31 citations · 78 across the 14 of their papers we have counts for
15 papers
On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning
Jianhong Bai, Zuozhu Liu, Hualiang Wang +4
Though Self-supervised learning (SSL) has been widely studied as a promising technique for representation learning, it doesn't generalize well on long-tailed datasets due to the ma…
A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis
Jiaxiang Liu, Tianxiang Hu, Yan Zhang +3
Zero-shot medical image classification is a critical process in real-world scenarios where we have limited access to all possible diseases or large-scale annotated data. It involve…
DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes
Shenghao Hao, Peiyuan Liu, Yibing Zhan +5
Cross-view multi-object tracking aims to link objects between frames and camera views with substantial overlaps. Although cross-view multi-object tracking has received increased at…
Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework
Yiming Chen, Yan Zhang, Bin Wang +2
Most sentence embedding techniques heavily rely on expensive human-annotated sentence pairs as the supervised signals. Despite the use of large-scale unlabeled data, the performanc…
Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-tailed Learning
Hualiang Wang, Siming Fu, Xiaoxuan He +3
Long-tailed learning aims to tackle the crucial challenge that head classes dominate the training procedure under severe class imbalance in real-world scenarios. However, little at…
Towards Federated Long-Tailed Learning
Zihan Chen, Songshang Liu, Hualiang Wang +3
Data privacy and class imbalance are the norm rather than the exception in many machine learning tasks. Recent attempts have been launched to, on one side, address the problem of l…
Federated Stochastic Gradient Descent Begets Self-Induced Momentum
Howard H. Yang, Zuozhu Liu, Yaru Fu +2
Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistic…
Track without Appearance: Learn Box and Tracklet Embedding with Local and Global Motion Patterns for Vehicle Tracking
Gaoang Wang, Renshu Gu, Zuozhu Liu +3
Vehicle tracking is an essential task in the multi-object tracking (MOT) field. A distinct characteristic in vehicle tracking is that the trajectories of vehicles are fairly smooth…
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
Yan Zhang, Zhijiang Guo, Zhiyang Teng +4
AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMR) into text. A key challenge in this task is to efficiently learn effective graph represe…
An Unsupervised Sentence Embedding Method by Mutual Information Maximization
Yan Zhang, Ruidan He, Zuozhu Liu +2
BERT is inefficient for sentence-pair tasks such as clustering or semantic search as it needs to evaluate combinatorially many sentence pairs which is very time-consuming. Sentence…
Biologically Plausible Sequence Learning with Spiking Neural Networks
Zuozhu Liu, Thiparat Chotibut, Christopher Hillar +1
Motivated by the celebrated discrete-time model of nervous activity outlined by McCulloch and Pitts in 1943, we propose a novel continuous-time model, the McCulloch-Pitts network (…
Scheduling Policies for Federated Learning in Wireless Networks
Howard H. Yang, Zuozhu Liu, Tony Q. S. Quek +1
Motivated by the increasing computational capacity of wireless user equipments (UEs), e.g., smart phones, tablets, or vehicles, as well as the increasing concerns about sharing pri…
Vprop: Variational Inference using RMSprop
Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt +1
Many computationally-efficient methods for Bayesian deep learning rely on continuous optimization algorithms, but the implementation of these methods requires significant changes t…
Variational Probability Flow for Biologically Plausible Training of Deep Neural Networks
Zuozhu Liu, Tony Q. S. Quek, Shaowei Lin
The quest for biologically plausible deep learning is driven, not just by the desire to explain experimentally-observed properties of biological neural networks, but also by the ho…
Variational Adaptive-Newton Method for Explorative Learning
Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt +2
We present the Variational Adaptive Newton (VAN) method which is a black-box optimization method especially suitable for explorative-learning tasks such as active learning and rein…