377 citations · 392 across the 5 of their papers we have counts for
5 papers · 1 filter
Large-scale Dataset Pruning with Dynamic Uncertainty
Muyang He, Shuo Yang, Tiejun Huang +1
The state of the art of many learning tasks, e.g., image classification, is advanced by collecting larger datasets and then training larger models on them. As the outcome, the incr…
Improving Convergence and Generalization Using Parameter Symmetries
Bo Zhao, Robert M. Gower, Robin Walters +1
In many neural networks, different values of the parameters may result in the same loss value. Parameter space symmetries are loss-invariant transformations that change the model p…
Concentric Spherical GNN for 3D Representation Learning
James Fox, Bo Zhao, Sivasankaran Rajamanickam +2
Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and c…
Dataset Condensation with Differentiable Siamese Augmentation
Bo Zhao, Hakan Bilen
In many machine learning problems, large-scale datasets have become the de-facto standard to train state-of-the-art deep networks at the price of heavy computation load. In this pa…
iDLG: Improved Deep Leakage from Gradients
Bo Zhao, Konda Reddy Mopuri, Hakan Bilen
It is widely believed that sharing gradients will not leak private training data in distributed learning systems such as Collaborative Learning and Federated Learning, etc. Recentl…