With the growing volume of publications in the Computer Science (CS) discipline, tracking the research evolution and predicting the future research trending topics are of great importance for researchers to keep up with the rapid progress of research. Within a research area, there are many top conferences that publish the latest research results. These conferences mutually influence each other and jointly promote the development of the research area. To predict the trending topics of mutually influenced conferences, we propose a correlated neural influence model, which has the ability to capture the sequential properties of research evolution in each individual conference and discover the dependencies among different conferences simultaneously. The experiments conducted on a scientific dataset including conferences in artificial intelligence and data mining show that our model consistently outperforms the other state-of-the-art methods. We also demonstrate the interpretability and predictability of the proposed model by providing its answers to two questions of concern, i.e., what the next rising trending topics are and for each conference who the most influential peer is.
With the increasing volume and impact of communication on social media, social media analysis has become one of the most trending topics in natural language research, which can be observed in a growing number of workshops and conferences dedicated to this topic, projects funded, and research centers established. As a result, a number of social media resources containing chats, online commentaries, reviews, blogs, emails, forums, etc., as well as audio and video recordings, have been accumulated in the repositories of CLARIN centers. What is more, due to their distinct communicative characteristics, they pose new technical challenges for the standard natural language processing tools as well as new legal and ethical challenges for the dissemination of such resources, which has also been addressed by CLARIN, making the available infrastructure an important means for attracting new users to the CLARIN community.
Peer review is an essential process that subjects new research to the scrutiny of other experts in the same field. Today's top Machine Learning (ML) conferences are heavily reliant on peer review as it allows them to gauge submitted academic papers' quality and suitability. However, a series of unsettling incidents and heated discussions on social media have now put the peer review process itself under scrutiny. The annual Computer Vision and Pattern Recognition (CVPR) Conference is one of the world's top three academic gatherings in the field of computer vision (along with ICCV and ECCV). A paper accepted to CVPR 2018 recently came under question when a Reddit user claimed the authors' proposed method could not achieve the accuracy promised.
While artificial intelligence may be powering Siri, Google searches, and the advance of self-driving cars, many people still have sci-fi-inspired notions of what AI actually looks like and how it will affect our lives. AI-focused conferences give researchers and business executives a clear view of what is already working and what is coming down the road. To bring AI researchers from academia and industry together to share their work, learn from one another, and inspire new ideas and collaborations, there are a plethora of AI-focused conferences around the world. There's a growing number of AI conferences geared toward business leaders who want to learn how to use artificial intelligence and related machine learning and deep learning to propel their companies beyond their competitors. So, whether you're a post-doc, a professor working on robotics, or a programmer for a major company, there are conferences out there to help you code better, network with other researchers, and show off your latest papers.
Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we revisit this question from a quantitative perspective. Concretely, we collect 54K abstracts of papers published between 2007 and 2016 in leading machine learning journals and conferences. We then use machine learning in order to determine the top 10 topics in machine learning. We not only include models, but provide a holistic view across optimization, data, features, etc. This quantitative approach allows reducing the bias of surveys. It reveals new and up-to-date insights into what the 10 most prolific topics in machine learning research are. This allows researchers to identify popular topics as well as new and rising topics for their research.