44 citations · 106 across the 15 of their papers we have counts for
15 papers
The curse of random quantum data
Kaining Zhang, Junyu Liu, Liu Liu +3
Quantum machine learning, which involves running machine learning algorithms on quantum devices, may be one of the most significant flagship applications for these devices. Unlike…
Stochastic Optimization for Non-convex Problem with Inexact Hessian Matrix, Gradient, and Function
Liu Liu, Xuanqing Liu, Cho-Jui Hsieh +1
Trust-region (TR) and adaptive regularization using cubics (ARC) have proven to have some very appealing theoretical properties for non-convex optimization by concurrently computin…
On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling
Chuang Liu, Yibing Zhan, Baosheng Yu +4
A pooling operation is essential for effective graph-level representation learning, where the node drop pooling has become one mainstream graph pooling technology. However, current…
Balancing Stability and Plasticity through Advanced Null Space in Continual Learning
Yajing Kong, Liu Liu, Zhen Wang +1
Continual learning is a learning paradigm that learns tasks sequentially with resources constraints, in which the key challenge is stability-plasticity dilemma, i.e., it is uneasy…
Online Continual Learning with Contrastive Vision Transformer
Zhen Wang, Liu Liu, Yajing Kong +2
Online continual learning (online CL) studies the problem of learning sequential tasks from an online data stream without task boundaries, aiming to adapt to new data while allevia…
Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits
Kaining Zhang, Liu Liu, Min-Hsiu Hsieh +1
Variational quantum circuits have been widely employed in quantum simulation and quantum machine learning in recent years. However, quantum circuits with random structures have poo…
Resistance Training using Prior Bias: toward Unbiased Scene Graph Generation
Chao Chen, Yibing Zhan, Baosheng Yu +3
Scene Graph Generation (SGG) aims to build a structured representation of a scene using objects and pairwise relationships, which benefits downstream tasks. However, current SGG me…
Toward Trainability of Deep Quantum Neural Networks
Kaining Zhang, Min-Hsiu Hsieh, Liu Liu +1
Quantum Neural Networks (QNNs) with random structures have poor trainability due to the exponentially vanishing gradient as the circuit depth and the qubit number increase. This re…
SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks
Weigang Lu, Yibing Zhan, Binbin Lin +6
Graph Convolutional Networks (GCNs) suffer from performance degradation when models go deeper. However, earlier works only attributed the performance degeneration to over-smoothing…
Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation
Ye Huang, Di Kang, Wenjing Jia +2
Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation. H…
Toward Trainability of Quantum Neural Networks
Kaining Zhang, Min-Hsiu Hsieh, Liu Liu +1
Quantum Neural Networks (QNNs) have been recently proposed as generalizations of classical neural networks to achieve the quantum speed-up. Despite the potential to outperform clas…
On the Guaranteed Almost Equivalence between Imitation Learning from Observation and Demonstration
Zhihao Cheng, Liu Liu, Aishan Liu +3
Imitation learning from observation (LfO) is more preferable than imitation learning from demonstration (LfD) due to the nonnecessity of expert actions when reconstructing the expe…
Quantum Gram-Schmidt Processes and Their Application to Efficient State Read-out for Quantum Algorithms
Kaining Zhang, Min-Hsiu Hsieh, Liu Liu +1
Many quantum algorithms that claim speed-up over their classical counterparts only generate quantum states as solutions instead of their final classical description. The additional…
Quantum algorithm for finding the negative curvature direction in non-convex optimization
Kaining Zhang, Min-Hsiu Hsieh, Liu Liu +1
We present an efficient quantum algorithm aiming to find the negative curvature direction for escaping the saddle point, which is the critical subroutine for many second-order non-…
Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient
Liu Liu, Xuanqing Liu, Cho-Jui Hsieh +1
Trust region and cubic regularization methods have demonstrated good performance in small scale non-convex optimization, showing the ability to escape from saddle points. Each iter…