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Found 30 papers, 12 papers with code
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.
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
Untangling Blockchain: A Data Processing View of Blockchain Systems
Blockchain technologies are gaining massive momentum in the last few years.
Databases Cryptography and Security
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.
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.
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 #30 on
Abstractive Text Summarization
on CNN / Daily Mail
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
Repairing Deep Neural Networks Based on Behavior Imitation
For the fine-tuning repair process, BIRDNN analyzes the behavior differences of neurons on positive and negative samples to identify the most responsible neurons for the erroneous behaviors.
Safety Verification for Neural Networks Based on Set-boundary Analysis
The homeomorphism property exists in some widely used NNs such as invertible NNs.
Verifying Safety of Neural Networks from Topological Perspectives
Moreover, for NNs that do not feature these properties with respect to the input set, we explore subsets of the input set for establishing the local homeomorphism property and then abandon these subsets for reachability computations.
StyleEDL: Style-Guided High-order Attention Network for Image Emotion Distribution Learning
Emotion distribution learning has gained increasing attention with the tendency to express emotions through images.
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.
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.
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.
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.
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 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.
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.
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.
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.
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
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.
Scalable Spectral Clustering with Group Fairness Constraints
In this paper, we present a scalable algorithm for spectral clustering (SC) with group fairness constraints.
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.
An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks
Quantized neural networks (QNNs) have been developed, with binarized neural networks (BNNs) restricted to binary values as a special case.
Rethink Baseline of Integrated Gradients from the Perspective of Shapley Value
Thus, we propose a novel baseline construction method called Shapley Integrated Gradients (SIG) that searches for a set of baselines by proportional sampling to partly simulate the computation path of Shapley Value.
UR4NNV: Neural Network Verification, Under-approximation Reachability Works!
However, these strategies face challenges in addressing the "unknown dilemma" concerning whether the exact output region or the introduced approximation error violates the property in question.
A Framework for Safe Probabilistic Invariance Verification of Stochastic Dynamical Systems
Ensuring safety through set invariance has proven to be a valuable method in various robotics and control applications.
