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Found 33 papers, 7 papers with code
Personalized Federated Instruction Tuning via Neural Architecture Search
Federated Instruction Tuning (FIT) has shown the ability to achieve collaborative model instruction tuning among massive data owners without sharing private data.
MIP: CLIP-based Image Reconstruction from PEFT Gradients
Contrastive Language-Image Pre-training (CLIP) model, as an effective pre-trained multimodal neural network, has been widely used in distributed machine learning tasks, especially Federated Learning (FL).
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
In real-world material research, machine learning (ML) models are usually expected to predict and discover novel exceptional materials that deviate from the known materials.
HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding
We explore constructing the class hierarchy into a graph, with its nodes representing the textual or image features of each category.
Fine-Grained Visual Recognition
Graph Representation Learning
AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems
On the one hand, to achieve model training in all the diverse clients, mobile computing systems can only use small low-performance models for collaborative learning.
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems
Although Federated Learning (FL) is promising to enable collaborative learning among Artificial Intelligence of Things (AIoT) devices, it suffers from the problem of low classification performance due to various heterogeneity factors (e. g., computing capacity, memory size) of devices and uncertain operating environments.
Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning
However, due to data heterogeneity and stragglers, SFL suffers from the challenges of low inference accuracy and low efficiency.
Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation
By using these trigger images, our approach eliminates poisoned models to ensure the updated global model is benign.
MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding
Monitoring animal behavior can facilitate conservation efforts by providing key insights into wildlife health, population status, and ecosystem function.
Personalization as a Shortcut for Few-Shot Backdoor Attack against Text-to-Image Diffusion Models
Although recent personalization methods have democratized high-resolution image synthesis by enabling swift concept acquisition with minimal examples and lightweight computation, they also present an exploitable avenue for high accessible backdoor attacks.
FedMR: Federated Learning via Model Recombination
Although Federated Learning (FL) enables global model training across clients without compromising their raw data, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance, especially for unevenly distributed data among clients.
Architecture-agnostic Iterative Black-box Certified Defense against Adversarial Patches
The adversarial patch attack aims to fool image classifiers within a bounded, contiguous region of arbitrary changes, posing a real threat to computer vision systems (e. g., autonomous driving, content moderation, biometric authentication, medical imaging) in the physical world.
LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition
Long-tailed multi-label visual recognition (LTML) task is a highly challenging task due to the label co-occurrence and imbalanced data distribution.
Ranked #1 on
Long-tail Learning
on COCO-MLT
(using extra training data)
Towards Interpretable Federated Learning
Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data.
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning
Based on the concept of Continual Learning (CL), we prove that CyclicFL approximates existing centralized pre-training methods in terms of classification and prediction performance.
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation
Federated learning (FL) has been recognized as a privacy-preserving distributed machine learning paradigm that enables knowledge sharing among various heterogeneous artificial intelligence (AIoT) devices through centralized global model aggregation.
GitFL: Adaptive Asynchronous Federated Learning using Version Control
Unlike traditional FL, the cloud server of GitFL maintains a master model (i. e., the global model) together with a set of branch models indicating the trained local models committed by selected devices, where the master model is updated based on both all the pushed branch models and their version information, and only the branch models after the pull operation are dispatched to devices.
Algorithmic Decision-Making Safeguarded by Human Knowledge
In view of such a conflict, we provide a general analytical framework to study the augmentation of algorithmic decisions with human knowledge: the analyst uses the knowledge to set a guardrail by which the algorithmic decision is clipped if the algorithmic output is out of bound, and seems unreasonable.
FedCross: Towards Accurate Federated Learning via Multi-Model Cross Aggregation
Due to the remarkable performance in preserving data privacy for decentralized data scenarios, Federated Learning (FL) has been considered as a promising distributed machine learning paradigm to deal with data silos problems.
FedMR: Fedreated Learning via Model Recombination
Worse still, in each round of FL training, FedAvg dispatches the same initial local models to clients, which can easily result in stuck-at-local-search for optimal global models.
Efficient Self-supervised Vision Pretraining with Local Masked Reconstruction
After finetuning the pretrained LoMaR on 384$\times$384 images, it can reach 85. 4% top-1 accuracy, surpassing MAE by 0. 6%.
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment
Along with the popularity of Artificial Intelligence (AI) and Internet-of-Things (IoT), Federated Learning (FL) has attracted steadily increasing attentions as a promising distributed machine learning paradigm, which enables the training of a central model on for numerous decentralized devices without exposing their privacy.
Model-Contrastive Learning for Backdoor Defense
Due to the popularity of Artificial Intelligence (AI) techniques, we are witnessing an increasing number of backdoor injection attacks that are designed to maliciously threaten Deep Neural Networks (DNNs) causing misclassification.
Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints
Discovering new materials is a challenging task in materials science crucial to the progress of human society.
FedCAT: Towards Accurate Federated Learning via Device Concatenation
As a promising distributed machine learning paradigm, Federated Learning (FL) enables all the involved devices to train a global model collaboratively without exposing their local data privacy.
Predicting Lattice Phonon Vibrational Frequencies Using Deep Graph Neural Networks
Our work demonstrates the capability of deep graph neural networks to learn to predict phonon spectrum properties of crystal structures in addition to phonon density of states (DOS) and electronic DOS in which the output dimension is constant.
Phonon Scattering in the Complex Strain Field of a Dislocation
Strain engineering is critical to the performance enhancement of electronic and thermoelectric devices because of its influence on the material thermal conductivity.
Materials Science
Probing the Phonon Mean Free Paths in Dislocation Core by Molecular Dynamics Simulation
In this study, the 1D McKelvey-Shockley phonon BTE method was extended to model inhomogeneous materials, where the effect of defects on the phonon MFPs is explicitly obtained.
Materials Science Computational Physics 80A05 I.6.0
Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks
Extensive benchmark experiments over 2, 170 Fm-3m face-centered-cubic (FCC) materials show that our ECD based CNNs can achieve good performance for elasticity prediction.
Machine Learning based prediction of noncentrosymmetric crystal materials
Noncentrosymmetric materials play a critical role in many important applications such as laser technology, communication systems, quantum computing, cybersecurity, and etc.
Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials
The percentage of chemically valid (charge neutral and electronegativity balanced) samples out of all generated ones reaches 84. 5% by our GAN when trained with materials from ICSD even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules.
A Bilingual Interactive Human Avatar Dialogue System
This demonstration paper presents a bilingual (Arabic-English) interactive human avatar dialogue system.
DIMM-SC: A Dirichlet mixture model for clustering droplet-based single cell transcriptomic data
More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods.
