Salesforce Research Deep Learning Grant

Connecting deep learning researchers with the world's smartest CRM.

Thank you for your interest in the Salesforce Research Deep Learning Grant. Our 2019 grant applications are now closed. Please check back on our website for more partnership opportunities or follow us on Twitter @SFResearch. We look forward to announcing our grant winners in early December 2019.

As we see great progress in deep learning models and applications, we're looking for diverse individuals with innovative ideas who can join us in shaping the future of AI.

Our Salesforce Research team is inviting submissions from university faculty, non-profit organizations, and NGOs to apply for our second annual Salesforce Research Deep Learning Grant. Our goal is to support individuals who extend, use or analyze deep learning methods or can provide fresh perspectives on current research. We encourage people to submit problems or research pertaining to our current research projects but it is not required. The purpose of the grant is to advance the state of the art in AI and create lasting relationships with our grant winners.

Salesforce will fund up to $50,000 USD, depending on the needs to support the research. We will fund up to five different proposals.

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  • AI for Good*
  • DecaNLP*
  • De-biasing Deep Learning Models
  • Democratizing Scalable Deep Learning
  • Efficient Training Algorithms
  • Ethics in AI
  • Explainable AI (XAI)
  • Few-Shot Learning
  • Lifelong Learning
  • Multi-Task Learning
  • Natural Language Processing
  • Reinforcement Learning
*We aim to have one winner with a proposal pertaining to each of these topics.


Permanent faculty members, non-profit organizations, and NGOs around the world.

The Grant

What are you looking for in a grant proposal?
- Proposal Title
- Contact Info: Name, Email Address, Phone Number, Postal Address
- Affiliation
- Job Title
- Homepage or website (optional)

Pages 1-2
- Abstract (which will be published to our public research site)
- Proposed research and the impact: detail your proposed test case, expected challenges and how you plan to mitigate them
- Notate contributions from prior work and citations (if applicable)
- Explain how this research would move the field forward
- Budget overview of how the grant funding would be applied, and if you currently have additional funding for this area of research

Page 3
Reference Page (if applicable)
Your proposed Salesforce Research partner or liaison (if applicable). Please view our recent publications here.

Does the grant need a mentor or leader?
Yes, your submission should have one lead who will act as the point of contact.
Can I submit more than one grant proposal?
Who should write the grant?
The people applying and/or a grant writer from your organization.
Can my proposal include the 3rd page for references?
Who will own the intellectual property of the research conducted using the grant?
The Salesforce Research Deep Learning Grant is not subject to any intellectual property (IP) restrictions. IP developed solely by the grant recipient will be owned by the grant recipient or his/her organization. If the grant recipient uses the grant to conduct joint research with Salesforce, IP that is jointly developed with Salesforce will be subject to the terms of a separate research collaboration agreement.
How will grants be judged and who will review the proposals?
Grants will be judged based on the specifics noted above, the quality of the proposed research and how closely it matches the research we are currently doing at Salesforce. We will also be looking for diversity in backgrounds and areas of interest to those of the other grant winners. The proposals will be reviewed and judged by a panel of research scientists, managers and directors within the Salesforce Research team.
When will I be notified of the acceptance/decline of my proposal?
We expect to notify grant applicants by early December.

The Funding

What can the funding be used for?
The funding can be used to support the costs of your proposed research, including:
- Cloud computing costs (AWS, GCP, Azure, etc.)
- Indirect/overhead costs
- Student stipends
- Tuition
- Travel expenses to present your work
- Office supplies and expenses
Where is the funding coming from?, inc. Salesforce is a for-profit entity.
How many people can share a single $50,000 grant?
If my proposal is selected, when will I receive the grant funding?
Winners of the Salesforce Deep Learning Grant will be contacted by early December, 2019. We plan to have all payments made by the end of January 2020.
Tianfu Wu, Learning Deep Grammar Networks for Visual Question Answering
North Carolina State University · Computer Vison & Natural Language Processing

In this project, we will focus on visual question answering (VQA) tasks for which we study unified deep grammar networks to not only improve performance, but advance the explainability of the QA process.

Mohit Bansal, Multi-Task Multimodal Translation and Content Selection
University of North Carolina, Chapel Hill · Natural Language Processing

In this proposal, we focus on the hierarchical and parallel multi-task training of several multimodal translation and content selection tasks: video captioning, document summarization, and video highlight prediction.

Junyi Jessy Li, & Katrin Erk, Hierarchical Graph-based Advice Summarization from Online Forums
University of Texas at Austin · Natural Language Processing

To significantly improve the efficiency of gathering advice from online forums, we propose hierarchical summarization of advice—where readers will be able to ‘zoom in’ — by inferencing and clustering the discourse structure of posts.

Quanquan Gu, Understanding and Advancing Nonconvex Optimization for Deep Learning
University of CA, Los Angeles · Machine Learning

In this proposal, we aim at a better understanding of non-convex optimization for deep learning. Such understandings holds the promise of making deep learning more predictable and can be used as a guidance to design new network architectures.

Zachary Chase Lipton, Failing Loudly: Detecting, Quantifying, and Interpreting Distribution Shift
Carnegie Mellon University · Data Mining

We propose to build upon our recent work investigating robust deep learning systems capable detecting and correcting for shifting label distributions. In the proposed work, we will focus on the more general problem of detecting natural shifts in data distributions.

Application process

Applicants should submit a two-page PDF detailing their proposed research and the impact it will have on their respective research community. We encourage you to identify what your research would be enabling, and the proposed outcome should you achieve your vision. Please detail your proposed test case, expected challenges, and how you plan to mitigate them. Applicants should also include a budget overview of how the grant funding would be applied, and if you currently have additional funding for this area of research. Please provide links to previous research papers and citations. A third page can be used for references.

See our papers for an overview of recent research projects. Please mention any relevant papers and authors in your proposal. We have an incredible team of researchers at Salesforce who would love to partner with you on the grant.

Should you be selected we will notify you via email. Grant winners will be announced in our research blog and will be requested to provide an abstract that we will post to our website.

More information about the application and suggested research topics can be found in our FAQ.

26 AUG 2019
Grant announced and open to receive new applications.
31 OCT 2019
Application deadline by midnight pacific standard time.
Decisions notified by email
Grant winners are announced online and notified!

Contact us

Feel free to send us any further questions.

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