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Found 18 papers, 5 papers with code
Causally Consistent Normalizing Flow
Prior works to achieve causal consistency inevitably compromise the expressiveness of their models by disallowing hidden layers.
Fine-gained air quality inference based on low-quality sensing data using self-supervised learning
Fine-grained air quality (AQ) mapping is made possible by the proliferation of cheap AQ micro-stations (MSs).
Continuous-Time Digital Twin with Analogue Memristive Neural Ordinary Differential Equation Solver
We experimentally validate our approach by developing a digital twin of the HP memristor, which accurately extrapolates its nonlinear dynamics, achieving a 4. 2-fold projected speedup and a 41. 4-fold projected decrease in energy consumption compared to state-of-the-art digital hardware, while maintaining an acceptable error margin.
Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model
Demonstrating equivalent generative quality to the software baseline, our system achieved remarkable enhancements in generative speed for both unconditional and conditional generation tasks, by factors of 64. 8 and 156. 5, respectively.
OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language Models
We evaluate several representative multilingual LLMs on the proposed OMGEval, which we believe will provide a valuable reference for the community to further understand and improve the multilingual capability of LLMs.
CAMBranch: Contrastive Learning with Augmented MILPs for Branching
CAMBranch leverages both MILPs and AMILPs for imitation learning and employs contrastive learning to enhance the model's ability to capture MILP features, thereby improving the quality of branching decisions.
Short-term prediction of construction waste transport activities using AI-Truck
Construction waste hauling trucks (or `slag trucks') are among the most commonly seen heavy-duty diesel vehicles in urban streets, which not only produce significant carbon, NO$_{\textbf{x}}$ and PM$_{\textbf{2. 5}}$ emissions but are also a major source of on-road and on-site fugitive dust.
BEV-Locator: An End-to-end Visual Semantic Localization Network Using Multi-View Images
Then a cross-model transformer associates the BEV features and semantic map queries.
Attention mechanism-based generative adversarial networks for cloud removal in Landsat images
First, attention maps of the input cloudy images are generated to extract the cloud distributions and features through an attentive recurrent network.
A Critical Analysis of Image-based Camera Pose Estimation Techniques
Camera, and associated with its objects within the field of view, localization could benefit many computer vision fields, such as autonomous driving, robot navigation, and augmented reality (AR).
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples
In general, deep learning models often contain many trainable parameters and require a massive number of labeled samples to achieve optimal performance.
Deep Learning-based Time-varying Channel Estimation for RIS Assisted Communication
Reconfigurable intelligent surface (RIS) is considered as a revolutionary technology for future wireless communication networks.
Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution
The main body of the generator contains two blocks; one is the pyramidal convolution in the residual-dense block (PCRDB), and the other is the attention-based upsample (AUP) block.
Heat transport through a superconducting artificial atom
Quantum heat transfer through a generic superconducting set-up consisting of a tunable transmon qubit placed between resonators that are termined by thermal reservoirs is explored.
Quantum Physics
Ordinary Differential Equation-based CNN for Channel Extrapolation over RIS-assisted Communication
The reconfigurable intelligent surface (RIS) is considered as a promising new technology for reconfiguring wireless communication environments.
Information Theory Information Theory
Generalized reliability based on distances
To meet this need, we propose a new distance-based intraclass correlation coefficient (dbICC), defined in terms of arbitrary distances among observations.
Methodology Applications
Distribution-Free Pointwise Adjusted P-Values for Functional Hypotheses
Whereas such envelope tests examine deviation from a functional null distribution in an omnibus sense, in some applications we wish to do more: to obtain p-values at each point in the function domain, adjusted to control the familywise error rate.
Methodology Applications
