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Semi-Supervised Clustering via Dynamic Graph Structure Learning
However, existing methods adopt a static affinity matrix to learn the low-dimensional representations of data points and do not optimize the affinity matrix during the learning process.
Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization
In this paper, to eliminate the effort for tuning the momentum-related hyperparameter, we propose a new adaptive momentum inspired by the optimal choice of the heavy ball momentum for quadratic optimization.
Deep Learning with Data Privacy via Residual Perturbation
Empirically, we show that residual perturbation outperforms the state-of-the-art DP stochastic gradient descent (DPSGD) in both membership privacy protection and maintaining the DL models' utility.
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