297 citations · 958 across the 50 of their papers we have counts for
19 papers · 1 filter
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy +6
Transformer-based models have gained large popularity and demonstrated promising results in long-term time-series forecasting in recent years. In addition to learning attention in…
Artificial Intelligence Security Competition (AISC)
Yinpeng Dong, Peng Chen, Senyou Deng +49
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artifi…
Robust Perception through Equivariance
Chengzhi Mao, Lingyu Zhang, Abhishek Joshi +3
Deep networks for computer vision are not reliable when they encounter adversarial examples. In this paper, we introduce a framework that uses the dense intrinsic constraints in na…
Visual Prompt Tuning for Test-time Domain Adaptation
Yunhe Gao, Xingjian Shi, Yi Zhu +5
Models should be able to adapt to unseen data during test-time to avoid performance drops caused by inevitable distribution shifts in real-world deployment scenarios. In this work,…
Nano-Resolution Visual Identifiers Enable Secure Monitoring in Next-Generation Cyber-Physical Systems
Hao Wang, Xiwen Chen, Abolfazl Razi +3
Today's supply chains heavily rely on cyber-physical systems such as intelligent transportation, online shopping, and E-commerce. It is advantageous to track goods in real-time by…
Fast Key Points Detection and Matching for Tree-Structured Images
Hao Wang, Xiwen Chen, Abolfazl Razi +1
This paper offers a new authentication algorithm based on image matching of nano-resolution visual identifiers with tree-shaped patterns. The algorithm includes image-to-tree conve…
1st ICLR International Workshop on Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data (PAIR^2Struct)
Hao Wang, Wanyu Lin, Hao He +3
Recent years have seen advances on principles and guidance relating to accountable and ethical use of artificial intelligence (AI) spring up around the globe. Specifically, Data Pr…
Landscape Learning for Neural Network Inversion
Ruoshi Liu, Chengzhi Mao, Purva Tendulkar +2
Many machine learning methods operate by inverting a neural network at inference time, which has become a popular technique for solving inverse problems in computer vision, robotic…
Exploring evolution-aware & -free protein language models as protein function predictors
Mingyang Hu, Fajie Yuan, Kevin K. Yang +5
Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions. In particula…
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning
Dun Zeng, Shiyu Liu, Siqi Liang +4
Federated learning enables isolated clients to train a shared model collaboratively by aggregating the locally-computed gradient updates. However, privacy information could be leak…
DH-GAN: A Physics-driven Untrained Generative Adversarial Network for 3D Microscopic Imaging using Digital Holography
Xiwen Chen, Hao Wang, Abolfazl Razi +2
Digital holography is a 3D imaging technique by emitting a laser beam with a plane wavefront to an object and measuring the intensity of the diffracted waveform, called holograms.…
Causal Transportability for Visual Recognition
Chengzhi Mao, Kevin Xia, James Wang +4
Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features. Our main observation is that image classifiers may perform poo…
Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms
Diederick Vermetten, Hao Wang, Manuel López-Ibañez +2
The stochastic nature of iterative optimization heuristics leads to inherently noisy performance measurements. Since these measurements are often gathered once and then used repeat…
Switching between Numerical Black-box Optimization Algorithms with Warm-starting Policies
Dominik Schröder, Diederick Vermetten, Hao Wang +2
When solving optimization problems with black-box approaches, the algorithms gather valuable information about the problem instance during the optimization process. This informatio…
DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow
Zihua Zheng, Ni Nie, Zhi Ling +4
Recently, the dense correlation volume method achieves state-of-the-art performance in optical flow. However, the correlation volume computation requires a lot of memory, which mak…
Adversarial samples for deep monocular 6D object pose estimation
Jinlai Zhang, Weiming Li, Shuang Liang +2
Estimating 6D object pose from an RGB image is important for many real-world applications such as autonomous driving and robotic grasping. Recent deep learning models have achieved…
Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks
Haorui Wang, Haoteng Yin, Muhan Zhang +1
Graph neural networks (GNN) have shown great advantages in many graph-based learning tasks but often fail to predict accurately for a task-based on sets of nodes such as link/motif…
Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm
Charles Moussa, Hao Wang, Thomas Bäck +1
As combinatorial optimization is one of the main quantum computing applications, many methods based on parameterized quantum circuits are being developed. In general, a set of para…
Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study
Xiwen Chen, Hao Wang, Abolfazl Razi +6
Driving safety analysis has recently experienced unprecedented improvements thanks to technological advances in precise positioning sensors, artificial intelligence (AI)-based safe…