Search Results for author:
Found 14 papers, 6 papers with code
Large Model driven Radiology Report Generation with Clinical Quality Reinforcement Learning
Finally, to better reflect the clinical significant and insignificant errors that radiologists would normally assign in the report, we introduce a novel clinical quality reinforcement learning strategy.
VLPrompt: Vision-Language Prompting for Panoptic Scene Graph Generation
Panoptic Scene Graph Generation (PSG) aims at achieving a comprehensive image understanding by simultaneously segmenting objects and predicting relations among objects.
Ranked #1 on
Panoptic Scene Graph Generation
on PSG Dataset
DeepMetricEye: Metric Depth Estimation in Periocular VR Imagery
Despite the enhanced realism and immersion provided by VR headsets, users frequently encounter adverse effects such as digital eye strain (DES), dry eye, and potential long-term visual impairment due to excessive eye stimulation from VR displays and pressure from the mask.
Data-driven localized waves and parameter discovery in the massive Thirring model via extended physics-informed neural networks with interface zones
In this paper, we study data-driven localized wave solutions and parameter discovery in the massive Thirring (MT) model via the deep learning in the framework of physics-informed neural networks (PINNs) algorithm.
HiLo: Exploiting High Low Frequency Relations for Unbiased Panoptic Scene Graph Generation
Existing unbiased methods tackle the long-tail problem by data/loss rebalancing to favor low-frequency relations.
Ranked #2 on
Panoptic Scene Graph Generation
on PSG Dataset
Probably Approximate Shapley Fairness with Applications in Machine Learning
We observe that the fairness guarantees of exact SVs are too restrictive for SV estimates.
Auxiliary Factor Method to Remove ISI of Nyquist Filters
As has been known, the Nyquist first condition promises no intersymbol interference (ISI) as derived in the frequency domain.
Deep neural networks for solving forward and inverse problems of (2+1)-dimensional nonlinear wave equations with rational solitons
In this paper, we investigate the forward problems on the data-driven rational solitons for the (2+1)-dimensional KP-I equation and spin-nonlinear Schr\"odinger (spin-NLS) equation via the deep neural networks leaning.
Data-driven discoveries of Bäcklund transforms and soliton evolution equations via deep neural network learning schemes
We introduce a deep neural network learning scheme to learn the B\"acklund transforms (BTs) of soliton evolution equations and an enhanced deep learning scheme for data-driven soliton equation discovery based on the known BTs, respectively.
Deep learning neural networks for the third-order nonlinear Schrodinger equation: Solitons, breathers, and rogue waves
The third-order nonlinear Schrodinger equation (alias the Hirota equation) is investigated via deep leaning neural networks, which describes the strongly dispersive ion-acoustic wave in plasma and the wave propagation of ultrashort light pulses in optical fibers, as well as broader-banded waves on deep water.
Early-Bird GCNs: Graph-Network Co-Optimization Towards More Efficient GCN Training and Inference via Drawing Early-Bird Lottery Tickets
Experiments on various GCN models and datasets consistently validate our GEB finding and the effectiveness of our GEBT, e. g., our GEBT achieves up to 80. 2% ~ 85. 6% and 84. 6% ~ 87. 5% savings of GCN training and inference costs while offering a comparable or even better accuracy as compared to state-of-the-art methods.
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model
Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchmarks.
Gaussian Vector: An Efficient Solution for Facial Landmark Detection
Significant progress has been made in facial landmark detection with the development of Convolutional Neural Networks.
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning
There has recently been a surge in research in batch Deep Reinforcement Learning (DRL), which aims for learning a high-performing policy from a given dataset without additional interactions with the environment.
