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Found 32 papers, 7 papers with code
S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search
To this end, based on a given CNN model, we first generate a CNN architecture space in which each architecture is a multi-stage CNN generated from the given model using some predefined transformations.
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
Post-training quantization (PTQ) is a popular method for compressing deep neural networks (DNNs) without modifying their original architecture or training procedures.
Fairness-guided Few-shot Prompting for Large Language Models
Large language models have demonstrated surprising ability to perform in-context learning, i. e., these models can be directly applied to solve numerous downstream tasks by conditioning on a prompt constructed by a few input-output examples.
Federated Nearest Neighbor Machine Translation
To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention.
Post-training Quantization on Diffusion Models
These approaches define a forward diffusion process for transforming data into noise and a backward denoising process for sampling data from noise.
Learning with Noisy Labels over Imbalanced Subpopulations
Then, we refurbish the noisy labels using the estimated clean probabilities and the pseudo-labels from the model's predictions.
Vertical Federated Linear Contextual Bandits
In this paper, we investigate a novel problem of building contextual bandits in the vertical federated setting, i. e., contextual information is vertically distributed over different departments.
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup
Importance reweighting is a normal way to handle the subpopulation shift issue by imposing constant or adaptive sampling weights on each sample in the training dataset.
ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery
The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD).
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization
Recently, there has been a growing surge of interest in enabling machine learning systems to generalize well to Out-of-Distribution (OOD) data.
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup
In this paper, we propose a general framework to solve the above two challenges simultaneously.
Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift
Despite the progress, applying DGL to real-world applications faces a series of reliability threats including adversarial attacks, inherent noise, and distribution shift.
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient.
HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation
Second, the overall design space composed of HW/SW partitioning, hardware optimization, and software optimization is huge.
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
In this paper, we propose SPNN - a Scalable and Privacy-preserving deep Neural Network learning framework, from algorithmic-cryptographic co-perspective.
ASFGNN: Automated Separated-Federated Graph Neural Network
Specifically, to solve the data Non-IID problem, we first propose a separated-federated GNN learning model, which decouples the training of GNN into two parts: the message passing part that is done by clients separately, and the loss computing part that is learnt by clients federally.
S3ML: A Secure Serving System for Machine Learning Inference
We present S3ML, a secure serving system for machine learning inference in this paper.
ENAS4D: Efficient Multi-stage CNN Architecture Search for Dynamic Inference
The inference of a input sample can exit from early stage if the prediction of the stage is confident enough.
A Comprehensive Analysis of Information Leakage in Deep Transfer Learning
To the best of our knowledge, our study is the first to provide a thorough analysis of the information leakage issues in deep transfer learning methods and provide potential solutions to the issue.
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes.
Industrial Scale Privacy Preserving Deep Neural Network
Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction.
A Survey of Adversarial Learning on Graphs
To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically.
Practical Privacy Preserving POI Recommendation
Meanwhile, the public data need to be accessed by all the users are kept by the recommender to reduce the storage costs of users' devices.
Secure Social Recommendation based on Secret Sharing
It is well known that social information, which is rich on social platforms such as Facebook, are useful to recommender systems.
S2DNAS:Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search
Recently, dynamic inference has emerged as a promising way to reduce the computational cost of deep convolutional neural network (CNN).
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics
Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent.
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection.
BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference
Specifically, we use homomorphic encryption to protect a client's raw data and use Bayesian neural networks to protect the DNN weights in a cloud server.
P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification
(2) privacy leakage: the model trained using a conventional method may involuntarily reveal the private information of the patients in the training dataset.
G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification
Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has).
SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution
In this paper, we build a super resolution perceptual generative adversarial network (SRPGAN) framework for SISR tasks.
