Conference Information

DeLTA 2027: International Conference on Deep Learning Theory and Applications

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Submission Date:
2027-03-25 Due in 167 days
Notification Date:
2027-05-17
Conference Date:
2027-07-19
Location:
Rome, Italy
Years:
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DeLTA
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Conference Partner Index (CP-I)

49.3 / 100
Ranked #1,786 of 5,694 conferences · Top 32%

#184 of 741 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
59
Community attention (10%)
17
Public record completeness (15%)
55

Inputs used: Editions on record: 8 · Researchers following it here: 1 · Researchers who opened this page in the past 24 months: 3

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-10-09

Call For Papers

DeLTA 2027 (International Conference on Deep Learning Theory and Applications) is an academic conference held in Rome, Italy on 2027-07-19. The paper submission deadline is 2027-03-25. Acceptance notifications are sent on 2027-05-17.

SCOPE Deep Learning and Big Data Analytics are two major topics of data science, nowadays. Big Data has become important in practice, as many organizations have been collecting massive amounts of data that can contain useful information for business analysis and decisions, impacting existing and future technology. A key benefit of Deep Learning is the ability to process these data and extract high-level complex abstractions as data representations, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled. Machine-learning and artificial intelligence are pervasive in most real-world applications scenarios such as computer vision, information retrieval and summarization from structured and unstructured multimodal data sources, natural language understanding and translation, and many other application domains. Deep learning approaches, leveraging on big data, are outperforming state-of-the-art more “classical” supervised and unsupervised approaches, directly learning relevant features and data representations without requiring explicit domain knowledge or human feature engineering. These approaches are currently highly important in IoT applications. CONFERENCE AREAS Each of these topic areas is expanded below but the sub-topics list is not exhaustive. Papers may address one or more of the listed sub-topics, although authors should not feel limited by them. Unlisted but related sub-topics are also acceptable, provided they fit in one of the following main topic areas: 1. MODELS AND ALGORITHMS 2. MACHINE LEARNING 3. BIG DATA ANALYTICS 4. COMPUTER VISION APPLICATIONS 5. NATURAL LANGUAGE UNDERSTANDING AREA 1: MODELS AND ALGORITHMS Recurrent Neural Network (RNN) Evolutionary Methods Convolutional Neural Networks (CNN) Deep Hierarchical Networks (DHN) Dimensionality Reduction Unsupervised Feature Learning Generative Adversarial Networks (GAN) Autoencoders AREA 2: MACHINE LEARNING Active Learning Meta-Learning and Deep Networks Deep Metric Learning Methods Deep Reinforcement Learning Learning Deep Generative Models Graph Representation Learning Clustering, Classification and Regression Classification Explainability AREA 3: BIG DATA ANALYTICS Extracting Complex Patterns Fast Information Retrieval Scalability of Models Data Integration and Fusion High-Dimensional Data AREA 4: COMPUTER VISION APPLICATIONS Image Classification Object Detection Image Retrieval Semantic Segmentation AREA 5: NATURAL LANGUAGE UNDERSTANDING Sentiment Analysis Question Answering Applications Content Filtering on Social Networks Recommender Systems
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