Machine Learning Conference - Call for Papers

We invite your original quality submissions to the 13th Intelligent Systems Conference 2027 Track on Machine Learning.

This track covers the algorithms and architectures that let systems learn from data and experience, from deep learning and reinforcement learning to the emerging frontier of quantum machine learning. We invite contributions on natural language processing and large language models alongside the classical knowledge representation and decision-making frameworks that determine how a learned model reasons once training ends. Submissions may include theoretical papers advancing a specific learning paradigm, systems papers on multi-agent coordination or neuromorphic computing platforms, or applied studies of human-computer interaction that examine how learned systems behave once people are in the loop. Particular value is placed on honest evaluation of a method under realistic constraints rather than benchmark gains reported in isolation. The track ultimately looks for machine learning research that holds up once a model has to learn, decide, and interact outside a controlled setting.

Round 1 (Open)

Submission Deadline: 01 November 2026
Notification of Acceptance: 01 December 2026
Camera-Ready Submission: 15 December 2026
Conference Dates: 2-3 September 2027

Round 2 (Closed)

Submission Deadline: 01 December 2026
Notification of Acceptance: 01 January 2027
Camera-Ready Submission: 15 January 2027
Conference Dates: 2-3 September 2027

All times are in Anywhere on Earth (AoE) time zone.

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Topics of Interest

Deep Learning: Neural network architectures for representation learning, including convolutional, recurrent, and transformer-based models, with an emphasis on scalability, training efficiency, and generalization across domains.
Reinforcement Learning: Sequential decision-making methods including value-based, policy-gradient, and model-based approaches, applied to control, games, and real-world optimization problems where an agent learns from interaction rather than labeled examples.
Quantum Machine Learning: Machine learning algorithms that leverage quantum computing to accelerate training or solve problems intractable for classical methods.
Agents and Multi-agent Systems: Design and coordination of autonomous software agents that perceive, reason, and act, individually or in cooperative and competitive groups.
Neuromorphic Systems: Brain-inspired computing architectures that emulate neural structures for energy-efficient, event-driven processing.
Natural Language Processing (NLP): Computational methods for understanding, generating, and translating human language, including syntax, semantics, and discourse-level modeling.
Large Language Models (LLMs): Pretraining, fine-tuning, prompting, and retrieval augmentation of large-scale language models, along with evaluation of their reasoning capabilities and failure modes.
Decision-Making Systems: Computational frameworks for structuring, optimizing, and automating complex decisions under uncertainty.
Knowledge Representation: Formal methods for representing facts, rules, and relationships about the world and reasoning over them, spanning logic-based, ontology-based, and neuro-symbolic approaches that combine symbolic reasoning with learned representations.
Human Computer Interaction: Interaction models between humans and digital or robotic systems across physical and virtual touchpoints, emphasizing trust, usability, and collaborative task performance.

Submission Guidelines

We accept initial submission in PDF format only.
To comply with the double-blind peer review process, please refrain from including author details, university, country information, or any other author-related information in the paper.
Prior to submission, authors are responsible for ensuring their papers are thoroughly checked and proofread. Once submitted, no revisions are permitted during the review process. If accepted, the authors may make minor revisions before final publication to address any identified areas for improvement.
At the time of camera ready submission, please include complete author information as per the paper format. Author names, email addresses, and their sequence will be considered final as per the submitted camera-ready version.
Submissions should be primarily original work. No more than 25% of the text can be drawn from previously published material by the same authors, and any such reuse must be properly cited. Therefore, at least 75% of the manuscript must consist of new, unpublished content.

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Formatting Guidelines

Manuscripts should generally be limited to 18 pages for the main text (excluding references and appendices of upto 7 pages). Submissions exceeding this limit may be considered at the chair's discretion, particularly for review or survey articles.
Manuscripts should present original work and fall within the scope of the conference topics.
It is the authors' responsibility to ensure the accuracy of all quotations, citations, figures, maps, and tables within their manuscript.
The abstract should adhere to the 150-250 word limit as per the conference guidelines.
Figures and tables must be positioned within the manuscript where they are discussed and should be clear and easily readable.
All figures, tables, references, appendices, algorithms, annexures, and supplementary materials must be clearly numbered, properly cited in the text.
Tables must be neatly aligned within the page margins and must not overflow the page boundaries.
Figure captions and table headings should be concise, unique, and ideally limited to a maximum of two lines.
References should be formatted correctly and consistently, including all the necessary details such as author names, paper titles, publication sources, and years. Every reference listed must be cited in the main text.
The use of foreign languages in the manuscript is not allowed unless accompanied by a corresponding English translation.
The manuscript must adhere to the provided template.

Review Process

Submissions will be reviewed anonymously (double-blind). Therefore, your manuscript must not reveal your identity or affiliation. Specifically: omit author names and affiliations from the manuscript; anonymize citations and mentions of your own prior work that is closely related to the submitted paper; and exclude funding or other acknowledgments.

Each paper will undergo review by at least three regular program committee members or two senior program committee members. Acceptance will be based on novelty, technical depth, elegance, impact (practical or theoretical), and presentation quality.

Original: The paper should explore a new idea, project, or issue; offer new insights on existing research; present new research; or provide a novel perspective on existing information.
Engaging: The presentation should be interactive and engage the readers.
Significant: The paper should address and discuss key issues related to improving the effectiveness of current techniques, and its content should be easily understood and widely applicable.
Quality: Claims made in the paper must be substantiated with adequate data, reference relevant existing work, and honestly acknowledge any limitations.
Clear: The intended outcomes should be easily understood.
Relevant: The paper's topic must be relevant to at least one of the conference's designated tracks.

Use of AI-Assisting Tools

The conference permits the responsible use of Generative AI (GenAI) tools in manuscript preparation. Authors are required to observe the following:

Declare Usage – Any use of GenAI must be disclosed in the manuscript (e.g., in a Declaration on Generative AI section).
Maintain Responsibility – Authors must critically review and edit AI-assisted text; unedited AI output is not acceptable.
No AI Authorship – GenAI tools cannot be listed as authors. Authorship is limited to humans who take full responsibility for the work.
Limit Role of AI – GenAI may support drafting and language refinement, but the development of scientific ideas, arguments, results, and conclusions must be carried out by human authors.
Accountability – Authors remain fully accountable for the accuracy, originality, and integrity of their manuscripts.

Note: Non-compliance with these guidelines will be treated as academic misconduct and may result in the removal of published work.

Other Tracks at 13th Intelligent Systems Conference 2027

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