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Found 26 papers, 8 papers with code
Physical formula enhanced multi-task learning for pharmacokinetics prediction
Overall, our work illustrates the benefits and potential of using PEMAL in AIDD and other scenarios with data scarcity and noise.
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical solution to transfer knowledge from a label-rich source graph to a completely unlabelled target graph.
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text
Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted label is accessible.
Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment
The final prediction of the equivalent entity is derived from the LLM's output.
Diffusion-based graph generative methods
Being the most cutting-edge generative methods, diffusion methods have shown great advances in wide generation tasks.
Scientific Large Language Models: A Survey on Biological & Chemical Domains
Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence.
Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation
The second challenge involves accommodating molecule generation to diffusion and accurately predicting the existence of bonds.
Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications
The existing precoding algorithm for downlink MU-LMA relies on a sub-array structured (SAS) transmitter which may suffer from decreased degrees of freedom and complex system configuration.
Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors
Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction.
Debunking Free Fusion Myth: Online Multi-view Anomaly Detection with Disentangled Product-of-Experts Modeling
Multi-view or even multi-modal data is appealing yet challenging for real-world applications.
Graph Neural Architecture Search with GPT-4
In this paper, we integrate GPT-4 into GNAS and propose a new GPT-4 based Graph Neural Architecture Search method (GPT4GNAS for short).
A comparative study of Grid and Natural sentences effects on Normal-to-Lombard conversion
Then We compare natural and grid sentences in terms of Lombard effect and Normal-to-Lombard conversion using LCT and Enhanced MAndarin Lombard Grid corpus (EMALG).
Circle Feature Graphormer: Can Circle Features Stimulate Graph Transformer?
Firstly, we define the first circle feature as modified swing for common graph, which comes from bipartite graph.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks
Then, we propose a novel framework to utilize the rich type semantic information in heterogeneous graphs comprehensively, namely HAGNN (Hybrid Aggregation for Heterogeneous GNNs).
Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling
Recently, ChatGPT, a representative large language model (LLM), has gained considerable attention due to its powerful emergent abilities.
Graph Neural Processes for Spatio-Temporal Extrapolation
We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph.
Boosting Few-Shot Text Classification via Distribution Estimation
Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain.
Few-Shot Image Classification
Few-Shot Text Classification
+1
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach
To overcome these limitations, we propose a novel adaptive mechanism for cross-silo FL, towards incentivizing organizations to contribute data to maximize their long-term payoffs in a real dynamic training environment.
Intelligent Computing: The Latest Advances, Challenges and Future
In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications.
LW-ISP: A Lightweight Model with ISP and Deep Learning
The deep learning (DL)-based methods of low-level tasks have many advantages over the traditional camera in terms of hardware prospects, error accumulation and imaging effects.
Path-aware Siamese Graph Neural Network for Link Prediction
In this paper, we propose a Path-aware Siamese Graph neural network(PSG) for link prediction tasks.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing
Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i. e., 7 types by SenseMag versus 4 types by the existing work in comparisons).
Thompson Sampling for Unimodal Bandits
In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal} bandits, where the expected reward is unimodal over the partially ordered arms.
Sparsity-Aware SSAF Algorithm with Individual Weighting Factors for Acoustic Echo Cancellation
In this paper, we propose and analyze the sparsity-aware sign subband adaptive filtering with individual weighting factors (S-IWF-SSAF) algorithm, and consider its application in acoustic echo cancellation (AEC).
PCNN: Pattern-based Fine-Grained Regular Pruning towards Optimizing CNN Accelerators
Weight pruning is a powerful technique to realize model compression.
Machine Learning-based Signal Detection for PMH Signals in Load-modulated MIMO System
By leveraging machine learning, the proposed detection scheme can accurately obtain information of the channel from a few of the received symbols with little resource cost and achieve comparable detection results as that of the optimal ML detector.
