Search Results for author:
Found 107 papers, 36 papers with code
ZiNet: Linking Chinese Characters Spanning Three Thousand Years
In addition, powered by the knowledge of radical systems in ZiNet, this paper introduces glyph similarity measurement between ancient Chinese characters, which could capture similar glyph pairs that are potentially related in origins or semantics.
Portrait3D: Text-Guided High-Quality 3D Portrait Generation Using Pyramid Representation and GANs Prior
Existing neural rendering-based text-to-3D-portrait generation methods typically make use of human geometry prior and diffusion models to obtain guidance.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Intelligent Chemical Purification Technique Based on Machine Learning
We present an innovative of artificial intelligence with column chromatography, aiming to resolve inefficiencies and standardize data collection in chemical separation and purification domain.
Efficient Surgical Tool Recognition via HMM-Stabilized Deep Learning
Recognizing various surgical tools, actions and phases from surgery videos is an important problem in computer vision with exciting clinical applications.
HandBooster: Boosting 3D Hand-Mesh Reconstruction by Conditional Synthesis and Sampling of Hand-Object Interactions
Reconstructing 3D hand mesh robustly from a single image is very challenging, due to the lack of diversity in existing real-world datasets.
Solvent-Aware 2D NMR Prediction: Leveraging Multi-Tasking Training and Iterative Self-Training Strategies
Although significant progresses have been made in predicting one-dimensional (1D) NMR, two-dimensional (2D) NMR prediction via ML remains a challenge due to the lack of annotated NMR training datasets.
LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration
Large language models (LLMs) have demonstrated impressive performance in understanding language and executing complex reasoning tasks.
SiMA-Hand: Boosting 3D Hand-Mesh Reconstruction by Single-to-Multi-View Adaptation
Estimating 3D hand mesh from RGB images is a longstanding track, in which occlusion is one of the most challenging problems.
Graph Multi-Similarity Learning for Molecular Property Prediction
Additionally, previous multi-similarity approaches require the specification of positive and negative pairs to attribute distinct predefined weights to different relative similarities, which can introduce potential bias.
TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification
However, these methods rely on unlabeled samples provided by the target domains, which renders the model ineffective when the target domain is agnostic.
Evaluating and Enhancing Large Language Models for Conversational Reasoning on Knowledge Graphs
We further introduce LLM-ARK, a LLM grounded KG reasoning agent designed to deliver precise and adaptable predictions on KG paths.
Deciphering Compatibility Relationships with Textual Descriptions via Extraction and Explanation
This fine-tuning allows the model to generate explanations that convey the compatibility relationships between items.
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection
To address these issues, we utilize the advantages of reinforcement learning in adaptively learning in complex environments and propose a novel method that incorporates Reinforcement neighborhood selection for unsupervised graph ANomaly Detection (RAND).
AS-XAI: Self-supervised Automatic Semantic Interpretation for CNN
Explainable artificial intelligence (XAI) aims to develop transparent explanatory approaches for "black-box" deep learning models.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Extrapolatable Transformer Pre-training for Ultra Long Time-Series Forecasting
This underscores the limitations of the existing transformer-based architectures, particularly their scalability to handle large-scale data and ability to capture long-term temporal dependencies.
Molecular Identification and Peak Assignment: Leveraging Multi-Level Multimodal Alignment on NMR
Nuclear magnetic resonance (NMR) spectroscopy plays an essential role in deciphering molecular structure and dynamic behaviors.
Asymmetric Contrastive Multimodal Learning for Advancing Chemical Understanding
Through practical tasks such as isomer discrimination and uncovering crucial chemical properties for drug discovery, ACML exhibits its capability to revolutionize chemical research and applications, providing a deeper understanding of chemical semantics of different modalities.
Sum-Rate Optimization for RIS-Aided Multiuser Communications with Movable Antenna
Numerical results are presented to verify the effectiveness of the proposed algorithm and the superiority of the proposed MA-based system in terms of sum-rate.
Interpretable AI-Driven Discovery of Terrain-Precipitation Relationships for Enhanced Climate Insights
Through this AI-driven knowledge discovery, we uncover previously undisclosed explicit equations that shed light on the connection between terrain features and precipitation patterns.
pLMFPPred: a novel approach for accurate prediction of functional peptides integrating embedding from pre-trained protein language model and imbalanced learning
Comparative experiments show that pLMFPPred outperforms current methods for predicting functional peptides. The experimental results suggest that the proposed method (pLMFPPred) can provide better performance in terms of Accuracy, Area under the curve - Receiver Operating Characteristics, and F1-Score than existing methods.
Crack-Net: Prediction of Crack Propagation in Composites
Computational solid mechanics has become an indispensable approach in engineering, and numerical investigation of fracture in composites is essential as composites are widely used in structural applications.
Modeling Spatiotemporal Periodicity and Collaborative Signal for Local-Life Service Recommendation
(2) spatiotemporal collaborative signal, which indicates similar users have similar preferences at specific locations and times.
Ano-SuPs: Multi-size anomaly detection for manufactured products by identifying suspected patches
Image-based systems have gained popularity owing to their capacity to provide rich manufacturing status information, low implementation costs and high acquisition rates.
SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation
Each individual slice of the volume signifies the relative distances between points and objects within a latent space.
D-IF: Uncertainty-aware Human Digitization via Implicit Distribution Field
In this paper, we propose replacing the implicit value with an adaptive uncertainty distribution, to differentiate between points based on their distance to the surface.
Toward Zero-shot Character Recognition: A Gold Standard Dataset with Radical-level Annotations
To increase the adaptability of ACCID, we propose a splicing-based synthetic character algorithm to augment the training samples and apply an image denoising method to improve the image quality.
3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses
We then propose 3DPortraitGAN, the first 3D-aware one-quarter headshot portrait generator that learns a canonical 3D avatar distribution from the 360{\deg}PHQ dataset with body pose self-learning.
Long-range Meta-path Search on Large-scale Heterogeneous Graphs
To this end, we investigate the importance of different meta-paths and introduce an automatic framework for utilizing long-range dependency on heterogeneous graphs, denoted as Long-range Meta-path Search through Progressive Sampling (LMSPS).
Ranked #2 on
Node Property Prediction
on ogbn-mag
CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks
Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world.
Worth of knowledge in deep learning
Our model-agnostic framework can be applied to a variety of common network architectures, providing a comprehensive understanding of the role of prior knowledge in deep learning models.
Automatic Counterfactual Augmentation for Robust Text Classification Based on Word-Group Search
Despite large-scale pre-trained language models have achieved striking results for text classificaion, recent work has raised concerns about the challenge of shortcut learning.
Geometric Pooling: maintaining more useful information
However, by analyzing the statistical characteristic of activated units after pooling, we found that a large number of units dropped by sorting pooling are negative-value units that contain useful information and can contribute considerably to the final decision.
SelfTalk: A Self-Supervised Commutative Training Diagram to Comprehend 3D Talking Faces
To enhance the visual accuracy of generated lip movement while reducing the dependence on labeled data, we propose a novel framework SelfTalk, by involving self-supervision in a cross-modals network system to learn 3D talking faces.
The ART of Conversation: Measuring Phonetic Convergence and Deliberate Imitation in L2-Speech with a Siamese RNN
Phonetic convergence describes the automatic and unconscious speech adaptation of two interlocutors in a conversation.
Speaker Diaphragm Excursion Prediction: deep attention and online adaptation
Speaker protection algorithm is to leverage the playback signal properties to prevent over excursion while maintaining maximum loudness, especially for the mobile phone with tiny loudspeakers.
Meta-multigraph Search: Rethinking Meta-structure on Heterogeneous Information Networks
Meta-structures are widely used to define which subset of neighbors to aggregate information in heterogeneous information networks (HINs).
Fast vehicle detection algorithm based on lightweight YOLO7-tiny
The swift and precise detection of vehicles plays a significant role in intelligent transportation systems.
A Registration- and Uncertainty-based Framework for White Matter Tract Segmentation With Only One Annotated Subject
Our method is constructed by proposed registration-based peak augmentation (RPA) and uncertainty-based refining (URe) modules.
EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation
Specifically, we introduce the emotion disentangling encoder (EDE) to disentangle the emotion and content in the speech by cross-reconstructed speech signals with different emotion labels.
Secure Antenna Selection and Beamforming in MIMO Systems
This work proposes a novel joint design for multiuser multiple-input multiple-output wiretap channels.
Representing Interlingual Meaning in Lexical Databases
In today's multilingual lexical databases, the majority of the world's languages are under-represented.
A novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch
In the first stage, a detection network named M_det is proposed to detect as many mitoses as possible.
Semi-Supervised Learning with Pseudo-Negative Labels for Image Classification
We also propose a mechanism to select a few pseudo-negative labels to feed into submodels.
FCC: Feature Clusters Compression for Long-Tailed Visual Recognition
Through representation learning, DNNs can map BFs into dense clusters in feature space, while the features of minority classes often show sparse clusters.
SIRA-PCR: Sim-to-Real Adaptation for 3D Point Cloud Registration
We design SIRA-PCR, a new approach to 3D point cloud registration.
H2ONet: Hand-Occlusion-and-Orientation-Aware Network for Real-Time 3D Hand Mesh Reconstruction
First, we decouple hand mesh reconstruction into two branches, one to exploit finger-level non-occluded information and the other to exploit global hand orientation, with lightweight structures to promote real-time inference.
Ranked #5 on
3D Hand Pose Estimation
on HO-3D
FuRPE: Learning Full-body Reconstruction from Part Experts
These novel elements of FuRPE not only serve to further refine the model but also to reduce potential biases that may arise from inaccuracies in pseudo labels, thereby optimizing the network's training process and enhancing the robustness of the model.
Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks
To address these issues, we propose a novel method called Partial Message Meta Multigraph search (PMMM) to automatically optimize the neural architecture design on HINs.
Hand gesture recognition using 802.11ad mmWave sensor in the mobile device
Range-Doppler information (RDI) is obtained by using pulse Doppler radar for gesture recognition.
Retention Time Prediction for Chromatographic Enantioseparation by Quantile Geometry-enhanced Graph Neural Network
A new research framework is proposed to incorporate machine learning techniques into the field of experimental chemistry to facilitate chromatographic enantioseparation.
Span-based joint entity and relation extraction augmented with sequence tagging mechanism
On the one hand, the core architecture enables our model to learn token-level label information via the sequence tagging mechanism and then uses the information in the span-based joint extraction; on the other hand, it establishes a bi-directional information interaction between NER and RE.
Joint Entity and Relation Extraction
named-entity-recognition
+3
Planning Assembly Sequence with Graph Transformer
In this paper, we present a graph-transformer based framework for the ASP problem which is trained and demonstrated on a self-collected ASP database.
Design of Automatic Driving Safety Level and Positioning Accuracy
Autonomous driving is a hot research topic in the frontier of science and technology.
Semi-Supervised Semantic Segmentation with Cross Teacher Training
In experiments, the cross-teacher module significantly improves the performance of traditional student-teacher approaches, and our framework outperforms stateof-the-art methods on benchmark datasets.
Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in Complex Fire Scenarios
Fire-detection technology is of great importance for successful fire-prevention measures.
A Two-Phase Paradigm for Joint Entity-Relation Extraction
An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task.
Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion
The PIC is also employed to discover unrevealed macroscale governing equations from microscopic simulation data in an actual physical scene.
Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation
In particular: 1) existing methods do not explicitly encode and capture the evolution of short-term preference as sequential methods do; 2) simply using last few interactions is not enough for modeling the changing trend.
Lens Antenna Arrays-Assisted mmWave MU-MIMO Uplink Transmission: Joint Beam Selection and Phase-Only Beamforming Design
This paper considers a lens antenna array-assisted millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) system.
CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising
Degraded images commonly exist in the general sources of character images, leading to unsatisfactory character recognition results.
RCRN: Real-world Character Image Restoration Network via Skeleton Extraction
SENet aims to preserve the structural consistency of the character and normalize complex noise.
RZCR: Zero-shot Character Recognition via Radical-based Reasoning
The long-tail effect is a common issue that limits the performance of deep learning models on real-world datasets.
Underwater Acoustic Communication Channel Modeling using Reservoir Computing
Underwater acoustic (UWA) communications have been widely used but greatly impaired due to the complicated nature of the underwater environment.
Ensemble Multi-Relational Graph Neural Networks
This EMR optimization objective is able to derive an iterative updating rule, which can be formalized as an ensemble message passing (EnMP) layer with multi-relations.
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology
Method: This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions of breast histopathological images.
Semantic interpretation for convolutional neural networks: What makes a cat a cat?
The interpretability of deep neural networks has attracted increasing attention in recent years, and several methods have been created to interpret the "black box" model.
2D Human Pose Estimation: A Survey
Briefly, existing approaches put their efforts in three directions, namely network architecture design, network training refinement, and post processing.
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs
On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC).
Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces
Results suggest that Surface Vision Transformers (SiT) demonstrate consistent improvement over geometric deep learning methods for brain age and fluid intelligence prediction and achieve comparable performance on calcium score classification to standard metrics used in clinical practice.
Fast-Spherical-Projection-Based Point Cloud Clustering Algorithm
To contribute to better roadside LiDAR-based transportation facilities, this paper presents a fast-spherical-projection-based clustering algorithm (FSPC) for real-time LiDAR data processing with higher clustering accuracy and noise handling.
Linear Array Network for Low-light Image Enhancement
However, the convolution operation is based on a local sliding window mechanism, which is difficult to construct the long-range dependencies of the feature maps.
EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation
Each type of EM contains 40 original and 40 GT images, in total 1680 EM images.
Learning Dynamic Preference Structure Embedding From Temporal Networks
Our proposed Dynamic Preference Structure (DPS) framework consists of two stages: structure sampling and graph fusion.
Whole Heart Anatomical Refinement from CCTA using Extrapolation and Parcellation
In addition to the new labels, the median Dice scores were improved for all the initial 6 labels to be above 95% in the 10-label segmentation, e. g. from 91% to 97% for the left atrium body and from 92% to 96% for the right ventricle.
Predicting the Stereoselectivity of Chemical Transformations by Machine Learning
Since late 1960s, there have been numerous successes in the exciting new frontier of asymmetric catalysis.
A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers
In recent years, deep learning has made brilliant achievements in Environmental Microorganism (EM) image classification.
Label prompt for multi-label text classification
In this paper, we propose a Label Mask multi-label text classification model (LM-MTC), which is inspired by the idea of cloze questions of language model.
Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)
Partial differential equations (PDEs) are concise and understandable representations of domain knowledge, which are essential for deepening our understanding of physical processes and predicting future responses.
FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration
Data association is important in the point cloud registration.
Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data
Although deep-learning has been successfully applied in a variety of science and engineering problems owing to its strong high-dimensional nonlinear mapping capability, it is of limited use in scientific knowledge discovery.
Robust discovery of partial differential equations in complex situations
In the framework, a preliminary result of potential terms provided by the deep learning-genetic algorithm is added into the loss function of the PINN as physical constraints to improve the accuracy of derivative calculation.
Topological Regularization for Graph Neural Networks Augmentation
The complexity and non-Euclidean structure of graph data hinder the development of data augmentation methods similar to those in computer vision.
OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration
On the other hand, previous global feature based approaches can utilize the entire point cloud for the registration, however they ignore the negative effect of non-overlapping points when aggregating global features.
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks
EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions.
BE-RAN: Blockchain-enabled Open RAN with Decentralized Identity Management and Privacy-Preserving Communication
In this paper, Blockchain-enabled Radio Access Networks (BE-RAN) is proposed as a novel decentralized RAN architecture to facilitate enhanced security and privacy on identification and authentication.
Cryptography and Security Distributed, Parallel, and Cluster Computing Networking and Internet Architecture
Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data
Our proposed algorithm is also able to discover PDEs with high-order derivatives or heterogeneous parameters accurately with sparse and noisy data.
Multi-Modal Subjective Context Modelling and Recognition
Second, existing models typically assume that context is objective, whereas in most applications context is best viewed from the user's perspective.
GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs
Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and relations to empower downstream tasks.
Revisiting Factorizing Aggregated Posterior in Learning Disentangled Representations
In this paper, we provide a theoretical explanation that low total correlation of sampled representation cannot guarantee low total correlation of the mean representation.
Multistability of Small Reaction Networks
For three typical sets of small reaction networks (networks with two reactions, one irreversible and one reversible reaction, or two reversible-reaction pairs), we completely answer the challenging question: what is the smallest subset of all multistable networks such that any multistable network outside of the subset contains either more species or more reactants than any network in this subset?
TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network
To start, we reformulate tiling as a graph problem by modeling candidate tile locations in the target shape as graph nodes and connectivity between tile locations as edges.
Deep-learning of Parametric Partial Differential Equations from Sparse and Noisy Data
Next, genetic algorithm is utilized to discover the form of PDEs and corresponding coefficients with an incomplete candidate library.
A Large Scale Speech Sentiment Corpus
We present a multimodal corpus for sentiment analysis based on the existing Switchboard-1 Telephone Speech Corpus released by the Linguistic Data Consortium.
Tri-graph Information Propagation for Polypharmacy Side Effect Prediction
The use of drug combinations often leads to polypharmacy side effects (POSE).
Ranked #1 on
Pose Prediction
on SUN-Mem
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
In the proposed framework, a deep neural network that is trained with available data of a physical problem is utilized to generate meta-data and calculate derivatives, and the genetic algorithm is then employed to discover the underlying PDE.
D2D-LSTM based Prediction of the D2D Diffusion Path in Mobile Social Networks
However, current mainstream content popularity prediction methods only use the number of downloads and shares or the distribution of user interests, which do not consider important time and geographic location information in mobile social networks, and all of data is from OSN which is not same as MSN.
RTC-VAE: HARNESSING THE PECULIARITY OF TOTAL CORRELATION IN LEARNING DISENTANGLED REPRESENTATIONS
In the problem of unsupervised learning of disentangled representations, one of the promising methods is to penalize the total correlation of sampled latent vari-ables.
DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data
However, prior to achieving this goal, major challenges remain to be resolved, including learning PDE under noisy data and limited discrete data.
Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning
In this paper, a biologically-inspired adaptive intelligent secondary controller is developed for microgrids to tackle system dynamics uncertainties, faults, and/or disturbances.
Automatic Background Filtering Method for Roadside LiDAR Data
Background filtering is the preprocessing step to obtain the HRMTD of different roadway users from roadside LiDAR data.
A Neural-Network-Based Optimal Control of Ultra-Capacitors with System Uncertainties
Conventional control strategies usually produce large disturbances to buses during charging and discharging (C&D) processes of UCs, which significantly degrades the power quality and system performance, especially under fast C&D modes.
Pixel Chem: A Representation for Predicting Material Properties with Neural Network
In this work we developed a new representation of the chemical information for the machine learning models, with benefits from both the real space (R-space) and energy space (K-space).
Multiple Instance Curriculum Learning for Weakly Supervised Object Detection
When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e. g., finding the face of a cat instead of the full body, due to lacking the supervision on the extent of full objects.
Object Boundary Guided Semantic Segmentation
Semantic segmentation is critical to image content understanding and object localization.
Hybrid Affinity Propagation
We formulate this problem as finding a few image exemplars to represent the image set semantically and visually, and solve it in a hybrid way by exploiting both visual and textual information associated with images.
