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25 papers · 2 filters
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing
Jinyuan Jia, Xiaoyu Cao, Binghui Wang +1
It is well-known that classifiers are vulnerable to adversarial perturbations. To defend against adversarial perturbations, various certified robustness results have been derived.…
RealMix: Towards Realistic Semi-Supervised Deep Learning Algorithms
Varun Nair, Javier Fuentes Alonso, Tony Beltramelli
Semi-Supervised Learning (SSL) algorithms have shown great potential in training regimes when access to labeled data is scarce but access to unlabeled data is plentiful. However, o…
VarNet: Variational Neural Networks for the Solution of Partial Differential Equations
Reza Khodayi-Mehr, Michael M. Zavlanos
In this paper we propose a new model-based unsupervised learning method, called VarNet, for the solution of partial differential equations (PDEs) using deep neural networks (NNs).…
A Tale of Two-Timescale Reinforcement Learning with the Tightest Finite-Time Bound
Gal Dalal, Balazs Szorenyi, Gugan Thoppe
Policy evaluation in reinforcement learning is often conducted using two-timescale stochastic approximation, which results in various gradient temporal difference methods such as G…
Neural Predictor for Neural Architecture Search
Wei Wen, Hanxiao Liu, Hai Li +3
Neural Architecture Search methods are effective but often use complex algorithms to come up with the best architecture. We propose an approach with three basic steps that is conce…
Dynamic Embedding on Textual Networks via a Gaussian Process
Pengyu Cheng, Yitong Li, Xinyuan Zhang +3
Textual network embedding aims to learn low-dimensional representations of text-annotated nodes in a graph. Prior work in this area has typically focused on fixed graph structures;…