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New NSF-CSIRO Awards Will Tackle Responsible and Ethical AI

New NSF-CSIRO Awards Will Tackle Responsible and Ethical AI
datanami.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from datanami.com Daily Mail and Mail on Sunday newspapers.

United States , New South Wales , Sethuraman Panchanathan , Larry Marshall , George Mason University , Emory University , Arizona State University , Rensselaer Polytechnic Institute , University Of Nebraska , University Of Technology Sydney , University Of Texas At Austin , University Of New South Wales , University Of Melbourne , Us National Science Foundation , Science Foundation , Executive Larry , Colleague Letter , Toward Net Zero , Infectious Disease , Sequential Collective Decision Making , New South , Infectious Disease Spread , Representation Learning , Fair Teaming , Crisis Response , Technology Sydney ,

"Kernelized Few-shot Object Detection with Efficient Integral Aggregati" by Shan Zhang, Lei Wang et al.

We design a Kernelized Few-shot Object Detector by leveraging kernelized matrices computed over multiple proposal regions, which yield expressive non-linear representations whose model complexity is learned on the fly. Our pipeline contains several modules. An Encoding Network encodes support and query images. Our Kernelized Autocorrelation unit forms the linear, polynomial and RBF kernelized representations from features extracted within support regions of support images. These features are then cross-correlated against features of a query image to obtain attention weights, and generate query proposal regions via an Attention Region Proposal Net. As the query proposal regions are many, each described by the linear, polynomial and RBF kernelized matrices, their formation is costly but that cost is reduced by our proposed Integral Region-of-Interest Aggregation unit. Finally, the Multi-head Relation Net combines all kernelized (second-order) representations with the first-order feature ....

Kernelized Few Shot Object Detector , Encoding Network , Kernelized Autocorrelation , Attention Region Proposal , Integral Region Of Interest Aggregation , Multi Head Relation Net , Computer Vision Theory , Machine Learning , Ecognition Detection , Representation Learning ,

"Node Representation Learning in Graph via Node-to-Neighbourhood Mutual" by Wei Dong, Junsheng Wu et al.

The key towards learning informative node representations in graphs lies in how to gain contextual information from the neighbourhood. In this work, we present a simple-yet-effective self-supervised node representation learning strategy via directly maximizing the mutual information between the hidden representations of nodes and their neighbourhood, which can be theoretically justified by its link to graph smoothing. Following InfoNCE, our framework is optimized via a surrogate contrastive loss, where the positive selection underpins the quality and efficiency of rep-resentation learning. To this end, we propose a topology-aware positive sampling strategy, which samples positives from the neighbourhood by considering the structural dependencies between nodes and thus enables positive selection upfront. In the extreme case when only one positive is sampled, we fully avoid expensive neighbourhood aggregation. Our methods achieve promising performance on various node classification datas ....

Representation Learning , Elf Semi Meta Unsupervised Learning ,

"Class Similarity Weighted Knowledge Distillation for Continual Semanti" by Minh Hieu Phan, The Anh Ta et al.

Deep learning models are known to suffer from the problem of catastrophic forgetting when they incrementally learn new classes. Continual learning for semantic segmentation (CSS) is an emerging field in computer vision. We identify a problem in CSS: A model tends to be confused between old and new classes that are visually similar, which makes it forget the old ones. To address this gap, we propose REMINDER - a new CSS framework and a novel class similarity knowledge distillation (CSW-KD) method. Our CSW-KD method distills the knowledge of a previous model on old classes that are similar to the new one. This provides two main benefits: (i) selectively revising old classes that are more likely to be forgotten, and (ii) better learning new classes by relating them with the previously seen classes. Extensive experiments on Pascal-Voc 2012 and ADE20k datasets show that our approach outperforms state-of-the-art methods on standard CSS settings by up to 7.07% and 8.49%, respectively. ....

Computer Vision Theory , Deep Learning Architectures And Techniques , Fficient Learning And Inferences , Rouping And Shape Analysis , Representation Learning , Cene Analysis And Understanding , Ision Applications And Systems ,

New NSF-CSIRO Awards Will Tackle Responsible and Ethical AI

New NSF-CSIRO Awards Will Tackle Responsible and Ethical AI
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United States , New South Wales , Sethuraman Panchanathan , Larry Marshall , George Mason University , Emory University , Arizona State University , Rensselaer Polytechnic Institute , University Of Nebraska , University Of Technology Sydney , University Of Texas At Austin , University Of New South Wales , University Of Melbourne , Us National Science Foundation , Science Foundation , Executive Larry , Colleague Letter , Toward Net Zero , Infectious Disease , Sequential Collective Decision Making , New South , Infectious Disease Spread , Representation Learning , Fair Teaming , Crisis Response , Technology Sydney ,