+91 9344546233 [email protected] Physical / Digital Conference
ICTLDS · Registering as Listener

International Conference on Transfer Learning and Data Science

16–17 Feb 2027 Calgary, Canada Standard / Physical Participation
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$135
virtual · $195 in person

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Connect with researchers across 30+ countries advancing Artificial Intelligence,Data Science,Machine Learning.

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1. Paper Submission & Publication :

  • All authors and co-authors must inform their institution (e.g., Head of Department, Principal, Supervisor) prior to submitting papers to Researchfora conferences.
  • Each submission is reviewed by at least two qualified reviewers (internal or external).
  • Only papers with completed registration and payment will be considered for publication.
  • The submitter will be deemed the corresponding author unless otherwise specified.
  • Researchfora is not liable for any authorship disputes. If a complaint regarding authorship or originality is validated, the paper will be withdrawn immediately.
  • Withdrawn or suspended papers will not be published or distributed under any circumstances.

2. Conference Attendance :

  • All participants must register and pay in full to attend any Researchfora conference.
  • The organizing committee reserves the right to change the venue, dates, or format (virtual/hybrid/in-person) at any time. Notifications will be sent via registered email.
  • Researchfora is not responsible for any travel or accommodation costs incurred due to changes in format or schedule.
  • No refunds will be granted for participants who choose not to attend the conference after registration.
  • If the primary author cannot attend, a co-author may attend in their place. However, no refunds will be issued for non-attendance.

3. Cancellation & Refund Policy :

  • 3.1 Cancellation is only permitted before the issuance of an official invitation letter. Once issued, no refund is available.
  • Full refund: cancellations made at least 70 days before the conference, with a cancellation form submitted 60 days prior.
  • Partial refund: cancellations 60–30 days prior may incur administrative/documentation fees.
  • No refund: cancellations within 30 days of the event, though a credit voucher valid for one year may be issued for future participation.
  • If a participant is unable to attend the conference due to personal reasons, the registration fee is non-refundable. However, the paid amount will be credited toward attendance at any of our international conferences within one year from the original date of registration.
  • As the conference will be held in a hybrid format, the organizer reserves the right to conduct the event either in person or virtually. Please note that no refunds will be issued due to changes in the event format.
  • Virtual registrations are non-refundable; participants may receive credit toward a future Researchfora event.

4. Registrant Responsibilities & Communication :

  • Upon successful completion of the registration process, it is the responsibility of the registrant to contact the researchfora team for updates, scheduling details, and further instructions related to the event.
  • If the registrant fails to respond to communications from Researchfora or its representatives for a period of more than 15 days after registration, the registration will be considered invalid, and refunds will not apply.
  • If the registration form is not submitted within 7 days from the date of registration, the registration will be considered invalid, and refunds will not apply.

5. Travel and Accommodation :

  • Participants are fully responsible for travel, transport, and accommodation arrangements.
  • Researchfora will not compensate for losses due to changes in event formats or schedules.
  • Registration fees do not cover travel or accommodation.

6. Transfer of Registration :

  • A paid registration may be transferred to another individual from the same institution, pending approval. Requests must be submitted via email with authorization and updated registration details.
  • Transfers must be completed at least 14 days before the event start date.
  • Transferred registrations are non-refundable.
  • Transfers to another Researchfora event are allowed if within the valid registration period.

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  • Legal action may be taken against individuals who falsify or unlawfully use the invitation letter.
  • By accepting the invitation letter, the attendee agrees to comply with international travel laws, conference policies, and ethical participation standards.

8. Important Notes :

  • All changes, cancellations, and transfer requests must be submitted in writing to: [email protected]
  • Participants are deemed to have read and agreed to these terms upon completing registration.
  • All transactions are carried out voluntarily after acceptance of these terms.
  • Completed registration forms must be submitted within 3 days of payment.
  • Official conference programs and schedules will be emailed at least 15 days prior to the event—do not make travel bookings before receiving this confirmation.
  • In hybrid-format events, any registrations received after the deadline will be processed as virtual-only. All registrations include access to one additional Researchfora conference, depending on your registration category.

9. Conference Programme and Participation Policy :

  • To enhance the academic value of the event, the Organiser may combine research contributions from multiple disciplines and relevant SDG areas in interdisciplinary or multidisciplinary sessions that foster learning, connections, and cooperation.

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What you'll hear about

Session tracks covered across the event.

View all 11 tracks →
This track focuses on the latest methodologies and innovations in transfer learning, including domain adaptation and fine-tuning techniques. Researchers are encouraged to present their findings on deep transfer learning and its implications for various applications.
This session will explore the utilization of pre-trained models in data science, highlighting their effectiveness in improving model performance. Contributions that discuss the challenges and benefits of using these models in real-world scenarios are particularly welcome.
This track aims to investigate strategies for cross-domain learning, emphasizing the importance of knowledge transfer across different domains. Papers that present novel approaches or case studies demonstrating successful cross-domain applications are encouraged.
This session will delve into few-shot and zero-shot learning paradigms, examining their potential to enhance model generalization in data-scarce environments. Submissions should focus on innovative techniques and their applications in various engineering fields.
This track will cover multi-task learning approaches that leverage shared representations to improve performance across related tasks. Researchers are invited to share their insights on the effectiveness and challenges of implementing multi-task learning in practice.
This session focuses on the mechanisms of knowledge transfer in artificial intelligence, exploring how information can be effectively reused across different tasks. Contributions that discuss theoretical frameworks and practical applications are highly encouraged.
This track will examine feature reuse techniques in machine learning, emphasizing their role in enhancing model efficiency and accuracy. Papers that provide empirical evidence of feature reuse benefits in various applications are particularly welcome.
This session will focus on representation learning techniques that facilitate effective transfer learning. Researchers are invited to present novel approaches that enhance the quality of learned representations for improved model performance.
This track will highlight diverse applications of transfer learning within the engineering domain, showcasing real-world case studies and implementations. Contributions that demonstrate the impact of transfer learning on engineering challenges are encouraged.
This session will explore scalable transfer methods that address the challenges posed by big data in machine learning. Papers that propose innovative solutions for efficient data processing and model training are particularly welcome.
This track will investigate techniques aimed at improving model generalization in machine learning, focusing on strategies that enhance performance across unseen data. Researchers are encouraged to share their findings on effective generalization methods and their implications.
What's Included

Benefits of Registering as Listener

Access to Conference Sessions

Attend keynote, plenary and parallel sessions.

Networking Opportunities

Connect with researchers, educators and scholars.

Certificate of Participation

Receive a digital certificate after participation.

Invitation Letter Support

Official letter support after confirmed registration.

Conference Kit / Materials

E-proceedings and conference resource materials.

Access to Keynote Sessions

Learn from leading experts across event themes.