Search Results for author:
Found 22 papers, 10 papers with code
Stochastic Optimization for Non-convex Problem with Inexact Hessian Matrix, Gradient, and Function
In this paper, we explore a family of stochastic TR and ARC methods that can simultaneously provide inexact computations of the Hessian matrix, gradient, and function values.
FastEnsemble: Benchmarking and Accelerating Ensemble-based Uncertainty Estimation for Image-to-Image Translation
In addition, we introduce FastEnsemble, a fast ensemble method which only requires less than $8\%$ of the full-ensemble training time to generate a new ensemble member.
Label Disentanglement in Partition-based Extreme Multilabel Classification
Partition-based methods are increasingly-used in extreme multi-label classification (XMC) problems due to their scalability to large output spaces (e. g., millions or more).
How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers
For end-to-end efficiency, unlike previous work that assumes random hyperparameter tuning, which over-emphasizes the tuning time, we propose to evaluate with a bandit hyperparameter tuning strategy.
Improving the Speed and Quality of GAN by Adversarial Training
Generative adversarial networks (GAN) have shown remarkable results in image generation tasks.
Provably Robust Metric Learning
Metric learning is an important family of algorithms for classification and similarity search, but the robustness of learned metrics against small adversarial perturbations is less studied.
How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework
In this paper, we propose a new continuous neural network framework called Neural Stochastic Differential Equation (Neural SDE), which naturally incorporates various commonly used regularization mechanisms based on random noise injection.
Evaluations and Methods for Explanation through Robustness Analysis
In this paper, we establish a novel set of evaluation criteria for such feature based explanations by robustness analysis.
Learning to Encode Position for Transformer with Continuous Dynamical Model
The main reason is that position information among input units is not inherently encoded, i. e., the models are permutation equivalent; this problem justifies why all of the existing models are accompanied by a sinusoidal encoding/embedding layer at the input.
Ranked #5 on
Semantic Textual Similarity
on MRPC
Gradient Boosting Neural Networks: GrowNet
A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''.
GraphDefense: Towards Robust Graph Convolutional Networks
Inspired by the previous works on adversarial defense for deep neural networks, and especially adversarial training algorithm, we propose a method called GraphDefense to defend against the adversarial perturbations.
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning
In this paper, we proposed a general framework for data poisoning attacks to graph-based semi-supervised learning (G-SSL).
Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective
Furthermore, we show that dual solutions for these QP problems could give us a valid lower bound of the adversarial perturbation that can be used for formal robustness verification, giving us a nice view of attack/verification for NN models.
Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise
In this paper, we propose a new continuous neural network framework called Neural Stochastic Differential Equation (Neural SDE) network, which naturally incorporates various commonly used regularization mechanisms based on random noise injection.
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99. 36 on the PPI dataset, while the previous best result was 98. 71 by [16].
Ranked #1 on
Node Classification
on Amazon2M
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
Instead, we model randomness under the framework of Bayesian Neural Network (BNN) to formally learn the posterior distribution of models in a scalable way.
Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient
Trust region and cubic regularization methods have demonstrated good performance in small scale non-convex optimization, showing the ability to escape from saddle points.
Fast Variance Reduction Method with Stochastic Batch Size
In this paper we study a family of variance reduction methods with randomized batch size---at each step, the algorithm first randomly chooses the batch size and then selects a batch of samples to conduct a variance-reduced stochastic update.
Rob-GAN: Generator, Discriminator, and Adversarial Attacker
Adversarial training is the technique used to improve the robustness of discriminator by combining adversarial attacker and discriminator in the training phase.
Better Generalization by Efficient Trust Region Method
Solving this subproblem is non-trivial---existing methods have only sub-linear convergence rate.
Towards Robust Neural Networks via Random Self-ensemble
In this paper, we propose a new defense algorithm called Random Self-Ensemble (RSE) by combining two important concepts: {\bf randomness} and {\bf ensemble}.
An inexact subsampled proximal Newton-type method for large-scale machine learning
We propose a fast proximal Newton-type algorithm for minimizing regularized finite sums that returns an $\epsilon$-suboptimal point in $\tilde{\mathcal{O}}(d(n + \sqrt{\kappa d})\log(\frac{1}{\epsilon}))$ FLOPS, where $n$ is number of samples, $d$ is feature dimension, and $\kappa$ is the condition number.
