We show that we can learn highly informative posteriors from the source task, through supervised or self-supervised approaches, which then serve as the basis for priors that modify the whole loss surface on the downstream task. This approach enables significant performance gains and more data-efficient learning on a variety of downstream classification and segmentation tasks
Study the generalization properties of infinite ensembles of infinitely-wide neural networks. We report analytical and empirical investigations in the search for signals that correlate with generalization.
A semi supervised model for detecting anomalies in videos inspiredby the Video Pixel Network. We extend the Convolutional LSTM video encoder of the VPN with a novel convolutional based attention. This approach could be a component in applications requiring visual common sense.
We explored whether deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.
A deep-learning based system, which performs real-time detection of diverse visual corruptions in videos. Developing this system involved challenging data science aspects to enable detection of small distortions with low false alert rates.