Search Results for author:
Found 13 papers, 5 papers with code
Retrospective Learning from Interactions
If an LLM responds in an unexpected way to an instruction, the user is likely to signal it by rephrasing the request, expressing frustration, or pivoting to an alternative task.
Diffusion Guided Language Modeling
For auto-regressive language models, existing guidance methods are prone to decoding errors that cascade during generation and degrade performance.
Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2
We propose a new approach for non-Cartesian magnetic resonance image reconstruction.
Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Study
In this study, we carry out the preliminary and comprehensive case study of utilizing GPT-4V for marine analysis.
Tile Classification Based Viewport Prediction with Multi-modal Fusion Transformer
Viewport prediction is a crucial aspect of tile-based 360 video streaming system.
Quantity-Aware Coarse-to-Fine Correspondence for Image-to-Point Cloud Registration
Image-to-point cloud registration aims to determine the relative camera pose between an RGB image and a reference point cloud, serving as a general solution for locating 3D objects from 2D observations.
Single-photon Image Super-resolution via Self-supervised Learning
Single-Photon Image Super-Resolution (SPISR) aims to recover a high-resolution volumetric photon counting cube from a noisy low-resolution one by computational imaging algorithms.
Deep Domain Adversarial Adaptation for Photon-efficient Imaging
Photon-efficient imaging with the single-photon light detection and ranging (LiDAR) captures the three-dimensional (3D) structure of a scene by only a few detected signal photons per pixel.
Robust photon-efficient imaging using a pixel-wise residual shrinkage network
Single-photon light detection and ranging (LiDAR) has been widely applied to 3D imaging in challenging scenarios.
Residual Tensor Train: A Quantum-inspired Approach for Learning Multiple Multilinear Correlations
We prove that such a rule is much more relaxed than that of TT, which means ResTT can easily address the vanishing and exploding gradient problem that exists in the existing TT models.
Detecting quantum entanglement with unsupervised learning
Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks.
Generating Fundus Fluorescence Angiography Images from Structure Fundus Images Using Generative Adversarial Networks
Fluorescein angiography can provide a map of retinal vascular structure and function, which is commonly used in ophthalmology diagnosis, however, this imaging modality may pose risks of harm to the patients.
Domain adaptation model for retinopathy detection from cross-domain OCT images
A deep neural network (DNN) can assist in retinopathy screening by automatically classifying patients into normal and abnormal categories according to optical coherence tomography (OCT) images.
