116 citations · 430 across the 65 of their papers we have counts for
11 papers · 1 filter
TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting
Lingyu Jiang, Lingyu Xu, Peiran Li +14
We propose TimePre, a simple framework that unifies the efficiency of Multilayer Perceptron (MLP)-based models with the distributional flexibility of Multiple Choice Learning (MCL)…
KANMixer: a minimal KAN-centered mixer for long-term time series forecasting
Lingyu Jiang, Dengzhe Hou, Yuping Wang +9
Long-term time series forecasting (LTSF) underpins critical applications from energy management to weather prediction, yet achieving reliable multi-step-ahead accuracy remains chal…
Auto-Encoding Adversarial Imitation Learning
Kaifeng Zhang, Rui Zhao, Ziming Zhang +1
Reinforcement learning (RL) provides a powerful framework for decision-making, but its application in practice often requires a carefully designed reward function. Adversarial Imit…
Don't Knock! Rowhammer at the Backdoor of DNN Models
M. Caner Tol, Saad Islam, Andrew J. Adiletta +2
State-of-the-art deep neural networks (DNNs) have been proven to be vulnerable to adversarial manipulation and backdoor attacks. Backdoored models deviate from expected behavior on…
-GAIL: Learning -Divergence for Generative Adversarial Imitation Learning
Xin Zhang, Yanhua Li, Ziming Zhang +1
Imitation learning (IL) aims to learn a policy from expert demonstrations that minimizes the discrepancy between the learner and expert behaviors. Various imitation learning algori…
RNN Training along Locally Optimal Trajectories via Frank-Wolfe Algorithm
Yun Yue, Ming Li, Venkatesh Saligrama +1
We propose a novel and efficient training method for RNNs by iteratively seeking a local minima on the loss surface within a small region, and leverage this directional vector for…
Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs
Yecheng Lyu, Xinming Huang, Ziming Zhang
Graph convolutional networks (GCNs) suffer from the irregularity of graphs, while more widely-used convolutional neural networks (CNNs) benefit from regular grids. To bridge the ga…
White-Box Adversarial Defense via Self-Supervised Data Estimation
Zudi Lin, Hanspeter Pfister, Ziming Zhang
In this paper, we study the problem of how to defend classifiers against adversarial attacks that fool the classifiers using subtly modified input data. In contrast to previous wor…
RNNs Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients?
Anil Kag, Ziming Zhang, Venkatesh Saligrama
Recurrent neural networks (RNNs) are particularly well-suited for modeling long-term dependencies in sequential data, but are notoriously hard to train because the error backpropag…
Equilibrated Recurrent Neural Network: Neuronal Time-Delayed Self-Feedback Improves Accuracy and Stability
Ziming Zhang, Anil Kag, Alan Sullivan +1
We propose a novel {\it Equilibrated Recurrent Neural Network} (ERNN) to combat the issues of inaccuracy and instability in conventional RNNs. Drawing upon the concept of autapse i…
Time-Delay Momentum: A Regularization Perspective on the Convergence and Generalization of Stochastic Momentum for Deep Learning
Ziming Zhang, Wenju Xu, Alan Sullivan
In this paper we study the problem of convergence and generalization error bound of stochastic momentum for deep learning from the perspective of regularization. To do so, we first…