507 citations · 2.3k across the 101 of their papers we have counts for
12 papers · 1 filter
DeSCo: Towards Generalizable and Scalable Deep Subgraph Counting
Tianyu Fu, Chiyue Wei, Yu Wang +1
We introduce DeSCo, a scalable neural deep subgraph counting pipeline, designed to accurately predict both the count and occurrence position of queries on target graphs post single…
LLQL: Logistic Likelihood Q-Learning for Reinforcement Learning
Outongyi Lv, Bingxin Zhou
Modern reinforcement learning (RL) can be categorized into online and offline variants. As a pivotal aspect of both online and offline RL, current research on the Bellman equation…
OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models
Enshu Liu, Xuefei Ning, Zinan Lin +2
Diffusion probabilistic models (DPMs) are a new class of generative models that have achieved state-of-the-art generation quality in various domains. Despite the promise, one major…
Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation
Yu Wang, Tongya Zheng, Shunyu Liu +4
Human mobility patterns have shown significant applications in policy-decision scenarios and economic behavior researches. The human mobility simulation task aims to generate human…
Interpretable Outlier Summarization
Yu Wang, Lei Cao, Yizhou Yan +1
Outlier detection is critical in real applications to prevent financial fraud, defend network intrusions, or detecting imminent device failures. To reduce the human effort in evalu…
Out-of-Distribution Detection with Hilbert-Schmidt Independence Optimization
Jingyang Lin, Yu Wang, Qi Cai +4
Outlier detection tasks have been playing a critical role in AI safety. There has been a great challenge to deal with this task. Observations show that deep neural network classifi…
How Powerful are Shallow Neural Networks with Bandlimited Random Weights?
Ming Li, Sho Sonoda, Feilong Cao +2
We investigate the expressive power of depth-2 bandlimited random neural networks. A random net is a neural network where the hidden layer parameters are frozen with random assignm…
Learning Monotone Dynamics by Neural Networks
Yu Wang, Qitong Gao, Miroslav Pajic
Feed-forward neural networks (FNNs) work as standard building blocks in applying artificial intelligence (AI) to the physical world. They allow learning the dynamics of unknown phy…
A New Concept of Deep Reinforcement Learning based Augmented General Sequence Tagging System
Yu Wang, Abhishek Patel, Hongxia Jin
In this paper, a new deep reinforcement learning based augmented general sequence tagging system is proposed. The new system contains two parts: a deep neural network (DNN) based s…
LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions
Yu Wang, Jiayi Liu, Yuxiang Liu +5
We present LADDER, the first deep reinforcement learning agent that can successfully learn control policies for large-scale real-world problems directly from raw inputs composed of…
A Deep Learning Approach for Blind Drift Calibration of Sensor Networks
Yuzhi Wang, Anqi Yang, Xiaoming Chen +3
Temporal drift of sensory data is a severe problem impacting the data quality of wireless sensor networks (WSNs). With the proliferation of large-scale and long-term WSNs, it is be…
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
Huizi Mao, Song Han, Jeff Pool +4
Sparsity helps reduce the computational complexity of deep neural networks by skipping zeros. Taking advantage of sparsity is listed as a high priority in next generation DNN accel…