---
title: "Montreal — research institutions and their papers"
description: "Montreal (Canada): 7 institutions with papers on Elamur Research — 5 universities, 1 research institute, 1 company — and 100,848 citations of their works."
canonical: "https://research.elamur.ai/free/en/agglomerations/montreal/"
---
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# Montreal

Agglomeration [Canada](https://md.research.elamur.ai/free/en/countries/canada/) · metro population 4,243,910 (functional urban area, GHSL 2015)

**7** institutions with a page**75** works in our library**100,848** citations of their works

## About

### Profile

Montreal is a city region in [Canada](https://md.research.elamur.ai/free/en/countries/canada/) and one of the urban centres through which this library groups research institutions by place. Its institutions on this site fall into three kinds. Most are universities. Alongside them stand a research institute devoted to artificial intelligence and the Montreal research arm of a technology company. Together they form a compact research hub whose works in the library have drawn a large body of citations, though that attention is spread very unevenly across the institutions.

The institutions grouped under Montreal are [Université de Montréal](https://md.research.elamur.ai/free/en/universities/universite-de-montreal/), [McGill University](https://md.research.elamur.ai/free/en/universities/mcgill-university/), [Mila - Quebec Artificial Intelligence Institute](https://md.research.elamur.ai/free/en/labs/mila-quebec-artificial-intelligence-institute/), [HEC Montréal](https://md.research.elamur.ai/free/en/universities/hec-montreal/), [Polytechnique Montréal](https://md.research.elamur.ai/free/en/universities/polytechnique-montreal/), [Microsoft Research Montréal](https://md.research.elamur.ai/free/en/companies/microsoft-research-montreal/) and [Concordia University](https://md.research.elamur.ai/free/en/universities/concordia-university/). An institution belongs to the city region when its registered coordinates fall inside the urban centre or within a short distance of it. The regional view therefore pulls together organisations that publish under their own names, each with its own page in the library.

What the regional page offers is a map of who does research here and how much the library's readers and citing authors have drawn on it. It does not list individual works, scientists or topics at the level of the city. Those details sit on each institution's page, and a reader who wants the papers themselves should go there. What follows describes the shape of Montreal's research as the library shows it: which institutions carry the weight, how the different kinds of institution sit beside one another, and what can be said about each.

### The city as a science hub

Seen through its institutions on this site, Montreal is a city of universities first. Most of the organisations grouped here are universities, and they account for most of the works the library holds for the region. Within that academic core one institution stands well ahead of the rest in citations. The others range from large bodies of work with modest citation totals to small sets of papers that have been cited heavily.

The second element of the hub is a research institute whose name states its field outright: artificial intelligence. The Mila - Quebec Artificial Intelligence Institute is the only organisation in the regional group classed as a research institute rather than a university or a company. On this site it ranks second in the city by citations. That gives the region a visible concentration in one field, held by an organisation built around it.

The third element is industry. Microsoft Research Montréal is the only company in the group. Its presence means that corporate research appears in the library's picture of Montreal beside academic research, though on a much smaller scale among the works here. A city whose research on this site is mostly academic also has a corporate laboratory publishing papers under its own name.

The overall picture is concentrated. Citations of Montreal's works in our library come largely from a single university. A second large share comes from the artificial intelligence institute, and a third, more surprising one comes from a university with very few works in the library. The remaining institutions add steady but smaller contributions.

### Leading institutions

The regional page lists the institutions with the most cited first, and in that list Université de Montréal comes first by a wide margin. It holds the largest set of works for the city on this site, and citations of its works make up the greater part of the regional total. Whatever fields its papers cover, and the institution's own page is where those can be seen, Université de Montréal is the centre of gravity of Montreal research as the library presents it. It is the place to start for anyone looking for the region's most cited papers.

McGill University comes second in the page's listing. It holds the second-largest body of works among Montreal's institutions here, close behind Université de Montréal in volume. Its citation total, though substantial, is far below that of Université de Montréal and also below the artificial intelligence institute. McGill's place in the library is that of a broad producer of research. Its weight lies in the size of its output on this site rather than in a few heavily cited papers.

The Mila - Quebec Artificial Intelligence Institute holds fewer works here than either of the two large universities. Yet its citation total is second only to Université de Montréal's. Each of its works has, on average, drawn far more attention than a typical McGill work. For a field-specific institute this marks a clear strength: a focused programme in artificial intelligence whose papers in our library are widely cited.

HEC Montréal shows the sharpest contrast between volume and attention in the region. It is represented in the library by only a very small number of works, yet their citations put it third in the city, ahead of McGill University. One or both of its papers here must therefore be among the most cited works connected to Montreal on this site. Readers interested in the region's high-impact papers should look at its page, even though its presence here is slight in size.

Polytechnique Montréal, Microsoft Research Montréal and Concordia University complete the group. Each holds a small set of works in our library. Polytechnique Montréal and Microsoft Research Montréal have similar, modest citation totals. Concordia University's total is the smallest of the seven. These institutions add range to the regional picture, an engineering school's name, a corporate lab and another university, without changing its overall balance.

### Research profile

The regional page does not break Montreal's research down by topic, so a field-by-field account has to rest on what the institutions themselves indicate. One field can be named with confidence: artificial intelligence, the declared subject of the Mila - Quebec Artificial Intelligence Institute. Its works here are among the most cited in the city, which makes artificial intelligence the clearest identifiable strength of Montreal on this site.

The presence of Microsoft Research Montréal points the same way in general terms, as a computing and technology research setting. The company's lab sits alongside the artificial intelligence institute in a region whose most visible specialised organisation works on machine intelligence. The library's regional view, however, does not say which topics the company's works address. Their content should be read from the company's own page.

Beyond these two, the universities are general institutions whose fields the regional page does not set out. Université de Montréal and McGill University, with the largest bodies of work, are likely to cover many subjects. The library's city-level view does not say which, and any account of their strengths by discipline belongs to their individual pages. The same is true of HEC Montréal, whose small set of highly cited works is the most striking single feature of the region's citation profile. Polytechnique Montréal and Concordia University likewise contribute works whose topics are listed on their own pages rather than here.

The research profile of Montreal on this site is thus best described by its structure. A broad academic base is led by one heavily cited university. A specialised artificial intelligence institute stands out for the attention its works receive. A business-named university punches far above its size in citations. A corporate lab and several smaller academic contributors round out the hub.

### Landmark works

The regional page does not list individual works, so the region's landmark papers have to be found through its institutions. The citation totals show where to look. Université de Montréal, with by far the largest citation total, almost certainly holds the most cited works connected to the city on this site. The Mila - Quebec Artificial Intelligence Institute, with the second-largest total from a smaller set of works, is the next place to look. Its most cited papers are the landmark works of artificial intelligence research in Montreal as the library shows it.

HEC Montréal deserves special mention here. Because its citations come from so few works, its page leads almost directly to some of the most cited single papers in the region. For a reader trying to identify Montreal's high-impact research in the library, these three institutions, Université de Montréal, the artificial intelligence institute and HEC Montréal, are where the landmark works will be found.

### Labs and units

The units the regional page lists are the city's institutions themselves. Among them, the Mila - Quebec Artificial Intelligence Institute is the one the library classes as a research institute, a lab-type organisation distinct from the universities. Microsoft Research Montréal is classed as a company, a corporate research lab. The remaining five are universities: Université de Montréal, McGill University, HEC Montréal, Polytechnique Montréal and Concordia University.

The regional page does not list departments, faculties or research groups inside these institutions. Where the library records such units, they appear on the pages of the institutions to which they belong.

### Collaborations

The library's regional page does not show co-authorship between Montreal's institutions, nor their links to institutions in other cities or countries. Any account of collaboration must therefore stay close to what the institutional mix itself shows, and readers should look to the individual pages for the partners named on specific works.

What the mix does show is the setting in which links between academia and industry could form. A corporate research lab, Microsoft Research Montréal, works in the same city as several universities and an artificial intelligence institute. The institute's field and the company lab's computing orientation place them close to one another in subject. On this site, though, the regional view does not show joint works between them. Whether particular papers connect the company with the universities or with the institute is a question for the works listed on each organisation's page.

Within the academic sector, the city brings together several universities of different kinds. There are two large general institutions, a university bearing the name of a business school, an engineering school and another general university. A research institute in artificial intelligence works among them. That density of institutions in one urban centre is the basic fact of Montreal as a science hub in this library. The papers on each page show how often their authors cross institutional lines.

All of Montreal's institutions here sit within Canada, and the city's page links back to the country, where Montreal can be seen alongside the other Canadian city regions and institutions the library covers.

### How the picture fits together

Taken as a whole, Montreal's research on this site combines breadth and focus. Breadth comes from the universities, above all Université de Montréal and McGill University, which between them hold most of the region's works in the library. Focus comes from the Mila - Quebec Artificial Intelligence Institute, whose specialisation is plain from its name and whose papers here draw heavy citation. HEC Montréal adds a third kind of strength: small output with very high attention. Polytechnique Montréal, Concordia University and Microsoft Research Montréal contribute smaller bodies of work that widen the range of institutions without shifting the balance.

The regional page gives no years, so no history or timeline of Montreal's research can be drawn from it. When the region's research begins in the library, and how its fields came and grew, can be traced only through the dated works on each institution's page. The same holds for the region's scientists, who are listed under their institutions rather than at city level.

Several practical points follow for students, researchers and journalists using the library. For Montreal's most cited research, start with Université de Montréal. For artificial intelligence, the city's clearest field, go to the Mila - Quebec Artificial Intelligence Institute. For a small number of exceptionally cited papers, look at HEC Montréal. For the largest volume of work after Université de Montréal, look at McGill University. For industry research in the city, the place is Microsoft Research Montréal. And for engineering and the wider university landscape, Polytechnique Montréal and Concordia University complete the view.

## Institutions (7)

5 universities, 1 research institute, 1 company.

### 🎓 Universities (5)

1. **[Université de Montréal](https://md.research.elamur.ai/free/en/universities/universite-de-montreal/)**University · Montreal, [Canada](https://md.research.elamur.ai/free/en/countries/canada/)*42 works · 71,559 citations*
2. **[McGill University](https://md.research.elamur.ai/free/en/universities/mcgill-university/)**University · Montreal, [Canada](https://md.research.elamur.ai/free/en/countries/canada/)*30 works · 6,634 citations*
3. **[HEC Montréal](https://md.research.elamur.ai/free/en/universities/hec-montreal/)**University · Montreal, [Canada](https://md.research.elamur.ai/free/en/countries/canada/)*2 works · 9,514 citations*
4. **[Polytechnique Montréal](https://md.research.elamur.ai/free/en/universities/polytechnique-montreal/)**University · Montreal, [Canada](https://md.research.elamur.ai/free/en/countries/canada/)*4 works · 594 citations*
5. **[Concordia University](https://md.research.elamur.ai/free/en/universities/concordia-university/)**University · Montreal, [Canada](https://md.research.elamur.ai/free/en/countries/canada/)*4 works · 278 citations*

### 🔬 Labs and research institutes (1)

1. **[Mila - Quebec Artificial Intelligence Institute](https://md.research.elamur.ai/free/en/labs/mila-quebec-artificial-intelligence-institute/)**Research institute · Montreal, [Canada](https://md.research.elamur.ai/free/en/countries/canada/)*20 works · 11,663 citations*

### 🏢 Companies (1)

1. **[Microsoft Research Montréal](https://md.research.elamur.ai/free/en/companies/microsoft-research-montreal/)**Company · Montreal, [Canada](https://md.research.elamur.ai/free/en/countries/canada/)*4 works · 606 citations*

## Works in our library (75)

Works of its institutions, the most cited first.

1. [**Neural Machine Translation by Jointly Learning to Align and Translate**Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio*2014 · 18,908 citations*](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/)
2. [**Graph Attention Networks**Petar Veličkovic, Guillem Cucurull, Arantxa Casanova, et al.*2017 · 8,996 citations*](https://md.research.elamur.ai/free/en/papers/veli-vckovic-2017-graph-attention-networks/)
3. [**On the Properties of Neural Machine Translation: Encoder-Decoder Approaches**Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, et al.*2014 · 7,734 citations*](https://md.research.elamur.ai/free/en/papers/cho-2014-properties-neural-machine-translation-encoder/)
4. [**Show, Attend and Tell: Neural Image Caption Generation with Visual Attention**Kelvin Xu, Jimmy Ba, Ryan Kiros, et al.*2015 · 7,483 citations*](https://md.research.elamur.ai/free/en/papers/xu-2015-show-attend-tell-neural-image/)
5. [**Intriguing properties of neural networks**Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, et al.*2013 · 5,870 citations*](https://md.research.elamur.ai/free/en/papers/szegedy-2013-intriguing-properties-neural-networks/)
6. [**HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering**Zhilin Yang, Peng Qi, Saizheng Zhang, et al.*2018 · 3,489 citations*](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/)
7. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
8. [**Word Representations: A Simple and General Method for Semi-Supervised Learning**Joseph Turian, Lev-Arie Ratinov, Yoshua Bengio*2010 · 2,421 citations*](https://md.research.elamur.ai/free/en/papers/turian-2010-word-representations-simple-general-method/)
9. [**Personalizing Dialogue Agents: I have a dog, do you have pets too?**Saizheng Zhang, Emily Dinan, Jack Urbanek, et al.*2018 · 1,839 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2018-personalizing-dialogue-agents-i-have/)
10. [**Attention-Based Models for Speech Recognition**Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, et al.*2015 · 1,776 citations*](https://md.research.elamur.ai/free/en/papers/chorowski-2015-attention-based-models-speech-recognition/)
11. [**A Structured Self-attentive Sentence Embedding**Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, et al.*2017 · 1,773 citations*](https://md.research.elamur.ai/free/en/papers/lin-2017-structured-self-attentive-sentence-embedding/)
12. [**StereoSet: Measuring stereotypical bias in pretrained language models**Moin Nadeem, Anna Bethke, Siva Reddy*2021 · 1,131 citations*](https://md.research.elamur.ai/free/en/papers/nadeem-2021-stereoset-measuring-stereotypical-bias-pretrained/)
13. [**On Using Very Large Target Vocabulary for Neural Machine Translation**Sébastien Jean, Kyunghyun Cho, Roland Memisevic, et al.*2015 · 999 citations*](https://md.research.elamur.ai/free/en/papers/jean-2015-very-large-target-vocabulary-neural/)
14. [**A Survey of Data Augmentation Approaches for NLP**Steven Y. Feng, Varun Gangal, Jason Wei, et al.*2021 · 935 citations*](https://md.research.elamur.ai/free/en/papers/feng-2021-survey-data-augmentation-approaches-nlp/)
15. [**C-Pack: Packed Resources For General Chinese Embeddings**Shitao Xiao, Zheng Liu, Peitian Zhang, et al.*2023 · 920 citations*](https://md.research.elamur.ai/free/en/papers/xiao-2023-c-pack-packed-resources/)
16. [**Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism**Orhan Firat, Kyunghyun Cho, Yoshua Bengio*2016 · 821 citations*](https://md.research.elamur.ai/free/en/papers/firat-2016-multi-way-multilingual-neural-machine/)
17. [**HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models**Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, et al.*2023 · 646 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-halueval-large-scale-hallucination-evaluation/)
18. [**Pointing the Unknown Words**Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati, et al.*2016 · 642 citations*](https://md.research.elamur.ai/free/en/papers/gulcehre-2016-pointing-unknown-words/)
19. [**The Second Conversational Intelligence Challenge (ConvAI2)**Emily Dinan, Varvara Logacheva, Valentin Malykh, et al.*2019 · 525 citations*](https://md.research.elamur.ai/free/en/papers/dinan-2019-second-conversational-intelligence-challenge-convai2/)
20. [**Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models**Iulian V. Serban, Alessandro Sordoni, Yoshua Bengio, et al.*2015 · 524 citations*](https://md.research.elamur.ai/free/en/papers/serban-2015-building-end-end-dialogue-systems/)
21. [**A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues**Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, et al.*2016 · 518 citations*](https://md.research.elamur.ai/free/en/papers/serban-2016-hierarchical-latent-variable-encoder-decoder/)
22. [**Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses**Ryan Lowe, Michael Noseworthy, Iulian V. Serban, et al.*2017 · 486 citations*](https://md.research.elamur.ai/free/en/papers/lowe-2017-towards-automatic-turing-test-learning/)
23. [**The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems**Ryan Lowe, Nissan Pow, Iulian Serban, et al.*2015 · 474 citations*](https://md.research.elamur.ai/free/en/papers/lowe-2015-ubuntu-dialogue-corpus-large-dataset/)
24. [**A Character-level Decoder without Explicit Segmentation for Neural Machine Translation**Junyoung Chung, Kyunghyun Cho, Yoshua Bengio*2016 · 407 citations*](https://md.research.elamur.ai/free/en/papers/chung-2016-character-level-decoder-without-explicit/)
25. [**Long Range Arena: A Benchmark for Efficient Transformers**Yi Tay, Mostafa Dehghani, Samira Abnar, et al.*2020 · 396 citations*](https://md.research.elamur.ai/free/en/papers/tay-2020-long-range-arena-benchmark-efficient/)

[See all 75 works →](https://md.research.elamur.ai/free/en/agglomerations/montreal/works/)

## Works by year

### 2020s

[**2025** 6 works](https://md.research.elamur.ai/free/en/agglomerations/montreal/year/2025/) · [**2024** 9 works](https://md.research.elamur.ai/free/en/agglomerations/montreal/year/2024/) · [**2023** 7 works](https://md.research.elamur.ai/free/en/agglomerations/montreal/year/2023/) · [**2022** 7 works](https://md.research.elamur.ai/free/en/agglomerations/montreal/year/2022/) · [**2021** 6 works](https://md.research.elamur.ai/free/en/agglomerations/montreal/year/2021/) · [**2020** 4 works](https://md.research.elamur.ai/free/en/agglomerations/montreal/year/2020/)

### 2010s

[**2019** 4 works](https://md.research.elamur.ai/free/en/agglomerations/montreal/year/2019/) · [**2018** 6 works](https://md.research.elamur.ai/free/en/agglomerations/montreal/year/2018/) · [**2017** 4 works](https://md.research.elamur.ai/free/en/agglomerations/montreal/year/2017/) · [**2016** 9 works](https://md.research.elamur.ai/free/en/agglomerations/montreal/year/2016/) · [**2015** 6 works](https://md.research.elamur.ai/free/en/agglomerations/montreal/year/2015/) · [**2014** 3 works](https://md.research.elamur.ai/free/en/agglomerations/montreal/year/2014/) · [**2013** 1 work](https://md.research.elamur.ai/free/en/papers/szegedy-2013-intriguing-properties-neural-networks/) · [**2012** 1 work](https://md.research.elamur.ai/free/en/papers/bengio-2012-representation-learning-review-new-perspectives/) · [**2011** 1 work](https://md.research.elamur.ai/free/en/papers/charton-2011-poly-co-multilayer-perceptron-approach/) · [**2010** 1 work](https://md.research.elamur.ai/free/en/papers/turian-2010-word-representations-simple-general-method/)

## Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

💡 Concepts 5[Transformer and attention · 2 works](https://md.research.elamur.ai/free/en/concepts/transformer/) · [Language model · 1 work](https://md.research.elamur.ai/free/en/concepts/language-model/) · [Word embeddings · 1 work](https://md.research.elamur.ai/free/en/concepts/word-embeddings/) · [Distributional semantics · 1 work](https://md.research.elamur.ai/free/en/concepts/distributional-semantics/) · [Lexicography · 1 work](https://md.research.elamur.ai/free/en/concepts/lexicography/)

📚 Shelves 8[Other · 19 works](https://md.research.elamur.ai/free/en/shelves/other/) · [Natural language processing · 27 works](https://md.research.elamur.ai/free/en/shelves/nlp/) · [Linguistics & languages · 27 works](https://md.research.elamur.ai/free/en/shelves/linguistics/) · [AI & machine learning · 3 works](https://md.research.elamur.ai/free/en/shelves/ai/) · [Machine translation · 6 works](https://md.research.elamur.ai/free/en/shelves/machine-translation/) · [Mathematics & statistics · 2 works](https://md.research.elamur.ai/free/en/shelves/mathematics/) · [English · 11 works](https://md.research.elamur.ai/free/en/shelves/en/) · [Machine translation · 1 work](https://md.research.elamur.ai/free/en/shelves/mtrans/)

🧭 Topics 24[Machine translation · 7 works](https://md.research.elamur.ai/free/en/topics/machine-translation/) · [Dialogue systems · 9 works](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · [Language models & pretraining · 14 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Vision & language · 2 works](https://md.research.elamur.ai/free/en/topics/multimodal/) · [Question answering · 3 works](https://md.research.elamur.ai/free/en/topics/question-answering/) · [Speech & spoken language · 1 work](https://md.research.elamur.ai/free/en/topics/speech/) · [Coreference resolution · 6 works](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) · [Summarization · 3 works](https://md.research.elamur.ai/free/en/topics/summarization/) · [Evaluation & benchmarks · 7 works](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Multilingual & cross-lingual NLP · 4 works](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · [Bias, fairness & safety · 2 works](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Named entity recognition · 1 work](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/) · [Discourse analysis · 3 works](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) · [Efficient models & compression · 1 work](https://md.research.elamur.ai/free/en/topics/efficient-models/)+9 more

Agglomerations: the GHSL Urban Centre Database 2025; populations: the GHSL functional urban areas 2015 (urban centre and commuting zone), else the urban centre, as each page says (European Commission, Joint Research Centre, CC BY 4.0). An institution belongs to the urban centre its coordinates (ROR, OpenAlex) fall in, or one within 5 km. Land: Natural Earth.

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Updated 23 October 2026