Conference Theme:
Machine learning (ML) refers to solutions of problems modeled out of example solutions, rather than explicit algorithmic solution tailor-made for each problem. A number of minor improvements and alterations in structure and processing of traditional AI models such as artificial neural networks have together improved their overall performance tremendously and solved many long-standing issues such as over-fitting and slow or no learning.
These improvements along with hardware advances have enabled the repeated use of the various blocks of learning models, leading to “Deep Learning”. In new ML systems such as Convolution Neural Networks (CNN), learning has been made fully automatic. In traditional ANNs, human intervention was required to extract relevant features from data for training, testing and prediction. In CNNs, the features are also learned by the network. These automatically learned features bring in a new dimension to ML. Not only are these abstract features not intelligible to the human being, but can be refined by using multiple feature extraction blocks, creating features of features, making learning deep. This “divyadrishti” that human eye cannot capture, achieved through higher order representation of problem space, can be used to perform complex classifications.
Deep.Learn(2021) will deliberate the emerging models of deep learning and the successful applications from various fields that are continuously being developed and deployed. Abstracts on focal areas listed in the following tracks are solicited: Track1 (Artificial intelligence and deep learning: algorithms and techniques), Track2 (Deep learning: data visualization and prediction), Track3 (Multidisciplinary application of deep learning and data analytics), Track4 (Deep learning and COVID-19), Track5 (AI tools, platforms and emerging trends).
Conference mode:
Online
Conference events:
Plenary Talks, Industry Presentations, Research Paper Presentations, Panel Discussion
Keynote Speaker:
Prof. Deepak Garg, Bennett University
Plenary Speaker:
To be announced
Industry Presentation:
To be announced
Important dates to remember:
| Event | Open | Closed |
| Extended Abstract Submission | 20th December 2020 | 10th February 2021 |
| Notification of acceptance | Before 12th Feb 2021 | |
| Registration of participants | 20th December 2020 | 15th February 2021 |
| Conference Dates | 19, 20 February 2021 | |
All presenting authors should register.
Tracks:
- TRACK-1: Artificial intelligence and deep learning: algorithms and techniques
- TRACK-2: Deep learning: data visualization and prediction
- TRACK-3: Multidisciplinary application of deep learning and data analytics
- TRACK-4: Deep learning and COVID-19
- TRACK-5: AI tools, platforms and emerging trends
Call for abstracts:
Extended abstracts describing innovative machine learning models or improvements in existing models and/or their applications are invited for presentation. All abstracts must be original and should follow the prescribed abstract guidelines. Authors should ensure that the abstracts are not simultaneously submitted to another journal or conference. Selected abstracts will be given the opportunity for paper presentation and rest will be considered for the category of poster presentation. Selected extended abstracts will be published in the proceedings and authors are free to publish full length papers elsewhere.
Extended abstracts that fall in the tracks mentioned above are invited.
Submission:
https://easychair.org/conferences?conf=deeplearn2021
Extended abstract guidelines:
- Extended abstract should not exceed two pages.
- Identify the presenting author by underlining his/her name in the author list.
- The entire document should be prepared on an A-4 sized paper in single page using Times New Roman font of type 12.
- Maximum one image/table may be used within the one page limit and captions should be placed under the image/table.
- Extended abstract should include an introduction, methodology, results and brief discussion, and conclusion in the given format.
Paper presentation guidelines:
Selected abstracts will be given the opportunity for paper presentation during the conference and will be published in the proceedings with ISBN. There will not be any limit for the number of slides, but each presenter will get 8 minutes for presenting their work and 2 minutes will be allotted for discussion. Paper presentation certificate will be issued to the presenting author(s).
Poster presentation guidelines:
Extended abstracts other than those which are selected for paper presentation are considered for poster presentation. Ongoing works can also be considered for the category of poster presentation. The presenter is required to present their work as posters using a single slide. Each presenter will get 3 minutes for presenting their poster and 1 minute will be allotted for discussion. Poster presentation certificate will be issued to the presenting author.
Proceedings:
- Hard copy of proceedings will be posted to the registered participant
- Selected extended abstracts for paper presentation will be published in the proceedings with ISBN.
Registration:
Registration Fee (inclusive of all taxes and charges):
- Rs. 1000 for UG and PG students
- Rs. 1500 for Non-students & Research Scholars
- Rs. 9000 for Institutional Registration (5 delegates)
- USD 50 for Non-Indian Participants
- Discount: 25% reduction for CSI members
Payment options:
For online payment, please visit https://dlpay.csitvm.org
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