23 citations · 71 across the 20 of their papers we have counts for
20 papers
Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural Networks
Runzhong Wang, Yang Li, Junchi Yan +1
Combinatorial optimization (CO) is the fundamental problem at the intersection of computer science, applied mathematics, etc. The inherent hardness in CO problems brings up challen…
LinSATNet: The Positive Linear Satisfiability Neural Networks
Runzhong Wang, Yunhao Zhang, Ziao Guo +3
Encoding constraints into neural networks is attractive. This paper studies how to introduce the popular positive linear satisfiability to neural networks. We propose the first dif…
Boundary Matters: A Bi-Level Active Finetuning Framework
Han Lu, Yichen Xie, Xiaokang Yang +1
The pretraining-finetuning paradigm has gained widespread adoption in vision tasks and other fields, yet it faces the significant challenge of high sample annotation costs. To miti…
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving
Han Lu, Xiaosong Jia, Yichen Xie +3
End-to-end differentiable learning for autonomous driving (AD) has recently become a prominent paradigm. One main bottleneck lies in its voracious appetite for high-quality labeled…
Rethinking Classifier Re-Training in Long-Tailed Recognition: A Simple Logits Retargeting Approach
Han Lu, Siyu Sun, Yichen Xie +3
In the long-tailed recognition field, the Decoupled Training paradigm has demonstrated remarkable capabilities among various methods. This paradigm decouples the training process i…
Poisson Process for Bayesian Optimization
Xiaoxing Wang, Jiaxing Li, Chao Xue +5
BayesianOptimization(BO) is a sample-efficient black-box optimizer, and extensive methods have been proposed to build the absolute function response of the black-box function throu…
SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction
Zelin Peng, Zhengqin Xu, Zhilin Zeng +2
Segment Anything Model (SAM) has received remarkable attention as it offers a powerful and versatile solution for object segmentation in images. However, fine-tuning SAM for downst…
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm
Yichen Xie, Han Lu, Junchi Yan +3
Given the large-scale data and the high annotation cost, pretraining-finetuning becomes a popular paradigm in multiple computer vision tasks. Previous research has covered both the…
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning
Chao Chen, Haoyu Geng, Nianzu Yang +2
Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics directly in continuous time domain for its flexibility. This paper aims to d…
Xformer: Hybrid X-Shaped Transformer for Image Denoising
Jiale Zhang, Yulun Zhang, Jinjin Gu +3
In this paper, we present a hybrid X-shaped vision Transformer, named Xformer, which performs notably on image denoising tasks. We explore strengthening the global representation o…
Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution
Chao Chen, Haoyu Geng, Gang Zeng +4
Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only…
A K-variate Time Series Is Worth K Words: Evolution of the Vanilla Transformer Architecture for Long-term Multivariate Time Series Forecasting
Zanwei Zhou, Ruizhe Zhong, Chen Yang +3
Multivariate time series forecasting (MTSF) is a fundamental problem in numerous real-world applications. Recently, Transformer has become the de facto solution for MTSF, especiall…
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition
Shuai Jia, Bangjie Yin, Taiping Yao +4
Deep learning models have shown their vulnerability when dealing with adversarial attacks. Existing attacks almost perform on low-level instances, such as pixels and super-pixels,…
A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction
Wei Shen, Zelin Peng, Xuehui Wang +6
The rapid development of deep learning has made a great progress in image segmentation, one of the fundamental tasks of computer vision. However, the current segmentation algorithm…
DOTIN: Dropping Task-Irrelevant Nodes for GNNs
Shaofeng Zhang, Feng Zhu, Junchi Yan +2
Scalability is an important consideration for deep graph neural networks. Inspired by the conventional pooling layers in CNNs, many recent graph learning approaches have introduced…
Continual Predictive Learning from Videos
Geng Chen, Wendong Zhang, Han Lu +4
Predictive learning ideally builds the world model of physical processes in one or more given environments. Typical setups assume that we can collect data from all environments at…
Exploring Frequency Adversarial Attacks for Face Forgery Detection
Shuai Jia, Chao Ma, Taiping Yao +3
Various facial manipulation techniques have drawn serious public concerns in morality, security, and privacy. Although existing face forgery classifiers achieve promising performan…
EAutoDet: Efficient Architecture Search for Object Detection
Xiaoxing Wang, Jiale Lin, Junchi Yan +2
Training CNN for detection is time-consuming due to the large dataset and complex network modules, making it hard to search architectures on detection datasets directly, which usua…
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs
Han Lu, Zenan Li, Runzhong Wang +3
Solving combinatorial optimization (CO) on graphs is among the fundamental tasks for upper-stream applications in data mining, machine learning and operations research. Despite the…
ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation
Xiaoxing Wang, Xiangxiang Chu, Yuda Fan +4
Albeit being a prevalent architecture searching approach, differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet re…