Search Results for author:
Found 24 papers, 7 papers with code
ValCAT: Variable-Length Contextualized Adversarial Transformations Using Encoder-Decoder Language Model
Adversarial texts help explore vulnerabilities in language models, improve model robustness, and explain their working mechanisms.
Learning Service Selection Decision Making Behaviors During Scientific Workflow Development
In this paper, a novel context-aware approach is proposed to recommending next services in a workflow development process, through learning service representation and service selection decision making behaviors from workflow provenance.
Weakly Supervised Semantic Segmentation by Knowledge Graph Inference
Extensive experimentation on both the multi-label classification and segmentation network stages underscores the effectiveness of the proposed graph reasoning approach for advancing WSSS.
Deep3DSketch+: Rapid 3D Modeling from Single Free-hand Sketches
In this work, we introduce a novel end-to-end approach, Deep3DSketch+, which performs 3D modeling using only a single free-hand sketch without inputting multiple sketches or view information.
Achieving Covert Communication With A Probabilistic Jamming Strategy
In addition, we prove that the minimum jamming power should be the same as Alice's covert transmit power, depending on the covertness and average jamming power constraints.
NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition
Neural networks have shown great potential in accelerating the solution of partial differential equations (PDEs).
Goal-Driven Context-Aware Next Service Recommendation for Mashup Composition
As service-oriented architecture becoming one of the most prevalent techniques to rapidly deliver functionalities to customers, increasingly more reusable software components have been published online in forms of web services.
Whole-Body Lesion Segmentation in 18F-FDG PET/CT
There has been growing research interest in using deep learning based method to achieve fully automated segmentation of lesion in Positron emission tomography computed tomography(PET CT) scans for the prognosis of various cancers.
LordNet: Learning to Solve Parametric Partial Differential Equations without Simulated Data
Neural operators, as a powerful approximation to the non-linear operators between infinite-dimensional function spaces, have proved to be promising in accelerating the solution of partial differential equations (PDE).
Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition
As increasingly more software services have been published onto the Internet, it remains a significant challenge to recommend suitable services to facilitate scientific workflow composition.
AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation
As a specific semantic segmentation task, aerial imagery segmentation has been widely employed in high spatial resolution (HSR) remote sensing images understanding.
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting
How to discover the useful implicit relation between entities and effectively utilize the relations for each entity under various circumstances is crucial.
Learning Physics-Informed Neural Networks without Stacked Back-propagation
In this work, we develop a novel approach that can significantly accelerate the training of Physics-Informed Neural Networks.
Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentation
When tested on the wound images drawn from the supplemental data sets, the DS approach increased the mean MCC from 0. 17 to 0. 85.
Culling the herd of moments with penalized empirical likelihood
Models defined by moment conditions are at the center of structural econometric estimation, but economic theory is mostly agnostic about moment selection.
Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations
While adopting complex GNN models with more informative message passing and aggregation mechanisms can obviously benefit heterogeneous vertex representations and cooperative policy learning, it could, on the other hand, increase the training difficulty of MARL and demand more intense and direct reward signals compared to the original global reward.
COSEA: Convolutional Code Search with Layer-wise Attention
However, most existing studies overlook the code's intrinsic structural logic, which indeed contains a wealth of semantic information, and fails to capture intrinsic features of codes.
A model-independent constraint on the Hubble constant with gravitational waves from the Einstein Telescope
In this paper, we investigate the expected constraints on the Hubble constant from the gravitational-wave standard sirens, in a cosmological-model-independent way.
Cosmology and Nongalactic Astrophysics
Light Multi-segment Activation for Model Compression
Inspired by the nature of the expressiveness ability in Neural Networks, we propose to use multi-segment activation, which can significantly improve the expressiveness ability with very little cost, in the compact student model.
TabNN: A Universal Neural Network Solution for Tabular Data
Since there are no shared patterns among these diverse tabular data, it is hard to design specific structures to fit them all.
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network
Traditional solutions on these problems leverage combinatorial optimization with demand and supply forecasting.
Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks
With tens of thousands of electrocardiogram (ECG) records processed by mobile cardiac event recorders every day, heart rhythm classification algorithms are an important tool for the continuous monitoring of patients at risk.
Efficient Delivery Policy to Minimize User Traffic Consumption in Guaranteed Advertising
In the guaranteed delivery scenario, ad exposures (which are also called impressions in some works) to users are guaranteed by contracts signed in advance between advertisers and publishers.
Randomized Mechanisms for Selling Reserved Instances in Cloud
We then extend our mechanism to the general case and achieve a competitive ratio $\frac{1}{42\log k\log T}$ for both social welfare and revenue, where $T$ is the ratio of the maximum request length to the minimum request length and $k$ is the ratio of the maximum request value density to the minimum request value density.
