Search Results for author:
Found 24 papers, 8 papers with code
AI Empowered Net-RCA for 6G
6G is envisioned to offer higher data rate, improved reliability, ubiquitous AI services, and support massive scale of connected devices.
Scalable Spectral Clustering with Group Fairness Constraints
In this paper, we present a scalable algorithm for spectral clustering (SC) with group fairness constraints.
Robot Navigation with Reinforcement Learned Path Generation and Fine-Tuned Motion Control
Multiple predictive path points are dynamically generated by a deep Markov model optimized using RL approach for robot to track.
Safety Verification for Neural Networks Based on Set-boundary Analysis
The homeomorphism property exists in some widely used NNs such as invertible NNs.
On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions
Importantly, all the bounds in the theorems presented in this paper are independent of the dimension $n$.
On the Probability that a Rocky Planet's Composition Reflects its Host Star
We find that these two measures are unlikely to be resolvable as statistically different unless the bulk density CMF is at least 40% greater than or 50% less than the CMF as inferred from the host star.
Earth and Planetary Astrophysics Solar and Stellar Astrophysics
Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference
Keyphrase extraction (KE) aims to summarize a set of phrases that accurately express a concept or a topic covered in a given document.
AP-Loss for Accurate One-Stage Object Detection
This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem.
Input Validation for Neural Networks via Runtime Local Robustness Verification
We observe that the robustness radii of correctly classified inputs are much larger than that of misclassified inputs which include adversarial examples, especially those from strong adversarial attacks.
Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Models
For point-wise anomaly detection, our method achieves 90. 7\% AUROC on average and outperforms the baseline by 5. 2\% AUROC.
SEdroid: A Robust Android Malware Detector using Selective Ensemble Learning
To address the challenge, we propose a robust Android malware detection approach based on selective ensemble learning, trying to provide an effective solution not that limited to the quality of datasets.
Complex Question Decomposition for Semantic Parsing
We conduct experiments on COMPLEXWEBQUESTIONS which is a large scale complex question semantic parsing dataset, results show that our model achieves significant improvement compared to state-of-the-art methods.
Ranked #1 on
Semantic Parsing
on complexWebQuestions-V1.0
Private Deep Learning with Teacher Ensembles
Privacy-preserving deep learning is crucial for deploying deep neural network based solutions, especially when the model works on data that contains sensitive information.
Towards Accurate One-Stage Object Detection with AP-Loss
For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks.
Pretraining-Based Natural Language Generation for Text Summarization
For the decoder, there are two stages in our model, in the first stage, we use a Transformer-based decoder to generate a draft output sequence.
Ranked #27 on
Abstractive Text Summarization
on CNN / Daily Mail
Adversarial Attack and Defense on Graph Data: A Survey
Therefore, this review is intended to provide an overall landscape of more than 100 papers on adversarial attack and defense strategies for graph data, and establish a unified formulation encompassing most graph adversarial learning models.
Boosting the Robustness Verification of DNN by Identifying the Achilles's Heel
How to ensure the safety of DNN-based system is a critical problem for the research and application of DNN.
Private Model Compression via Knowledge Distillation
To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA.
Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction
Then, it is possible to utilize unlabeled data that have a potential of failure to further improve the performance of the model.
Deep Learning Towards Mobile Applications
In this paper, we provide an overview of the current challenges and representative achievements about pushing deep learning on mobile devices from three aspects: training with mobile data, efficient inference on mobile devices, and applications of mobile deep learning.
Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud
To benefit from the cloud data center without the privacy risk, we design, evaluate, and implement a cloud-based framework ARDEN which partitions the DNN across mobile devices and cloud data centers.
Untangling Blockchain: A Data Processing View of Blockchain Systems
Blockchain technologies are gaining massive momentum in the last few years.
Databases Cryptography and Security
BLOCKBENCH: A Framework for Analyzing Private Blockchains
However, there is a clear lack of a systematic framework with which different systems can be analyzed and compared against each other.
Databases Cryptography and Security Distributed, Parallel, and Cluster Computing
