Search Results for author:
Found 53 papers, 27 papers with code
AdaNovo: Adaptive \emph{De Novo} Peptide Sequencing with Conditional Mutual Information
Moreover, AdaNovo excels in identifying amino acids with PTMs and exhibits robustness against data noise.
Advances of Deep Learning in Protein Science: A Comprehensive Survey
Protein representation learning plays a crucial role in understanding the structure and function of proteins, which are essential biomolecules involved in various biological processes.
A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
Despite recent GNN and Graphformer efforts encoding graphs as Euclidean vectors, recovering original graph from the vectors remains a challenge.
Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding
Representing graph data in a low-dimensional space for subsequent tasks is the purpose of attributed graph embedding.
End-to-end Learnable Clustering for Intent Learning in Recommendation
Concretely, we encode users' behavior sequences and initialize the cluster centers (latent intents) as learnable neurons.
Graph-level Protein Representation Learning by Structure Knowledge Refinement
The mainstream method is utilizing contrastive learning to facilitate graph feature extraction, known as Graph Contrastive Learning (GCL).
Masked Modeling for Self-supervised Representation Learning on Vision and Beyond
As the deep learning revolution marches on, self-supervised learning has garnered increasing attention in recent years thanks to its remarkable representation learning ability and the low dependence on labeled data.
MMDesign: Multi-Modality Transfer Learning for Generative Protein Design
Protein design involves generating protein sequences based on their corresponding protein backbones.
AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems
On the one hand, to achieve model training in all the diverse clients, mobile computing systems can only use small low-performance models for collaborative learning.
Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning
However, due to data heterogeneity and stragglers, SFL suffers from the challenges of low inference accuracy and low efficiency.
AcademicGPT: Empowering Academic Research
Our training corpus mainly consists of academic papers, thesis, content from some academic domain, high-quality Chinese data and others.
Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis
While it is possible to obtain annotation locally by directly asking users to provide preferred responses, such annotations have to be sparse to not affect user experience.
WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks
Due to the popularity of Artificial Intelligence (AI) technology, numerous backdoor attacks are designed by adversaries to mislead deep neural network predictions by manipulating training samples and training processes.
Revisiting the Temporal Modeling in Spatio-Temporal Predictive Learning under A Unified View
In this paper, we re-examine the two dominant temporal modeling approaches within the realm of spatio-temporal predictive learning, offering a unified perspective.
Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation
By using these trigger images, our approach eliminates poisoned models to ensure the updated global model is benign.
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).
Reinforcement Graph Clustering with Unknown Cluster Number
To enable the deep graph clustering algorithms to work without the guidance of the predefined cluster number, we propose a new deep graph clustering method termed Reinforcement Graph Clustering (RGC).
Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?
Molecular property prediction (MPP) is a crucial task in the drug discovery pipeline, which has recently gained considerable attention thanks to advances in deep neural networks.
Dink-Net: Neural Clustering on Large Graphs
Subsequently, the clustering distribution is optimized by minimizing the proposed cluster dilation loss and cluster shrink loss in an adversarial manner.
Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designer
In this paper, we propose a \textit{simple yet effective} model that can co-design 1D sequences and 3D structures of CDRs in a one-shot manner.
CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignment
In this work, we propose a novel contrastive visual-textual transformation for SLR, CVT-SLR, to fully explore the pretrained knowledge of both the visual and language modalities.
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation
Federated learning (FL) has been recognized as a privacy-preserving distributed machine learning paradigm that enables knowledge sharing among various heterogeneous artificial intelligence (AIoT) devices through centralized global model aggregation.
Protein Language Models and Structure Prediction: Connection and Progression
The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding.
A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
However, the corresponding survey paper is relatively scarce, and it is imminent to make a summary of this field.
GitFL: Adaptive Asynchronous Federated Learning using Version Control
Unlike traditional FL, the cloud server of GitFL maintains a master model (i. e., the global model) together with a set of branch models indicating the trained local models committed by selected devices, where the master model is updated based on both all the pushed branch models and their version information, and only the branch models after the pull operation are dispatched to devices.
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings
Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e. g., Skip-gram, Word2Vec) still play an important role in low-resource and lightweight settings due to their low computational cost, ease of deployment, and stability.
A Systematic Survey of Chemical Pre-trained Models
Deep learning has achieved remarkable success in learning representations for molecules, which is crucial for various biochemical applications, ranging from property prediction to drug design.
Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification
Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications.
Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning
Spatiotemporal predictive learning aims to generate future frames by learning from historical frames.
Ranked #12 on
Video Prediction
on Moving MNIST
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment
Along with the popularity of Artificial Intelligence (AI) and Internet-of-Things (IoT), Federated Learning (FL) has attracted steadily increasing attentions as a promising distributed machine learning paradigm, which enables the training of a central model on for numerous decentralized devices without exposing their privacy.
Model-Contrastive Learning for Backdoor Defense
Due to the popularity of Artificial Intelligence (AI) techniques, we are witnessing an increasing number of backdoor injection attacks that are designed to maliciously threaten Deep Neural Networks (DNNs) causing misclassification.
Generative De Novo Protein Design with Global Context
Thus, we propose the Global-Context Aware generative de novo protein design method (GCA), consisting of local and global modules.
Eliminating Backdoor Triggers for Deep Neural Networks Using Attention Relation Graph Distillation
Due to the prosperity of Artificial Intelligence (AI) techniques, more and more backdoors are designed by adversaries to attack Deep Neural Networks (DNNs). Although the state-of-the-art method Neural Attention Distillation (NAD) can effectively erase backdoor triggers from DNNs, it still suffers from non-negligible Attack Success Rate (ASR) together with lowered classification ACCuracy (ACC), since NAD focuses on backdoor defense using attention features (i. e., attention maps) of the same order.
BIOS: An Algorithmically Generated Biomedical Knowledge Graph
For decades, these knowledge graphs have been developed via expert curation; however, this method can no longer keep up with today's AI development, and a transition to algorithmically generated BioMedKGs is necessary.
HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis
A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality.
A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications
Pretrained Language Models (PLMs) such as BERT have revolutionized the landscape of Natural Language Processing (NLP).
Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels
We demonstrate the effectiveness of our model with the combination of limited labelled data and sufficient unlabelled data or weakly-labelled data.
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation
Furthermore, we devise adversarial training scheme, dubbed \textbf{AT-SimGRACE}, to enhance the robustness of graph contrastive learning and theoretically explain the reasons.
AI-based Medical e-Diagnosis for Fast and Automatic Ventricular Volume Measurement in the Patients with Normal Pressure Hydrocephalus
Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV.
Swin Transformer for Fast MRI
The IM and OM were 2D convolutional layers and the FEM was composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D convolutional layers.
Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information
The dual discriminator design aims to improve the edge information in MRI reconstruction.
Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images
The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis.
Efficient Federated Learning for AIoT Applications Using Knowledge Distillation
Inspired by Knowledge Distillation (KD) that can increase the model accuracy, our approach adds the soft targets used by KD to the FL model training, which occupies negligible network resources.
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning
Contrastive Learning (CL) has emerged as a dominant technique for unsupervised representation learning which embeds augmented versions of the anchor close to each other (positive samples) and pushes the embeddings of other samples (negatives) apart.
Co-learning: Learning from Noisy Labels with Self-supervision
Noisy labels, resulting from mistakes in manual labeling or webly data collecting for supervised learning, can cause neural networks to overfit the misleading information and degrade the generalization performance.
Explainable AI For COVID-19 CT Classifiers: An Initial Comparison Study
XAI is an AI model that is programmed to explain its goals, logic, and decision making so that the end users can understand.
Decision Making
Explainable Artificial Intelligence (XAI)
+1
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond
Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made.
Towards Robust Graph Neural Networks against Label Noise
A pseudo label is computed from the neighboring labels for each node in the training set using LP; meta learning is utilized to learn a proper aggregation of the original and pseudo label as the final label.
Invertible Manifold Learning for Dimension Reduction
Dimension reduction (DR) aims to learn low-dimensional representations of high-dimensional data with the preservation of essential information.
Deep Clustering and Representation Learning that Preserves Geometric Structures
To overcome the problem that clusteringoriented losses may deteriorate the geometric structure of embeddings in the latent space, an isometric loss is proposed for preserving intra-manifold structure locally and a ranking loss for inter-manifold structure globally.
Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation
Though manifold-based clustering has become a popular research topic, we observe that one important factor has been omitted by these works, namely that the defined clustering loss may corrupt the local and global structure of the latent space.
Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images
An outbreak of a novel coronavirus disease (i. e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world.
Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT
Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19.
