United Kingdom	10	825 0.8×	221 0.5×	241 1.2×	308 1.9×	264 1.8×	17	1.5k
Dan Horgan United Kingdom	5	983 1.0×	269 0.6×	392 2.0×	305 1.9×	291 2.0×	5	1.7k
Nantas Nardelli United Kingdom	5	746 0.7×	154 0.3×	211 1.1×	162 1.0×	320 2.2×	6	1.2k
Haobin Shi China	18	371 0.4×	433 0.9×	227 1.1×	101 0.6×	138 0.9×	91	1.0k

Countries citing papers authored by Philip Bachman

Since Specialization

Citations

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

Fields of papers citing papers by Philip Bachman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Philip Bachman. 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 Philip Bachman. The network helps show where Philip Bachman may publish in the future.

Co-authorship network of co-authors of Philip Bachman

This figure shows the co-authorship network connecting the top 25 collaborators of Philip Bachman. A scholar is included among the top collaborators of Philip Bachman 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 Philip Bachman. Philip Bachman 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:

15 of 15 papers shown

1.

Sordoni, Alessandro, Nouha Dziri, Hannes Schulz, et al.. (2021). Decomposed Mutual Information Estimation for Contrastive Representation Learning. arXiv (Cornell University). 9859–9869. 5 indexed citations

2.

Bachman, Philip, et al.. (2019). Learning Representations by Maximizing Mutual Information Across Views. Neural Information Processing Systems. 32. 15509–15519. 122 indexed citations

3.

Almahairi, Amjad, et al.. (2018). Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data. International Conference on Machine Learning. 195–204. 26 indexed citations

4.

Henderson, Peter, Riashat Islam, Philip Bachman, et al.. (2018). Deep Reinforcement Learning That Matters. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 814 indexed citations breakdown →

5.

Sharma, Shikhar, Jing He, Kaheer Suleman, Hannes Schulz, & Philip Bachman. (2017). Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data. arXiv (Cornell University). 3 indexed citations

6.

Trischler, Adam, Tong Wang, Xingdi Yuan, et al.. (2017). NewsQA: A Machine Comprehension Dataset. 191–200. 374 indexed citations breakdown →

7.

Bachman, Philip & Doina Precup. (2017). Variational Generative Stochastic Networks with Collaborative Shaping. arXiv (Cornell University). 1964–1972. 2 indexed citations

8.

Henderson, Peter, Riashat Islam, Philip Bachman, et al.. (2017). Deep Reinforcement Learning that Matters. arXiv (Cornell University). 32(1). 3207–3214. 111 indexed citations

9.

Dai, Zihang, Amjad Almahairi, Philip Bachman, Eduard Hovy, & Aaron Courville. (2017). Calibrating Energy-based Generative Adversarial Networks. arXiv (Cornell University). 5 indexed citations

10.

Trischler, Adam, Zheng Ye, Xingdi Yuan, Jing He, & Philip Bachman. (2016). A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data. 432–441. 27 indexed citations

11.

Bachman, Philip. (2016). An Architecture for Deep, Hierarchical Generative Models. arXiv (Cornell University). 29. 4826–4834. 12 indexed citations

12.

Bachman, Philip & Doina Precup. (2015). Data Generation as Sequential Decision Making. arXiv (Cornell University). 28. 3249–3257. 19 indexed citations

13.

Bachman, Philip, Amir‐massoud Farahmand, & Doina Precup. (2014). Sample-based approximate regularization. PolyPublie (École Polytechnique de Montréal). 1926–1934. 1 indexed citations

14.

Precup, Doina & Philip Bachman. (2012). Improved Estimation in Time Varying Models. arXiv (Cornell University). 1459–1466. 2 indexed citations

15.

Bachman, Philip & Ying Liu. (2009). Structure discovery in PPI networks using pattern-based network decomposition. Bioinformatics. 25(14). 1814–1821. 10 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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