France

What if, by leveraging the rapid development of AI,cyber-infrastructure, and astronomical surveys we can create an extremely intelligent machine with infinite knowledge that after being feed with all ofthe available survey data from all the sources and wavelengths is able to recreate every observation for any object in any wavelength? What if we can feed that entity with an optical image and ask for a radiocounterpart? Or ask it to generate infrared data from a given set of properties? Will, that machine been able to make inferences from new observations, assuming its infinite memory? Even though this might sound too much science-fiction, 10 to 15 years from now might be an incubating project which needs to start today. In this talk, I&#39;ll discuss what efforts have been made in this direction, what deep learning advances might help us think in that future, how data from multiple surveys and telescopes can be combined in taking the first steps, and what have we done to make this happen.Thanks to the advancement of computing, AI, and gateways techniques, the possibilities are countless, and it is now that we need to think about these issues in order to be prepared and to understand how information can be extracted intelligently in favor ofscientific discoveries.

DeLTA 2021

More than Meets the Eye: Towards an Artificial Intelligence Observatory

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

What if, by leveraging the rapid development of AI,cyber-infrastructure, and astronomical surveys we can create an extremely intelligent machine with infinite knowledge that after being feed with all ofthe available survey data from all the sources and wavelengths is able to recreate every observation for any object in any wavelength? What if we can feed that entity with an optical image and ask for a radiocounterpart? Or ask it to generate infrared data from a given set of properties? Will, that machine been able to make inferences from new observations, assuming its infinite memory? Even though this might sound too much science-fiction, 10 to 15 years from now might be an incubating project which needs to start today. In this talk, I'll discuss what efforts have been made in this direction, what deep learning advances might help us think in that future, how data from multiple surveys and telescopes can be combined in taking the first steps, and what have we done to make this happen.Thanks to the advancement of computing, AI, and gateways techniques, the possibilities are countless, and it is now that we need to think about these issues in order to be prepared and to understand how information can be extracted intelligently in favor ofscientific discoveries.

keynote

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.
<br>
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.
<br><br>
CONFERENCE AREAS<br>
1 . Models and Algorithms<br>
2 . Machine Learning<br>
3 . Big Data Analytics<br>
4 . Computer Vision Applications<br>
5 . Natural Language Understanding


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.

An increased number of people face unfavorable mental health conditions at some points in their life, such as depression, stress, and anxiety. The Covid-19 crisis has even reinforced this negative trend. In my talk, I will discuss the potential of modern deep learning techniques to develop a better understanding of mental health conditions. I will describe how to exploit deep learning strategies for developing appropriate intervention strategies, such as personal health care coaches running on the user’s mobile device. Two factors contribute to increased interest in deep learning techniques for mental health promotion. First, the number of affordable mobile and wearable technologies with built-in sensors for quantifying many aspects of our lives is rapidly increasing. Consequently, new data sources and opportunities arise for the development of machine learning models and their applications. Second, deep learning techniques have led to breakthroughs in processing large amounts of data, including multimodal behavioral data and relevant contextual and environmental information. In my talk, I will discuss challenges we need to solve, to implement appropriate solutions for promoting good mental health. Such challenges include the need for lightweight architectures to enable the processing of data on mobile devices, strategies to deal with scarce and noisy data, and the appropriate handling of privacy concerns. Concrete examples from national and international projects, Emma, MindBot, and ForDigitHealth, will illustrate the talk.

Leveraging Deep Learning for Mental Health Promotion

Applications, services and devices using biometric systems are continuously growing, and always new challenges must be faced by researchers. Adaptability, robustness to non-ideal conditions, real-time capability, high accuracy as well as improved interactions with the user, are strong requirements now present in innovative applications and solutions such as cyber security, smart devices and ambient intelligent infrastructures. Biometric systems are designed not just for identity recognition, but they can also be extremely useful to profile users and to understand the human behavior inferring their needs and desires. The presentation will focus on innovative biometric recognition approaches and systems, with specific focus on recent approaches based on artificial intelligence showing how recent deep learning techniques are capable to learn discriminative features directly from complex multidimensional signals and images as well as increasing the accuracy, adaptability, and robustness to non-ideal conditions of biometric systems with respect to traditional approaches. The talk presents biometric systems from a technological point of view and provides an outlook of recent artificial intelligence approaches, including deep learning methods with current strong points and limitations. A review of new applications of biometric systems and recent trends will be also presented.

Deep Learning for Biometric Systems: Applications and Trends

Transfer learning can be attributed to several recent breakthroughs in deep learning. It has shown upbeat performance improvements, but most of the transfer learning applications are confined towards fine-tuning. Transfer learning facilitates the learnability of the networks on domains with less data. However, learning becomes a difficult task with complex domains, such as multi-label food recognition, owing to the number of food classes as well as to the fine-grained nature of food images. For this purpose, we will introduce the S2ML-TL, a new transfer learning framework to leverage the knowledge learnt on a simpler single-label food recognition task onto multi-label food recognition. We will show that negative transfer is also related to uncertainty of the classes. After introducing different methods for uncertainty modelling in Deep learning we will show how it can be useful to guide the process of data augmentation and classifier improvement.

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