2026 The 10th International Conference on Deep Learning Technologies (ICDLT 2026)
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About this event
2026 The 10th International Conference on Deep Learning Technologies (ICDLT 2026)
The 10th International Conference on Deep Learning Technologies (ICDLT 2026) will be held from July 17-19, 2026 in Kunming, China. The event is sponsored by Kunming University of Science and Technology and organized by the Faculty of Information Engineering and Automation of Kunming University of Science and Technology.
Event Overview
ICDLT 2026 aims to provide a valuable opportunity for researchers, scholars, and scientists to exchange ideas face-to-face in the field of Deep Learning Technologies. The conference invites submissions from researchers working in deep learning technologies and related domains. The conference themes include, but are not limited to:
- Deep Learning in Software
- Deep Learning in Hardware
- Conceptual Lectures and Cutting-Edge Research in Deep Learning
- Establishing Enterprise Research with Deep Learning at its Core
Key Details
Venue: Kunming, China
Dates: July 17-19, 2026
Submission Deadline: June 5, 2026 (Final Call)
Notification of Acceptance: June 20, 2026
Camera Ready Deadline: June 25, 2026
Registration Deadline: June 25, 2026
Tracks and Special Sessions
The conference features multiple tracks and a special session:
- Track 1: Deep Learning Model and Algorithm (CFP) - Track Chair: Xinhui Ma, University of Hull, United Kingdom
- Track 2: Machine Learning Theory and Technology (CFP) - Track Chair: Pascal Lorenz, University of Haute Alsace, France
- Track 3: Deep and Machine Learning Applications (CFP) - Track Chairs: Leiming Ma, Shanghai Typhoon Institute, China; Hui Zhang, Southwest University of Science and Technology, China
- Track 4: Responsible AI, Security, and Governance (CFP) - Track Chair: Zhu Meng, Beijing University of Posts and Telecommunications, China
- Special Session 1: Autonomous Machine Intelligence – Theory and Applications (AMITA) - Track Chairs: Hiep Xuan Huynh, Can Tho University, Vietnam; Fabrice Guillet, Nantes Université, France; Anh Hoang Pham, VNU-HCM Univ. of Technology (HCMUT), Vietnam; Ngan Thi Tran, VNU – International School (VNUIS), Hanoi, Vietnam
Publication
Accepted papers will be published in the ICDLT 2026 Conference Proceedings.
Contact and More Information
For more details, visit the official website at https://www.icdlt.org/. You can also contact the organizers through the contact information provided on the website.
Data sheet
2026 The 10th International Conference on Deep Learning Technologies (ICDLT 2026) – Event Attendee & Buyer Profile Analysis
Event date: July 17–19, 2026
Location: Kunming, Yunnan Province, China
Event status: Upcoming
Research date: June 29, 2026
Event Overview
| Event Name | 2026 The 10th International Conference on Deep Learning Technologies (ICDLT 2026) |
| Event Date | July 17–19, 2026 |
| Event Status | Upcoming |
| Venue | Kunming, China. Specific venue/hotel facility was not publicly confirmed in the provided official homepage text. |
| City | Kunming |
| State / Region | Yunnan Province |
| Country | China |
| Organizer | Organized by the Faculty of Information Engineering and Automation of Kunming University of Science and Technology; sponsored by Kunming University of Science and Technology, China |
| Official Event Website | icdlt.org |
| Event Type | International academic conference / research summit / paper-presentation event |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Education & Training; Science & Research |
| Audience Reach | International / global academic and research reach, with notable Asia-Pacific and Europe participation evidence from track chairs and session leaders |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for headcount; high for dates, city, organizer, conference themes, and named chair organizations based on official event website content |
| Main Purpose of Event | To convene researchers, scholars, scientists, and related technical stakeholders for face-to-face exchange on deep learning models, machine learning theory, applications, responsible AI, security, governance, and related enterprise research topics |
About the Event
ICDLT 2026 is a specialized international conference focused on deep learning technologies and adjacent machine learning fields. According to the official event website, the conference will be held in Kunming, China from July 17–19, 2026, and is sponsored by Kunming University of Science and Technology and organized by its Faculty of Information Engineering and Automation. The published themes span deep learning in software and hardware, conceptual and frontier research, machine learning theory, applied deep learning, and responsible AI, security, and governance.
From a commercial intelligence perspective, ICDLT 2026 is more research- and knowledge-exchange-oriented than a classic procurement expo. It is relevant for B2B outreach where the target audience includes university labs, AI research groups, R&D leaders, academic decision-makers, technical software providers, AI infrastructure vendors, security/governance solution providers, and innovation partnerships. It is less suitable for pure mass buyer-list building than a large trade show, but can still be useful for highly targeted account-based outreach to research, higher education, and advanced AI technology stakeholders.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| University research labs and faculty | Universities, engineering faculties, AI labs, graduate schools | Evaluate research software, compute tools, datasets, publication services, lab infrastructure | High relevance for academic software, GPU/cloud, analytics, model development, and collaboration platforms |
| Researchers, scholars, and scientists | Academic institutes, national labs, specialist research centers | Technical evaluators and influencers rather than budget approvers in many cases | Strong for thought leadership, product trials, pilot programs, and long-cycle technical adoption |
| AI and machine learning program leaders | University departments, applied AI centers, enterprise research teams | Shape tool selection, project architecture, and collaboration priorities | Relevant for enterprise AI tools, MLOps, model governance, and technical consulting |
| Responsible AI, security, and governance specialists | Research groups, cybersecurity teams, policy and governance units | Influence security evaluation, compliance criteria, and ethical AI adoption | Good fit for AI security, auditing, governance, privacy, and risk-management solutions |
| Enterprise R&D and innovation teams | Technology firms, industrial R&D teams, advanced software groups | Explore applied research partnerships and emerging methods | Useful for strategic partnership outreach and applied AI solution selling |
| Graduate students and doctoral candidates | Universities and research institutions | Early adopters, technical users, future champions | Useful for community building, freemium tools, and developer ecosystem growth |
| Academic administrators and conference committee members | Faculties, schools, conference secretariats, organizing departments | Can influence sponsorships, institutional partnerships, and research program procurement | Relevant for sponsorship sales, university partnerships, and event-related services |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Host city: Kunming | Local university affiliates, regional scholars, local organizing stakeholders | Moderate | Organizer sponsorship suggests strong participation from Kunming University of Science and Technology and nearby academic networks |
| Yunnan Province | Regional universities, technical institutes, and local innovation stakeholders | Moderate | Likely regional attendance base, especially for researchers and students |
| China national market | Researchers from universities, institutes, and applied AI groups across China | High | Official language, host institution, and track chair affiliations indicate broad Chinese academic participation |
| Asia-Pacific | Vietnam and other regional academic/research participants | Moderate | Special session chairs include organizations from Vietnam, supporting regional reach |
| Europe | Track chairs and research collaborators from the United Kingdom and France | Selective / specialist | Named committee leadership indicates active international academic representation |
| International | Global researchers in deep learning and machine learning | Moderate | Conference is positioned as an international event, but attendee-country counts were not publicly confirmed in the provided source text |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The event is explicitly international and includes confirmed track/session chair affiliations from China, the United Kingdom, France, and Vietnam. |
| National | Strong secondary reach | Chinese institutional sponsorship and organization make nationwide academic participation likely. |
| Regional | Operationally relevant secondary reach | Kunming and Yunnan Province are likely to contribute local and regional attendance, especially from host-affiliated institutions. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Kunming University of Science and Technology | University / sponsor / attendee-side institution | Official sponsor and host-side institution; highest-probability institutional stakeholder | kust.edu.cn | Dean, Professor, Research Director, Lab Director, IT Director | Confirmed Sponsor / Exhibitor |
| Faculty of Information Engineering and Automation, Kunming University of Science and Technology | Academic organizer | Official organizing body for the conference | kust.edu.cn | Faculty Dean, Department Chair, Conference Chair, Research Program Lead | Confirmed Current-Year Participant |
| University of Hull | University / research institution | Track chair organization for Deep Learning Model and Algorithm | hull.ac.uk | Professor, AI Research Lead, School Head, Research Computing Lead | Confirmed Speaker Organization |
| University of Haute Alsace | University / research institution | Track chair organization for Machine Learning Theory and Technology | uha.fr | Professor, Research Director, AI Program Lead | Confirmed Speaker Organization |
| Shanghai Typhoon Institute | Research institute | Track chair organization for Deep and Machine Learning Applications | typhoon.org.cn | Research Scientist, Lab Director, Data Science Lead, Applied AI Lead | Confirmed Speaker Organization |
| Southwest University of Science and Technology | University / research institution | Named track chair organization for applied machine learning track | swust.edu.cn | Professor, Dean, AI Research Lead, Engineering Department Head | Confirmed Speaker Organization |
| Beijing University of Posts and Telecommunications | University / technical research institution | Track chair organization for Responsible AI, Security, and Governance | bupt.edu.cn | Professor, Cybersecurity Director, AI Governance Lead, Research Dean | Confirmed Speaker Organization |
| Can Tho University | University / regional research institution | Special session chair organization for autonomous machine intelligence | ctu.edu.vn | Professor, Faculty Head, Research Program Manager | Confirmed Speaker Organization |
| Nantes Université | University / research institution | Special session chair organization with AI theory/application relevance | univ-nantes.fr | Professor, Research Director, Innovation Partnerships Lead | Confirmed Speaker Organization |
| VNU-HCM University of Technology (HCMUT) | University / engineering institution | Special session chair organization tied to autonomous machine intelligence | hcmut.edu.vn | Professor, Engineering Dean, ML Lab Director | Confirmed Speaker Organization |
| VNU International School | University / international academic institution | Special session chair organization; indicates cross-border academic engagement | is.vnu.edu.vn | Program Director, Faculty Lead, Research Partnerships Director | Confirmed Speaker Organization |
Note: This conference is research-oriented. Officially confirmed current-year organizations in the provided source material are mainly sponsor, organizer, and named track/session chair affiliations rather than classic commercial procurement buyers.
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Professor / Principal Investigator | Research / Faculty | Senior | Core decision influencers for research collaborations, software evaluation, and lab technology adoption |
| 2 | Director of Research / Research Director | Research & Development | Director | Useful for strategic partnerships, grant-aligned tools, and institutional deployments |
| 3 | Lab Director / AI Lab Head | Research / Engineering | Director | Strong fit for compute, data, MLOps, benchmarking, and experiment-management solutions |
| 4 | Dean / Faculty Dean / Department Chair | Academic Administration | Executive / Senior | Relevant for institutional partnerships, sponsorship, and larger departmental purchases |
| 5 | CTO / Chief Scientist | Executive / Technology | Executive | Relevant where enterprise or applied-research teams are present |
| 6 | Machine Learning Engineering Manager | Engineering / AI | Manager | Ideal for applied tools, deployment platforms, and technical workflows |
| 7 | AI Governance Lead / Security Research Lead | Security / Governance / Research | Senior / Director | Relevant because responsible AI, security, and governance are explicit conference themes |
| 8 | IT Director / Research Computing Manager | IT / Infrastructure | Director / Manager | Important for infrastructure, cloud, data pipelines, hardware, and security deployments |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Higher Education | Most directly aligned with named sponsor, organizer, and chair affiliations | Universities, faculties, labs, research centers |
| 2 | Research | Conference content is centered on research exchange and technical publication | Research institutes, applied science labs, AI centers |
| 3 | Information Technology & Services | Relevant for enterprise AI services, implementation partners, and technical vendors | AI integration, consulting, deployment support |
| 4 | Computer Software | Deep learning toolchains, MLOps, model development, analytics | Modeling platforms, developer tools, analytics suites |
| 5 | Computer Hardware | Relevant due to official theme coverage of deep learning in hardware | Accelerators, edge devices, compute systems |
| 6 | Semiconductors | AI hardware and performance optimization are adjacent interest areas | AI chips, inference acceleration, hardware partnerships |
| 7 | Computer & Network Security | Explicit fit with responsible AI, security, and governance track | AI risk, model security, privacy, governance solutions |
| 8 | Telecommunications | One track chair affiliation comes from a major telecom-oriented university | Network AI, edge intelligence, telecom analytics |
| 9 | Education Management | Relevant for academic leadership and institutional purchasing | University administration, digital learning and research services |
| 10 | Government Administration | Applicable where public universities and state-backed research entities participate | Public research funding, academic procurement, institutional partnerships |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | Official website excerpt provided by user | No attendee count, expected attendance, or registration total was shown in the provided official text |
| Exhibitor count | Not publicly confirmed | Unconfirmed | Official website excerpt provided by user | This appears to be a conference rather than a trade exhibition |
| Buyer count | Not publicly confirmed | Unconfirmed | Official website excerpt provided by user | Conference language identifies researchers, scholars, and scientists, not formal hosted buyers |
| Speaker / chair organizations named | 9 external chair-affiliated organizations plus host institution | Confirmed | Official homepage track and special session listings | Useful for account targeting, but not a full attendee list |
| Sponsor count | 1 named sponsor | Confirmed | Official homepage | Kunming University of Science and Technology |
| Organizer count | 1 named organizer | Confirmed | Official homepage | Faculty of Information Engineering and Automation, Kunming University of Science and Technology |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Deep learning models and algorithms | Advanced experimentation, optimization, and benchmarking | Engage faculty and lab leads with model development tools and technical workshops | ML platforms, notebooks, optimization software, experiment tracking |
| Machine learning theory and technology | Compute access, research tooling, and technical collaboration | Offer evaluation access, academic licensing, and joint research pilots | Cloud compute, software licenses, algorithm libraries, data infrastructure |
| Deep and machine learning applications | Applied use cases and deployment support | Target applied AI groups and interdisciplinary researchers | Application frameworks, deployment support, MLOps, APIs |
| Responsible AI | Model transparency, fairness, oversight, policy alignment | Lead with governance assessments and risk-management frameworks | Responsible AI software, audit tools, governance consulting |
| AI security | Model security, data protection, adversarial robustness | Engage security-focused labs and telecom/technical institutions | Security testing, privacy technologies, secure model deployment |
| Deep learning in hardware | Compute acceleration and hardware-aware optimization | Reach out to engineering labs and hardware-related research groups | GPU servers, accelerators, embedded AI systems, hardware tooling |
| Autonomous machine intelligence | Simulation, control, and advanced AI experimentation | Useful for innovation partnerships and cross-border R&D discussions | Simulation software, autonomy stacks, edge AI platforms |
| Enterprise research with deep learning at its core | Research commercialization and scalable AI workflows | Position partnership models rather than transactional selling | Enterprise AI platforms, consulting, licensing, collaboration frameworks |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | Medium | Strong for research, academic, and technical stakeholders; weaker for traditional procurement-heavy list building |
| Decision-maker availability | Medium | Professors, deans, and research directors are relevant, but many attendees may be technical influencers rather than direct purchasers |
| Data collection potential | Medium | Named chair organizations provide quality account intelligence, but attendee volume data is limited in the provided source |
| Apollo targeting potential | High | Clear fit exists for Higher Education, Research, IT, Software, Security, and Hardware industries |
| Geographic targeting potential | High | Can segment by China, APAC, and selected Europe-based academic institutions |
| Best outreach approach | High | Use consultative outreach: research collaboration, academic licensing, technical validation, benchmark access, and thought leadership |
| Overall lead quality | Medium to High | Good for niche AI research and institutional targeting; not ideal for mass-volume transactional buyer acquisition |
| Best use case | High | ABM outreach to universities, labs, AI researchers, governance/security experts, and research computing teams |
| Limitations / risks | Medium | Limited public attendee-count transparency; many participants may be authors or academics without immediate purchasing authority |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Higher Education; Research; Information Technology & Services; Computer Software; Computer Hardware; Computer & Network Security; Semiconductors; Telecommunications; Education Management; Government Administration | Focus on the most likely institutional and technical attendee-side organizations |
| Departments | Research; Engineering; Information Technology; Education; Operations; Security; Executive | Capture both academic and applied AI decision influencers |
| Seniority | Owner; Partner; C-Level; VP; Director; Head; Manager; Professor-equivalent where searchable | Prioritize purchasing influence and research leadership |
| Job titles | Professor, Principal Investigator, Research Director, Lab Director, Dean, Department Chair, AI Research Lead, Machine Learning Lead, CTO, Chief Scientist, IT Director, Research Computing Manager, Security Research Lead, AI Governance Lead | Align outreach to conference themes and institutional structures |
| Geography | China; Yunnan Province; Kunming; Vietnam; France; United Kingdom; broader APAC | Match official current-year named affiliations and likely attendance corridors |
| Employee size | 51-200; 201-500; 501-1000; 1001-5000; 5001+ | Capture universities, institutes, and established technical organizations |
| Keywords | deep learning, machine learning, AI lab, responsible AI, AI governance, neural networks, computer vision, model security, research computing, autonomous intelligence | Improve precision for AI-specialist organizations |
| Technologies | If available: cloud platforms, GPU compute, data science stack, security tools | Useful for identifying advanced research environments |
| Revenue range | Leave broad or optional | Revenue may be less reliable for universities and public institutions |
| Company type | Educational institution; research institute; public institution; private technology company | Split academic versus commercial prospecting motions |
Suggested Apollo Search Logic: ("deep learning" OR "machine learning" OR "AI research" OR "responsible AI" OR "AI governance" OR "neural network" OR "autonomous intelligence") AND (Professor OR "Research Director" OR "Lab Director" OR Dean OR "Department Chair" OR CTO OR "Chief Scientist" OR "IT Director" OR "Security Research Lead") AND industry in (Higher Education, Research, Information Technology & Services, Computer Software, Computer Hardware, Computer & Network Security).
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| ICDLT 2026 Official Website | Official event website | Confirmed event name, dates, city, country, sponsor, organizer, conference purpose, tracks, special session, track chair organizations, deadlines, and publication statement | High |
| Official website homepage text provided by user | Primary source excerpt | Used as source-of-truth content for the data-sheet, especially where no other verified source text was provided in the prompt | High |
Suitability for B2B attendee list building: Moderate. The event is valuable for targeted academic/research account identification and AI stakeholder outreach, but the provided official source does not publish a broad attendee roster or verified attendance volume.
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Tell us your work email and our AI instantly builds a buyer list matched to 2026 The 10th International Conference on Deep Learning Technologies (ICDLT 2026) — the best‑fit companies to target, the exact decision‑maker job titles, and the industry filters that fit what you sell.
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