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Found 21 papers, 8 papers with code
MedM2G: Unifying Medical Multi-Modal Generation via Cross-Guided Diffusion with Visual Invariant
Medical generative models, acknowledged for their high-quality sample generation ability, have accelerated the fast growth of medical applications.
SeqTrack3D: Exploring Sequence Information for Robust 3D Point Cloud Tracking
Most existing methods perform tracking between two consecutive frames while ignoring the motion patterns of the target over a series of frames, which would cause performance degradation in the scenes with sparse points.
UniDCP: Unifying Multiple Medical Vision-language Tasks via Dynamic Cross-modal Learnable Prompts
Medical vision-language pre-training (Med-VLP) models have recently accelerated the fast-growing medical diagnostics application.
Double-Flow-based Steganography without Embedding for Image-to-Image Hiding
Hence, it provides a way to directly generate stego images from secret images without a cover image.
PrivateLoRA For Efficient Privacy Preserving LLM
To our knowledge, our proposed framework is the first efficient and privacy-preserving LLM solution in the literature.
MultiLoRA: Democratizing LoRA for Better Multi-Task Learning
Further investigation into weight update matrices of MultiLoRA exhibits reduced dependency on top singular vectors and more democratic unitary transform contributions.
An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability
This flexibility enables NN models to work efficiently on data with time-varying variability, a common inherent aspect of data in practice.
Motion-to-Matching: A Mixed Paradigm for 3D Single Object Tracking
3D single object tracking with LiDAR points is an important task in the computer vision field.
MMF-Track: Multi-modal Multi-level Fusion for 3D Single Object Tracking
To overcome the limitations of geometry matching, we propose a Multi-modal Multi-level Fusion Tracker (MMF-Track), which exploits the image texture and geometry characteristic of point clouds to track 3D target.
Mean-variance hybrid portfolio optimization with quantile-based risk measure
This paper addresses the importance of incorporating various risk measures in portfolio management and proposes a dynamic hybrid portfolio optimization model that combines the spectral risk measure and the Value-at-Risk in the mean-variance formulation.
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction
Understanding genetic variation, e. g., through mutations, in organisms is crucial to unravel their effects on the environment and human health.
Controllable Fake Document Infilling for Cyber Deception
Recent works in cyber deception study how to deter malicious intrusion by generating multiple fake versions of a critical document to impose costs on adversaries who need to identify the correct information.
DBE-KT22: A Knowledge Tracing Dataset Based on Online Student Evaluation
This is known as the \emph{Knowledge Tracing} problem in the literature.
Improving Contextual Representation with Gloss Regularized Pre-training
To tackle this issue, we propose an auxiliary gloss regularizer module to BERT pre-training (GR-BERT), to enhance word semantic similarity.
RepBin: Constraint-based Graph Representation Learning for Metagenomic Binning
We solve the binning problem by developing new algorithms for (i) graph representation learning that preserves both homophily relations and heterophily constraints (ii) constraint-based graph clustering method that addresses the problems of skewed cluster size distribution.
SetConv: A New Approach for Learning from Imbalanced Data
For many real-world classification problems, e. g., sentiment classification, most existing machine learning methods are biased towards the majority class when the Imbalance Ratio (IR) is high.
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks
A large number of network embedding methods exist to learn vectorial node representations from general graphs with both homogeneous and heterogeneous node and edge types, including some that can specifically model the distinct properties of bipartite networks.
From Aircraft Tracking Data to Network Delay Model: A Data-Driven Approach Considering En-Route Congestion
In this study, we propose a new flight delay model, Multi-layer Air Traffic Network Delay (MATND) model, to capture the impact of en-route congestion on flight delays over an air traffic network.
Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN
It has achieved great success on many tasks of network analysis such as link prediction and node classification.
Deep Learning on Knowledge Graph for Recommender System: A Survey
Recent advances in research have demonstrated the effectiveness of knowledge graphs (KG) in providing valuable external knowledge to improve recommendation systems (RS).
GUIDEGAN: ATTENTION BASED SPATIAL GUIDANCE FOR IMAGE-TO-IMAGE TRANSLATION
To tackle this problem, we propose a GuideGAN based on attention mechanism.
Generative Adversarial Network
Image-to-Image Translation
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