Search Results for author:
Found 41 papers, 21 papers with code
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm
In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that have long challenged classical computational methods.
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation
In this paper, we introduce a method that incorporates gradient-guided parameter perturbations to the visual encoder of the multimodality model during both pre-training and fine-tuning phases, to improve model generalization for downstream medical VQA tasks.
SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark
The paper introduces SceMQA, a novel benchmark for scientific multimodal question answering at the college entrance level.
Inverse Molecular Design with Multi-Conditional Diffusion Guidance
We extensively validate our model for multi-conditional polymer and small molecule generation.
PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical Imaging
In this paper, we propose a parameter efficient framework for fine-tuning MLLMs, specifically validated on medical visual question answering (Med-VQA) and medical report generation (MRG) tasks, using public benchmark datasets.
Ranked #1 on
Medical Visual Question Answering
on VQA-RAD
(using extra training data)
Medical Report Generation
Medical Visual Question Answering
+4
Large Language Models on Graphs: A Comprehensive Survey
Besides, although LLMs have shown their pure text-based reasoning ability, it is underexplored whether such ability can be generalized to graphs (i. e., graph-based reasoning).
Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection
Finally, we show LLM generated decision tree explanations correlate highly with human ratings of readability, quality, and use of background knowledge while simultaneously providing better understanding of decision boundaries.
Motif-aware Attribute Masking for Molecular Graph Pre-training
Attribute reconstruction is used to predict node or edge features in the pre-training of graph neural networks.
Masked Vision and Language Pre-training with Unimodal and Multimodal Contrastive Losses for Medical Visual Question Answering
Medical visual question answering (VQA) is a challenging task that requires answering clinical questions of a given medical image, by taking consider of both visual and language information.
Ranked #1 on
Medical Visual Question Answering
on PathVQA
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning
More specifically, we decouple a NN model into a personalized feature extractor, obtained by aggregating models from similar clients, and a classifier, which is obtained by local training and used to estimate client similarity.
Semi-Supervised Graph Imbalanced Regression
The training data balance is achieved by (1) pseudo-labeling more graphs for under-represented labels with a novel regression confidence measurement and (2) augmenting graph examples in latent space for remaining rare labels after data balancing with pseudo-labels.
Data-Centric Learning from Unlabeled Graphs with Diffusion Model
A conventional approach is training a model with the unlabeled graphs on self-supervised tasks and then fine-tuning the model on the prediction tasks.
Capturing the motion of every joint: 3D human pose and shape estimation with independent tokens
In this paper we present a novel method to estimate 3D human pose and shape from monocular videos.
Ranked #35 on
3D Human Pose Estimation
on 3DPW
Improving Transformer-based Networks With Locality For Automatic Speaker Verification
The proposed models achieve 0. 75% EER on VoxCeleb 1 test set, outperforming the previously proposed Transformer-based models and CNN-based models, such as ResNet34 and ECAPA-TDNN.
Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling
Unlike traditional distributed machine learning, federated learning stores data locally for training and then aggregates the models on the server, which solves the data security problem that may arise in traditional distributed machine learning.
LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images
In order to make the model lightweight and improve the model accuracy, a Lightweight Network Using Object Attention (LOANet) for Buildings and Roads from UAV Aerial Remote Sensing Images is proposed.
Self-supervised vision-language pretraining for Medical visual question answering
Medical image visual question answering (VQA) is a task to answer clinical questions, given a radiographic image, which is a challenging problem that requires a model to integrate both vision and language information.
Ranked #1 on
Medical Visual Question Answering
on SLAKE-English
Efficient Rigid Body Localization based on Euclidean Distance Matrix Completion for AGV Positioning under Harsh Environment
Mounting multiple tags at different positions of the AGV to collect more TOFs is a feasible solution to tackle this difficulty.
D&D: Learning Human Dynamics from Dynamic Camera
In this work, we present D&D (Learning Human Dynamics from Dynamic Camera), which leverages the laws of physics to reconstruct 3D human motion from the in-the-wild videos with a moving camera.
Robust Vehicle Positioning based on Multi-Epoch and Multi-Antenna TOAs in Harsh Environments
For radio-based time-of-arrival (TOA) positioning systems applied in harsh environments, obstacles in the surroundings and on the vehicle itself will block the signals from the anchors, reduce the number of available TOA measurements and thus degrade the localization performance.
On the Relationship Between Counterfactual Explainer and Recommender
To enable explainability, recent techniques such as ACCENT and FIA are looking for counterfactual explanations that are specific historical actions of a user, the removal of which leads to a change to the recommendation result.
Graph Rationalization with Environment-based Augmentations
Rationale is defined as a subset of input features that best explains or supports the prediction by machine learning models.
Ranked #1 on
Graph Regression
on GlassTemp
Why does Self-Supervised Learning for Speech Recognition Benefit Speaker Recognition?
Recently, self-supervised learning (SSL) has demonstrated strong performance in speaker recognition, even if the pre-training objective is designed for speech recognition.
Graph Data Augmentation for Graph Machine Learning: A Survey
Overall, our work aims to clarify the landscape of existing literature in graph data augmentation and motivates additional work in this area, providing a helpful resource for researchers and practitioners in the broader graph machine learning domain.
Gaussian Mixture Model Based Distributionally Robust Optimal Power Flow With CVaR Constraints
This paper proposes a distributionally robust optimal power flow (OPF) model for transmission grids with wind power generation.
Learning from Counterfactual Links for Link Prediction
However, the causal relationship between the two variables was largely ignored for learning to predict links on a graph.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
Fluctuations in crystalline plasticity
We link this new size effect with other related phenomena like size dependence of strength ("smaller is stronger") and the size induced switch between different hardening mechanisms.
Mesoscale and Nanoscale Physics Materials Science Statistical Mechanics Computational Physics
Dendrite Net with Acceleration Module for Faster Nonlinear Mapping and System Identification
Significance: DD with AC can be used for most engineering systems, such as sensor systems, and will speed up computation in these online systems.
AnimeGAN: A Novel Lightweight GAN for Photo Animation
The existing methods usually have some problems, among which significant problems mainly include: 1) the generated images have no obvious animated style textures; 2) the generated images lose the content of the original images; 3) the parameters of the network require the large memory capacity.
A Relation Spectrum Inheriting Taylor Series: Muscle Synergy and Coupling for Hand
However, this link has yet to be understood due to the complexity of human hand.
Dendrite Net: A White-Box Module for Classification, Regression, and System Identification
The main contribution of this paper is the basic machine learning algorithm (DD) with a white-box attribute, controllable precision for better generalization capability, and lower computational complexity.
Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis
In this work, we address the problem of person-specific gaze model adaptation from only a few reference training samples.
A Differential Approach for Gaze Estimation
Non-invasive gaze estimation methods usually regress gaze directions directly from a single face or eye image.
An Online Attention-based Model for Speech Recognition
In previous work, researchers have shown that such architectures can acquire comparable results to state-of-the-art ASR systems, especially when using a bidirectional encoder and global soft attention (GSA) mechanism.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition
In this study, we propose a new variation of LSTM, advanced LSTM (A-LSTM), for better temporal context modeling.
MetaLDA: a Topic Model that Efficiently Incorporates Meta information
Besides the text content, documents and their associated words usually come with rich sets of meta informa- tion, such as categories of documents and semantic/syntactic features of words, like those encoded in word embeddings.
Texture Characterization by Using Shape Co-occurrence Patterns
In contrast with existing works, the proposed method not only inherits the strong ability to depict geometrical aspects of textures and the high robustness to variations of imaging conditions from the shape-based method, but also provides a flexible way to consider shape relationships and to compute high-order statistics on the tree.
Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints
This paper presents a significant improvement for the synthesis of texture images using convolutional neural networks (CNNs), making use of constraints on the Fourier spectrum of the results.
Meaningful Objects Segmentation from SAR Images via A Multi-Scale Non-Local Active Contour Model
The segmentation of synthetic aperture radar (SAR) images is a longstanding yet challenging task, not only because of the presence of speckle, but also due to the variations of surface backscattering properties in the images.
New explicit thresholding/shrinkage formulas for one class of regularization problems with overlapping group sparsity and their applications
To solve this kind of ill-posed problems, a regularization term (i. e., regularizer) should be introduced, under the assumption that the solutions have some specific properties, such as sparsity and group sparsity.
Total variation with overlapping group sparsity for image deblurring under impulse noise
The total variation (TV) regularization method is an effective method for image deblurring in preserving edges.
