Abstract and paper submission deadlines are strict. In no circumstances will extensions be given.

Authors should include a full title for their paper, as well as a complete abstract by the abstract submission deadline. Submission titles should not be modified after the abstract submission deadline, and abstracts should not be modified by more than 50% after the abstract submission deadline. Submissions violating these rules may be deleted after the paper submission deadline without reviewing. The author list at the abstract submission deadline will be considered final, and no changes in authorship will be permitted for accepted papers.

All submissions must be anonymized and may not contain any information with the intention or consequence of violating the double-blind reviewing policy, including (but not limited to) citing previous works of the authors or sharing links in a way that can infer any author's identity or institution, actions that reveal the identities of the authors to potential reviewers.

It is not appropriate to submit papers that are identical (or substantially similar) to versions that have been previously published, accepted for publication, or submitted in parallel to other conferences or journals. Such submissions violate our dual submission policy, and the organizers have the right to reject such submissions, or to remove them from the proceedings.

Accepted papers must be based on original research and must contain significant novel results of significant interest to the machine learning community. Results can be either theoretical or empirical. Results will be judged on the degree to which they have been objectively established and/or their potential for scientific and technological impact. Reproducibility of results and easy availability of code will be taken into account in the decision-making process whenever appropriate.

Authors and members of the program committee, including reviewers, are expected to follow standard ethical guidelines. Plagiarism in any form is strictly forbidden as is unethical use of privileged information by reviewers, such as sharing it or using it for any other purpose than the reviewing process. All suspected unethical behaviors will be investigated by an ethics board and individuals found violating the rules may face sanctions. Details of the guideline will be published on the website.

For Further Details Contact:

Prof. Dr. C.S.R.Prabhu,

Program Chair and Convenor AIMLSNLP 2023

Director, Research

KMIT-Hyderabad

Formerly Director General of National Informatics Center (NIC)

MEITY, Government of India, New Delhi

Prsent Chairman of ACM Hyderbad Deccan Chapter

csrprabhu@kmit.in / aneesh.chivukula@gmail.comv