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Found 12 papers, 4 papers with code
DNA: Differentially private Neural Augmentation for contact tracing
The COVID19 pandemic had enormous economic and societal consequences.
Protect Your Score: Contact Tracing With Differential Privacy Guarantees
The pandemic in 2020 and 2021 had enormous economic and societal consequences, and studies show that contact tracing algorithms can be key in the early containment of the virus.
Beyond Transfer Learning: Co-finetuning for Action Localisation
Transfer learning is the predominant paradigm for training deep networks on small target datasets.
Impact of Aliasing on Generalization in Deep Convolutional Networks
We investigate the impact of aliasing on generalization in Deep Convolutional Networks and show that data augmentation schemes alone are unable to prevent it due to structural limitations in widely used architectures.
Revisiting the Calibration of Modern Neural Networks
Accurate estimation of predictive uncertainty (model calibration) is essential for the safe application of neural networks.
SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size
Before deploying machine learning models it is critical to assess their robustness.
Representation learning from videos in-the-wild: An object-centric approach
We propose a method to learn image representations from uncurated videos.
On Robustness and Transferability of Convolutional Neural Networks
Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts.
Data Selection for training Semantic Segmentation CNNs with cross-dataset weak supervision
Training convolutional networks for semantic segmentation with strong (per-pixel) and weak (per-bounding-box) supervision requires a large amount of weakly labeled data.
A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation
We show that conventional normalization layers worsen the performance of current Unsupervised Adversarial Domain Adaption (UADA), which is a method to improve network performance on unlabeled datasets and the focus of our research.
Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction
In the hit-or-miss classification experiment, the proposed model outperformed other models in terms of the convergence speed and accuracy.
Applying Deep Learning to Basketball Trajectories
Using a dataset of over 20, 000 three pointers from NBA SportVu data, the models based simply on sequential positional data outperform a static feature rich machine learning model in predicting whether a three-point shot is successful.
