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Found 14 papers, 2 papers with code
A Continued Pretrained LLM Approach for Automatic Medical Note Generation
LLMs are revolutionizing NLP tasks.
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model
With the functional and characteristic similarity analysis, we introduce a novel gradient editing (GE) mechanism and verify its feasibility in generating transferable samples on various models.
Code Ownership in Open-Source AI Software Security
As open-source AI software projects become an integral component in the AI software development, it is critical to develop a novel methods to ensure and measure the security of the open-source projects for developers.
Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads
Being able to identify and track all the pedestrians in the dense crowd scene with computer vision approaches is a typical challenge in this field, also known as the Multiple Object Tracking (MOT) challenge.
FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder
Latest federated learning (FL) methods started to focus on how to use unlabeled data in clients for training due to users' privacy concerns, high labeling costs, or lack of expertise.
FedIL: Federated Incremental Learning from Decentralized Unlabeled Data with Convergence Analysis
Most existing federated learning methods assume that clients have fully labeled data to train on, while in reality, it is hard for the clients to get task-specific labels due to users' privacy concerns, high labeling costs, or lack of expertise.
Random Padding Data Augmentation
Random Padding is parameter-free, simple to construct, and compatible with the majority of CNN-based recognition models.
Enhancing Quantum Adversarial Robustness by Randomized Encodings
To address this issue, we propose a general scheme to protect quantum learning systems from adversarial attacks by randomly encoding the legitimate data samples through unitary or quantum error correction encoders.
Hierarchical Federated Learning with Momentum Acceleration in Multi-Tier Networks
In this paper, we propose Hierarchical Federated Learning with Momentum Acceleration (HierMo), a three-tier worker-edge-cloud federated learning algorithm that applies momentum for training acceleration.
Understanding Partial Multi-Label Learning via Mutual Information
To deal with ambiguities in partial multilabel learning (PML), state-of-the-art methods perform disambiguation by identifying ground-truth labels directly.
Fast Multi-label Learning
The goal of this paper is to provide a simple method, yet with provable guarantees, which can achieve competitive performance without a complex training process.
A Novel Graph-based Computation Offloading Strategy for Workflow Applications in Mobile Edge Computing
To achieve better Quality of Service (QoS), for instance, faster response time and lower energy consumption, computation offloading is widely used in the MEC environment.
Edge-computing
Distributed, Parallel, and Cluster Computing
C.2.4
Federated Learning with Nesterov Accelerated Gradient
It is well-known that Nesterov Accelerated Gradient (NAG) is a more advantageous form of momentum, but it is not clear how to quantify the benefits of NAG in FL so far.
Online Metric Learning for Multi-Label Classification
Specifically, in order to learn the new $k$NN-based metric, we first project instances in the training dataset into the label space, which make it possible for the comparisons of instances and labels in the same dimension.
