Cloud AI

We propose a novel model that integrates machine learning into compartmental disease modeling to predict the progression of Covid-19. Our model incorporates explainable encoding of information-bearing covariates to improve performance. The motivation to maintain explainability is two-fold: the behavior of the resulting model will be credible with epidemiologists, and will instill confidence in...

Quantifying the value of datum is a fundamental problem in machine learning. Besides building insights about the learning task, data valuation has applications in diverse use-cases, such as domain adaptation, corrupted sample discovery, and robust learning. To adaptively learn data values jointly with the predictive model, we propose a meta learning framework - named Data Valuator using...

Deep neural networks (DNNs) yield poorly-calibrated confidence estimates when their raw predicted posterior estimates are considered. Towards obtaining perfectly-calibrated confidence estimates, we propose a novel framework, named as `Distance-Based Learning from Errors' (DBLE). DBLE is based on two fundamental principles: (i) learning a representation space where distances correspond to...

We propose a novel framework, named learn to transfer learn (L2TL), to improve transfer learning on a target dataset by judicious extraction of information from a source dataset. Our framework considers joint optimization of vastly-shared weights between models of source and target tasks, and employs adaptive coefficients for scaling of constituent loss terms. The adaptation of the coefficients...

Active learning (AL) combines data labeling and model training to minimize the labeling cost by prioritizing the selection of high value data that can best improve model performance. In pool-based active learning, accessible unlabeled data are not used for model training in most conventional methods. Here, we propose to unify unlabeled sample selection and model training towards minimizing...

Collecting large-scale data with clean labels for supervised training of neural networks is practically challenging. Although noisy labels are usually cheap to acquire, existing methods suffer a lot from label noise. This paper targets at the challenge of robust training at high label noise regimes. The key insight to achieve this goal is to wisely leverage a small trusted set to estimate...

Multi-horizon prediction problems often contain a complex mix of inputs -- including static covariates, known future inputs, and other exogenous time series -- without any prior information on how they interact with the target. While several deep learning models have been proposed for multi-step prediction, they typically comprise black-box models which do not account for the full range of...

We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet. TabNet uses sequential attention to choose which features to reason from at each decision step, enabling interpretability and more efficient learning as the learning capacity is used for the most salient features. We demonstrate that TabNet outperforms other neural network and...

The recent direction of unpaired image-to-image translation is on one hand very exciting as it alleviates the big burden in obtaining label-intensive pixel-to-pixel supervision, but it is on the other hand not fully satisfactory due to the presence of artifacts and degenerated transformations. In this paper, we take a manifold view of the problem by introducing a smoothness constraint over the...