17 citations · 35 across the 9 of their papers we have counts for
9 papers
A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes
Tianye Wang, Haoxuan Yao, Tai Sing Lee +5
The visual system evolved to process natural scenes, yet most of our understanding of the topology and function of visual cortex derives from studies using artificial stimuli. To g…
Realistic Safety-critical Scenarios Search for Autonomous Driving System via Behavior Tree
Ping Zhang, Lingfeng Ming, Tingyi Yuan +5
The simulation-based testing of Autonomous Driving Systems (ADSs) has gained significant attention. However, current approaches often fall short of accurately assessing ADSs for tw…
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
David Henry Mguni, Taher Jafferjee, Jianhong Wang +7
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agent systems, coordinated exploration and behaviour is critical for agents to joint…
DSBERT:Unsupervised Dialogue Structure learning with BERT
Bingkun Chen, Shaobing Dai, Shenghua Zheng +2
Unsupervised dialogue structure learning is an important and meaningful task in natural language processing. The extracted dialogue structure and process can help analyze human dia…
Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks
Xiang Huang, Hongsheng Liu, Beiji Shi +11
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a pro…
Meta-Auto-Decoder for Solving Parametric Partial Differential Equations
Xiang Huang, Zhanhong Ye, Hongsheng Liu +11
Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i.e., PDEs with different physical parameters, bo…
IID-GAN: an IID Sampling Perspective for Regularizing Mode Collapse
Yang Li, Liangliang Shi, Junchi Yan
Despite its success, generative adversarial networks (GANs) still suffer from mode collapse, i.e., the generator can only map latent variables to a partial set of modes in the targ…
Interpretable Models in ANNs
Yang Li
Artificial neural networks are often very complex and too deep for a human to understand. As a result, they are usually referred to as black boxes. For a lot of real-world problems…
Inferring Latent dimension of Linear Dynamical System with Minimum Description Length
Yang Li
Time-invariant linear dynamical system arises in many real-world applications,and its usefulness is widely acknowledged. A practical limitation with this model is that its latent d…