Search Results for author:
Found 26 papers, 8 papers with code
Nonparametric Forest-Structured Neural Topic Modeling
Neural topic models have been widely used in discovering the latent semantics from a corpus.
YNetr: Dual-Encoder architecture on Plain Scan Liver Tumors (PSLT)
Concurrently, we utilized Dice coefficient as the metric for assessing the segmentation outcomes produced by YNetr, having advantage of capturing different frequency information.
Empowering Federated Learning for Massive Models with NVIDIA FLARE
In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge.
GPT4Battery: An LLM-driven Framework for Adaptive State of Health Estimation of Raw Li-ion Batteries
State of health (SOH) is a crucial indicator for assessing the degradation level of batteries that cannot be measured directly but requires estimation.
Lightweight High-Speed Photography Built on Coded Exposure and Implicit Neural Representation of Videos
In this work, by leveraging classical coded exposure imaging technique and emerging implicit neural representation for videos, we tactfully embed the motion direction cues into the blurry image during the imaging process and develop a novel self-recursive neural network to sequentially retrieve the latent video sequence from the blurry image utilizing the embedded motion direction cues.
Labeled Subgraph Entropy Kernel
In this paper, we propose a novel labeled subgraph entropy graph kernel, which performs well in structural similarity assessment.
DarkVision: A Benchmark for Low-light Image/Video Perception
In this paper, we contribute the first multi-illuminance, multi-camera, and low-light dataset, named DarkVision, serving for both image enhancement and object detection.
PA-GM: Position-Aware Learning of Embedding Networks for Deep Graph Matching
In this paper, we introduce a novel end-to-end neural network that can map the linear assignment problem into a high-dimensional space augmented with node-level relative position information, which is crucial for improving the method's performance for similar content matching.
NVIDIA FLARE: Federated Learning from Simulation to Real-World
Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data.
INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions
In terms of algorithm design, INFWIDE proposes a two-branch architecture, which explicitly removes noise and hallucinates saturated regions in the image space and suppresses ringing artifacts in the feature space, and integrates the two complementary outputs with a subtle multi-scale fusion network for high quality night photograph deblurring.
A Dual Sensor Computational Camera for High Quality Dark Videography
In this paper, we propose to build a dual-sensor camera to additionally collect the photons in NIR wavelength, and make use of the correlation between RGB and near-infrared (NIR) spectrum to perform high-quality reconstruction from noisy dark video pairs.
Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach
The architecture is based on a novel mapping from real-world data to Hilbert space.
Two-level Graph Neural Network
Moreover, we provide a mathematical analysis of the LPI problem which demonstrates that subgraph-level information is beneficial to overcoming the problems associated with LPI.
10-mega pixel snapshot compressive imaging with a hybrid coded aperture
Towards this end, snapshot compressive imaging (SCI) was proposed as a promising solution to improve the throughput of imaging systems by compressive sampling and computational reconstruction.
DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point Clouds
As there is a lack of 3D point clouds datasets related to the fine-grained building facade, we construct the first large-scale building facade point clouds benchmark dataset for semantic segmentation.
Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition
It aims to obtain salient and discriminative features for specific expressions and also predict expression by fusing the expression-specific features.
Micro Expression Recognition
Micro-Expression Recognition
+1
Low-cost and high-performance data augmentation for deep-learning-based skin lesion classification
At the network search stage, the DCNNs are fine-tuned with the full training set in order to select the model with the highest BACC.
Minority Oversampling for Imbalanced Time Series Classification
Many important real-world applications involve time-series data with skewed distribution.
Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis
With the dominant entropy time series for each pair of financial networks to hand, we develop a similarity measure based on the classical dynamic time warping framework, for analyzing the financial time-varying networks.
Quantum-based subgraph convolutional neural networks
This new architecture captures both the global topological structure and the local connectivity structure within a graph.
Ranked #11 on
Graph Classification
on MUTAG
Identifying The Most Informative Features Using A Structurally Interacting Elastic Net
With the feature graphs to hand, we propose a new information theoretic criterion to measure the joint relevance of different pairwise feature combinations with respect to the target feature graph representation.
Deep Multi-Task Learning to Recognise Subtle Facial Expressions of Mental States
To advance subtle expression recognition, we contribute a Large-scale Subtle Emotions and Mental States in the Wild database (LSEMSW).
Attribute-Enhanced Face Recognition With Neural Tensor Fusion Networks
To solve this problem, we establish a theoretical equivalence between tensor optimisation and a two-stream gated neural network.
Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions
We propose a novel investment decision strategy (IDS) based on deep learning.
Computational Finance
A Boosting Method to Face Image Super-resolution
Specifically, in each iteration of the AdaBoost operation, each facial patch is weighted automatically according to the performance of the model on it, so as to highlight those patches that are more critical for improving the reconstruction power in next step.
