17 citations · 20 across the 3 of their papers we have counts for
8 papers
On the Reproducibility of Neural Network Predictions
Srinadh Bhojanapalli, Kimberly Wilber, Andreas Veit +4
Standard training techniques for neural networks involve multiple sources of randomness, e.g., initialization, mini-batch ordering and in some cases data augmentation. Given that n…
Improving Calibration in Deep Metric Learning With Cross-Example Softmax
Andreas Veit, Kimberly Wilber
Modern image retrieval systems increasingly rely on the use of deep neural networks to learn embedding spaces in which distance encodes the relevance between a given query and imag…
Doubly-stochastic mining for heterogeneous retrieval
Ankit Singh Rawat, Aditya Krishna Menon, Andreas Veit +3
Modern retrieval problems are characterised by training sets with potentially billions of labels, and heterogeneous data distributions across subpopulations (e.g., users of a retri…
Why are Adaptive Methods Good for Attention Models?
Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit +4
While stochastic gradient descent (SGD) is still the \emph{de facto} algorithm in deep learning, adaptive methods like Clipped SGD/Adam have been observed to outperform SGD across…
Semantic Segmentation with Scarce Data
Isay Katsman, Rohun Tripathi, Andreas Veit +1
Semantic segmentation is a challenging vision problem that usually necessitates the collection of large amounts of finely annotated data, which is often quite expensive to obtain.…
How To Backdoor Federated Learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua +2
Federated learning enables thousands of participants to construct a deep learning model without sharing their private training data with each other. For example, multiple smartphon…