Search Results for author:
Found 31 papers, 13 papers with code
Underwater Acoustic Reconfigurable Intelligent Surfaces: from Principle to Practice
This article explores the potential of underwater acoustic reconfigurable intelligent surfaces (UA-RIS) for facilitating long-range and eco-friendly communication in marine environments.
Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions
Recent advancements in deep learning have significantly revolutionized the field of clinical diagnosis and treatment, offering novel approaches to improve diagnostic precision and treatment efficacy across diverse clinical domains, thus driving the pursuit of precision medicine.
Experimental Study of Underwater Acoustic Reconfigurable Intelligent Surfaces with In-Phase and Quadrature Modulation
Furthermore, with a fixed communication distance, positioning the UA-RIS at the transmitter side substantially boosts data rates, with an average increase of 63. 8% and peaks up to 96%.
Bidirectional-Reachable Hierarchical Reinforcement Learning with Mutually Responsive Policies
Hierarchical reinforcement learning (HRL) addresses complex long-horizon tasks by skillfully decomposing them into subgoals.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
SAM-VMNet: Deep Neural Networks For Coronary Angiography Vessel Segmentation
Coronary artery disease (CAD) is one of the most prevalent diseases in the cardiovascular field and one of the major contributors to death worldwide.
OMPO: A Unified Framework for RL under Policy and Dynamics Shifts
Training reinforcement learning policies using environment interaction data collected from varying policies or dynamics presents a fundamental challenge.
Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL
Based on this insight, we present Offline-Boosted Actor-Critic (OBAC), a model-free online RL framework that elegantly identifies the outperforming offline policy through value comparison, and uses it as an adaptive constraint to guarantee stronger policy learning performance.
Joint Edge Optimization Deep Unfolding Network for Accelerated MRI Reconstruction
Magnetic Resonance Imaging (MRI) is a widely used imaging technique, however it has the limitation of long scanning time.
Adaptive Mixed-Scale Feature Fusion Network for Blind AI-Generated Image Quality Assessment
Specifically, inspired by the characteristics of the human visual system and motivated by the observation that "visual quality" and "authenticity" are characterized by both local and global aspects, AMFF-Net scales the image up and down and takes the scaled images and original-sized image as the inputs to obtain multi-scale features.
CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)
To overcome these challenges, we have developed a framework based on a hierarchical visual Transformer with a sliding window technique to search for strong lensing systems within entire images.
ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization
The varying significance of distinct primitive behaviors during the policy learning process has been overlooked by prior model-free RL algorithms.
CBVS: A Large-Scale Chinese Image-Text Benchmark for Real-World Short Video Search Scenarios
Differently, video covers in short video search scenarios are presented as user-originated contents that provide important visual summaries of videos.
Ranked #1 on
Image Retrieval
on CBVS
Quantitative perfusion maps using a novelty spatiotemporal convolutional neural network
The results indicate that the network can accurately estimate perfusion parameters, including cerebral blood volume (CBV), cerebral blood flow (CBF), and time to maximum of the residual function (Tmax).
Efficient LLM Inference on CPUs
Large language models (LLMs) have demonstrated remarkable performance and tremendous potential across a wide range of tasks.
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
To quantify this inactivity, we adopt dormant ratio as a metric to measure inactivity in the RL agent's network.
Enhancing In-Situ Structural Health Monitoring through RF Energy-Powered Sensor Nodes and Mobile Platform
The experimental results indicate that an active RF-SN embedded in concrete at a depth of 13. 5 cm can be effectively powered by a 915MHz mobile radio transmitter with an effective isotropic radiated power (EIRP) of 32. 5dBm.
An Efficient Sparse Inference Software Accelerator for Transformer-based Language Models on CPUs
We apply our sparse accelerator on widely-used Transformer-based language models including Bert-Mini, DistilBERT, Bert-Base, and BERT-Large.
Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic
We find that such a long-neglected phenomenon is often related to the use of inferior actions from the current policy in Bellman updates as compared to the more optimal action samples in the replay buffer.
Perception and Semantic Aware Regularization for Sequential Confidence Calibration
In this work, we find tokens/sequences with high perception and semantic correlations with the target ones contain more correlated and effective information and thus facilitate more effective regularization.
Context-Aware Selective Label Smoothing for Calibrating Sequence Recognition Model
Despite the success of deep neural network (DNN) on sequential data (i. e., scene text and speech) recognition, it suffers from the over-confidence problem mainly due to overfitting in training with the cross-entropy loss, which may make the decision-making less reliable.
When to Update Your Model: Constrained Model-based Reinforcement Learning
Our follow-up derived bounds reveal the relationship between model shifts and performance improvement.
Model-based Reinforcement Learning
reinforcement-learning
+2
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing
On the other hand, we design a constrained contrastive strategy on the hierarchical structure to directly model the type differences, which can simultaneously perceive the distinguishability between types at different granularity.
ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal Understanding
However, in most of the public visual question answering datasets such as VQA, CLEVR, the questions are human generated that specific to the given image, such as `What color are her eyes?'.
Spatio-Temporal Federated Learning for Massive Wireless Edge Networks
The STFL model not only represents the realistic intermittent learning behavior from the edge server to the mobile devices due to data delivery outage, but also features a mechanism of compensating loss learning updates in order to mitigate the impacts of intermittent learning.
Adaptive Deconvolution-based stereo matching Net for Local Stereo Matching
In deep learning-based local stereo matching methods, larger image patches usually bring better stereo matching accuracy.
Surface Dyakonov-Cherenkov Radiation
Recent advances in engineered material technologies (e. g., photonic crystals, metamaterials, plasmonics, etc) provide valuable tools to control Cherenkov radiation.
Optics
Quantum Data Fitting Algorithm for Non-sparse Matrices
We propose a quantum data fitting algorithm for non-sparse matrices, which is based on the Quantum Singular Value Estimation (QSVE) subroutine and a novel efficient method for recovering the signs of eigenvalues.
Modeling disease progression in longitudinal EHR data using continuous-time hidden Markov models
Modeling disease progression in healthcare administrative databases is complicated by the fact that patients are observed only at irregular intervals when they seek healthcare services.
ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild
A system to model the emotional expressions based on bodily movements, named ARBEE (Automated Recognition of Bodily Expression of Emotion), has also been developed and evaluated.
Removing Rain From a Single Image via Discriminative Sparse Coding
The paper aims at developing an effective algorithm to remove visual effects of rain from a single rainy image, i. e. separate the rain layer and the de-rained image layer from an rainy image.
Lacunarity Analysis on Image Patterns for Texture Classification
Based on the concept of lacunarity in fractal geometry, we developed a statistical approach to texture description, which yields highly discriminative feature with strong robustness to a wide range of transformations, including photometric changes and geometric changes.
