# DeLTA — International Conference on Deep Learning Theory and Applications

- **Submission deadline**: 2027-03-25
- **Notification date**: 2027-05-17
- **Conference date**: 2027-07-19
- **Location**: Rome, Italy
- **Conference Partner Index**: 49.3/100 (ranked #1786, confidence 0.45, algorithm 1.1) — how this is computed: https://www.myhuiban.com/ranking
- **Trackers**: 1
- **Attendees**: 0
- **Canonical page**: https://www.myhuiban.com/conference/3713

## Past editions

| Year | Deadline | Conference date | Location |
|---|---|---|---|
| 2020 | 2020-03-06 | 2020-07-08 | Paris, France |

## Call for papers

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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Source: Conference Partner — https://www.myhuiban.com/conference/3713 (rankings reproduced from CCF / ICORE / QUALIS; data cached up to 1 hour)