179 citations · 292 across the 17 of their papers we have counts for
18 papers
Token-Efficient Multi-Agent Collaboration via System One-Guided Computational Division of Labor
Zihan Zhou, Xinzhe Hu, Hanxu Yang +2
Large language model (LLM)-based multi-agent systems (MAS) have become a promising paradigm for complex information-seeking and reasoning tasks by enabling collaborative problem so…
Beyond Missing Rates: Rethinking Incomplete Multi-View Clustering with Protocol Divergence
Haolu Liu, Xiyue Wang, Xuanting Xie +2
Incomplete multi-view clustering (IMVC) is typically evaluated by retraining separate models under different missing-view configurations. Evaluations indexed only by nominal missin…
The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence
Yuxuan Ye, Jun Han, Ao Hu +7
End-to-end LLM trading agents have moved quickly from research curiosity to a small ecosystem of named systems, including FinCon, FinMem, TradingAgents, FinAgent, QuantAgent, and F…
InfMasking: Unleashing Synergistic Information by Contrastive Multimodal Interactions
Liangjian Wen, Qun Dai, Jianzhuang Liu +7
In multimodal representation learning, synergistic interactions between modalities not only provide complementary information but also create unique outcomes through specific inter…
MVEB: Self-Supervised Learning with Multi-View Entropy Bottleneck
Liangjian Wen, Xiasi Wang, Jianzhuang Liu +1
Self-supervised learning aims to learn representation that can be effectively generalized to downstream tasks. Many self-supervised approaches regard two views of an image as both…
Enhancing Multivariate Time Series Forecasting with Mutual Information-driven Cross-Variable and Temporal Modeling
Shiyi Qi, Liangjian Wen, Yiduo Li +5
Recent advancements have underscored the impact of deep learning techniques on multivariate time series forecasting (MTSF). Generally, these techniques are bifurcated into two cate…
PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations
Shiyi Qi, Zenglin Xu, Yiduo Li +4
Recent advancements in deep learning have led to the development of various models for long-term multivariate time-series forecasting (LMTF), many of which have shown promising res…
Generative Oversampling for Imbalanced Data via Majority-Guided VAE
Qingzhong Ai, Pengyun Wang, Lirong He +3
Learning with imbalanced data is a challenging problem in deep learning. Over-sampling is a widely used technique to re-balance the sampling distribution of training data. However,…
Structure-Preserving Graph Representation Learning
Ruiyi Fang, Liangjian Wen, Zhao Kang +1
Though graph representation learning (GRL) has made significant progress, it is still a challenge to extract and embed the rich topological structure and feature information in an…
Self-Supervision Can Be a Good Few-Shot Learner
Yuning Lu, Liangjian Wen, Jianzhuang Liu +2
Existing few-shot learning (FSL) methods rely on training with a large labeled dataset, which prevents them from leveraging abundant unlabeled data. From an information-theoretic p…
Self-supervised Consensus Representation Learning for Attributed Graph
Changshu Liu, Liangjian Wen, Zhao Kang +2
Attempting to fully exploit the rich information of topological structure and node features for attributed graph, we introduce self-supervised learning mechanism to graph represent…
Boosting Few-Shot Classification with View-Learnable Contrastive Learning
Xu Luo, Yuxuan Chen, Liangjian Wen +2
The goal of few-shot classification is to classify new categories with few labeled examples within each class. Nowadays, the excellent performance in handling few-shot classificati…
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE
Qingzhong Ai, Lirong He, Shiyu Liu +1
Recent studies show that advanced priors play a major role in deep generative models. Exemplar VAE, as a variant of VAE with an exemplar-based prior, has achieved impressive result…
Rectifying the Shortcut Learning of Background for Few-Shot Learning
Xu Luo, Longhui Wei, Liangjian Wen +4
The category gap between training and evaluation has been characterised as one of the main obstacles to the success of Few-Shot Learning (FSL). In this paper, we for the first time…
AFINet: Attentive Feature Integration Networks for Image Classification
Xinglin Pan, Jing Xu, Yu Pan +4
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification. Recent advanced models in CNNs, such as ResNets,…
Mutual Information Gradient Estimation for Representation Learning
Liangjian Wen, Yiji Zhou, Lirong He +2
Mutual Information (MI) plays an important role in representation learning. However, MI is unfortunately intractable in continuous and high-dimensional settings. Recent advances es…
Structured Pruning of Recurrent Neural Networks through Neuron Selection
Liangjian Wen, Xuanyang Zhang, Haoli Bai +1
Recurrent neural networks (RNNs) have recently achieved remarkable successes in a number of applications. However, the huge sizes and computational burden of these models make it d…
Low-rank Kernel Learning for Graph-based Clustering
Zhao Kang, Liangjian Wen, Wenyu Chen +1
Constructing the adjacency graph is fundamental to graph-based clustering. Graph learning in kernel space has shown impressive performance on a number of benchmark data sets. Howev…