Contrastive learning has become a key component of self-supervised learning approaches for computer vision. By learning to embed two augmented versions of the same image close to each other and to push the embeddings of different images apart, one can train highly transferable visual representations. As revealed by recent studies, heavy data augmentation and large …

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It …

Abstract: Network science has developed into an essential interdisciplinary framework for discovering patterns in data, finding important nodes and edges, determining how to improve the resilience of systems, etc. Sometimes one knows not only what pairs of nodes are in contact but also when the contacts happen. To include such information about the timing of …

Abstract: TBD Bio: Lexing Xie is Professor of Computer Science at the Australian National University, she leads the ANU Computational Media lab (http://cm.cecs.anu.edu.au). Her research interests are in machine learning, multimedia, social media. Of particular recent interest are stochastic time series models, neural networks for sequences and graphs. Her research is supported by the US …

Abstract: Detecting extreme events in large datasets is a major challenge in climate science research. Current algorithms for extreme event detection are build upon human expertise in defining events based on subjective thresholds of relevant physical variables. Often, multiple competing methods produce vastly different results on the same dataset. Accurate characterization of extreme events in …

Abstract: While news organizations must create original news stories, there are other ways that they can provide value for their subscribers. One way is to use hardware devices and software interfaces to create a better reading experience. By offering a superior reading experience, subscribers will read more often, which will increase the perceived value of …

Abstract: TBD Bio: Senior Researcher, IBS DataScience Group

Abstract: TBD Bio: Assistant Professor, KDI School of Public Policy and Management

Abstract: Neural language models have revolutionized how we model text data as well as a broad range of machine learning methods, even beyond methods for natural language processing. One of the first, simplest, and most widely used methods is the skip-gram negative sampling model, or simply word2vec, which allows us to obtain vector representations of …