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Reflection on one’s personal data can be an effective tool for supporting wellbeing. However, current wellbeing reflection support tools tend to offer a one-size-fits-all approach, ignoring the diversity of people’s wellbeing goals and their agency in ...
Cloud providers often have resources that are not being fully utilized, and they may offer them at a lower cost to make up for the reduced availability of these resources. However, customers may be hesitant to use such offerings (such as spot VMs) as ...
Classically, data interpolation with a parametrized model class is possible as long as the number of parameters is larger than the number of equations to be satisfied. A puzzling phenomenon in deep learning is that models are trained with many more ...
Data scientists require rich mental models of how AI systems behave to effectively train, debug, and work with them. Despite the prevalence of AI analysis tools, there is no general theory describing how people make sense of what their models have ...
We propose to accelerate use-inspired basic research in causal AI through a suite of causal tools and libraries that simultaneously provides core causal AI functionality to practitioners and creates a platform for research advances to be rapidly ...
With the availability of massive labeled training data, powerful machine learning models can be trained. However, the traditional I.I.D. assumption that the training and testing data should follow the same distribution is often violated in reality. ...
Knowledge in NLP has been a rising trend especially after the advent of large-scale pre-trained models. Knowledge is critical to equip statistics-based models with common sense, logic and other external information. In this tutorial, we will introduce ...
User engagement prediction plays a critical role in designing interaction strategies to grow user engagement and increase revenue in online social platforms. Through the in-depth analysis of the real-world data from the world's largest professional ...
Extreme Classification (XC) seeks to tag data points with the most relevant subset of labels from an extremely large label set. Performing deep XC with dense, learnt representations for data points and labels has attracted much attention due to its ...
Edge computing is being widely used for video analytics. To alleviate the inherent tension between accuracy and cost, various video analytics pipelines have been proposed to optimize the usage of GPU on edge nodes. Nonetheless, we find that GPU compute ...
Complex reasoning aims to draw a correct inference based on complex rules. As a hallmark of human intelligence, it involves a degree of explicit reading comprehension, interpretation of logical knowledge and complex rule application. In this paper, we ...
Camera-based contactless photoplethysmography refers to a set of popular techniques for contactless physiological measurement. The current state-of-the-art neural models are typically trained in a supervised manner using videos accompanied by gold ...
Systems for training massive deep learning models (billions of parameters) today assume and require specialized "hyperclusters": hundreds or thousands of GPUs wired with specialized high-bandwidth interconnects such as NV-Link and Infiniband. Besides ...
Writing formulas on the spreadsheet grid is arguably the most widely practiced form of programming. Still, studies highlight the difficulties experienced by end-user programmers when learning and using traditional formulas, especially for slightly ...
Knowledge distillation (KD) is an effective strategy for neural machine translation (NMT) to improve the performance of a student model. Usually, the teacher can guide the student to be better by distilling the soft label or data knowledge from the ...
Leveraging sparsity in deep neural network (DNN) models is promising for accelerating model inference. Yet existing GPUs can only leverage the sparsity from weights but not activations, which are dynamic, unpredictable, and hence challenging to exploit. ...
Multi-agent reinforcement learning (MARL) has been increasingly explored to learn the cooperative policy towards maximizing a certain global reward. Many existing studies take advantage of graph neural networks (GNN) in MARL to propagate critical ...
We design a prediction market to recover a complete and fully general probability distribution over a random variable. Traders buy and sellinterval securities that pay $1 if the outcome falls into an interval and $0 otherwise. Our market takes the form ...
Affinity, which represents whether two pixels belong to a same instance, is an equivalent representation to the instance segmentation labels. Conventional works do not make an explicit exploration on the affinity. In this article, we present two instance ...
There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging. Existing machine learning systems struggle to generalize well to unseen subjects or contexts and can often contain ...