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Found 54 papers, 19 papers with code
Constructing Data Transaction Chains Based on Opportunity Cost Exploration
However, the inherent replicability and privacy concerns of data make it challenging to directly apply traditional trading theories to data markets.
Efficient Pruning of Large Language Model with Adaptive Estimation Fusion
Large language models (LLMs) have become crucial for many generative downstream tasks, leading to an inevitable trend and significant challenge to deploy them efficiently on resource-constrained devices.
From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations
However, existing time series anomaly detection methods fall short in tackling the unique characteristics of astronomical observations where each star is inherently independent but interfered by random concurrent noise, resulting in a high rate of false alarms.
Distributionally Generative Augmentation for Fair Facial Attribute Classification
This work proposes a novel, generation-based two-stage framework to train a fair FAC model on biased data without additional annotation.
Improving Group Connectivity for Generalization of Federated Deep Learning
Bridging the gap between LMC and FL, in this paper, we leverage fixed anchor models to empirically and theoretically study the transitivity property of connectivity from two models (LMC) to a group of models (model fusion in FL).
Generalizable Two-Branch Framework for Image Class-Incremental Learning
Specifically, the main branch can be any existing CL model and the newly introduced side branch is a lightweight convolutional network.
Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models
Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large language models (MLLMs), where improving performance on unseen tasks often leads to a significant performance drop on the original tasks.
Training-time Neuron Alignment through Permutation Subspace for Improving Linear Mode Connectivity and Model Fusion
In deep learning, stochastic gradient descent often yields functionally similar yet widely scattered solutions in the weight space even under the same initialization, causing barriers in the Linear Mode Connectivity (LMC) landscape.
EdgeOL: Efficient in-situ Online Learning on Edge Devices
Emerging applications, such as robot-assisted eldercare and object recognition, generally employ deep learning neural networks (DNNs) and naturally require: i) handling streaming-in inference requests and ii) adapting to possible deployment scenario changes.
Scalable Geometric Fracture Assembly via Co-creation Space among Assemblers
Therefore, there is a need to develop a scalable framework for geometric fracture assembly without relying on semantic information.
Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge
Recent works show that 8-bit or lower weight quantization is feasible with minimal impact on end-to-end task performance, while the activation is still not quantized.
FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning
A typical way of pFL focuses on label distribution skew, and they adopt a decoupling scheme where the model is split into a common feature extractor and two prediction heads (generic and personalized).
Score-PA: Score-based 3D Part Assembly
In this paper, we formulate this task from a novel generative perspective, introducing the Score-based 3D Part Assembly framework (Score-PA) for 3D part assembly.
Using Adamic-Adar Index Algorithm to Predict Volunteer Collaboration: Less is More
Social networks exhibit a complex graph-like structure due to the uncertainty surrounding potential collaborations among participants.
Understanding Prompt Tuning for V-L Models Through the Lens of Neural Collapse
It is found that NC optimality of text-to-image representations shows a positive correlation with downstream generalizability, which is more severe under class imbalance settings.
Generalized Universal Domain Adaptation with Generative Flow Networks
We introduce a new problem in unsupervised domain adaptation, termed as Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target labels including unknown categories.
Data valuation: The partial ordinal Shapley value for machine learning
Data valuation using Shapley value has emerged as a prevalent research domain in machine learning applications.
Universal Domain Adaptation via Compressive Attention Matching
To address this issue, we propose a Universal Attention Matching (UniAM) framework by exploiting the self-attention mechanism in vision transformer to capture the crucial object information.
Ranked #1 on
Universal Domain Adaptation
on Office-Home
Edge-cloud Collaborative Learning with Federated and Centralized Features
Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy.
Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients
It includes a noise-resilient local solver and a robust global aggregator.
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier
Recent advances in neural collapse have shown that the classifiers and feature prototypes under perfect training scenarios collapse into an optimal structure called simplex equiangular tight frame (ETF).
Delving into the Adversarial Robustness of Federated Learning
In this work, we propose a novel algorithm called Decision Boundary based Federated Adversarial Training (DBFAT), which consists of two components (local re-weighting and global regularization) to improve both accuracy and robustness of FL systems.
Revisiting Weighted Aggregation in Federated Learning with Neural Networks
In federated learning (FL), weighted aggregation of local models is conducted to generate a global model, and the aggregation weights are normalized (the sum of weights is 1) and proportional to the local data sizes.
All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management
By re-configuring the model to the corresponding pruning ratio for a specific execution frequency (and voltage), we are able to achieve stable inference speed, i. e., keeping the difference in speed performance under various execution frequencies as small as possible.
Federated Learning with Label Distribution Skew via Logits Calibration
Extensive experiments on federated datasets and real-world datasets demonstrate that FedLC leads to a more accurate global model and much improved performance.
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?
To this end, we propose a sparse state based MARL (S2RL) framework, which utilizes a sparse attention mechanism to discard irrelevant information in local observations.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
+2
Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification
To learn camera-view invariant features for person Re-IDentification (Re-ID), the cross-camera image pairs of each person play an important role.
Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching
In the literature, centralized clustered FL algorithms require the assumption of the number of clusters and hence are not effective enough to explore the latent relationships among clients.
Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning
Adversarial examples are inputs for machine learning models that have been designed by attackers to cause the model to make mistakes.
FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks
This technique mitigates the user heterogeneity problem and better protects user privacy.
Towards Efficient Data Free Black-Box Adversarial Attack
The proposed method can efficiently imitate the target model through a small number of queries and achieve high attack success rate.
DENSE: Data-Free One-Shot Federated Learning
One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the central server to learn a model in a single communication round.
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.
Unified Group Fairness on Federated Learning
We validate the advantages of the FMDA-M algorithm with various kinds of distribution shift settings in experiments, and the results show that FMDA-M algorithm outperforms the existing fair FL algorithms on unified group fairness.
Ensemble Federated Adversarial Training with Non-IID data
Despite federated learning endows distributed clients with a cooperative training mode under the premise of protecting data privacy and security, the clients are still vulnerable when encountering adversarial samples due to the lack of robustness.
Federated Graph Learning -- A Position Paper
Federated learning (FL) is a an emerging technique that can collaboratively train a shared model while keeping the data decentralized, which is a rational solution for distributed GNN training.
Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent
A novel ring FL topology as well as a map-reduce based synchronizing method are designed in the proposed RDFL to improve decentralized FL performance and bandwidth utilization.
Verifying Design through Generative Visualization of Neural Activities
A recurrent neural network is used as the encoder to learn latent representation from electroencephalogram (EEG) signals, recorded while subjects looked at 50 categories of images.
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation
(2) A dynamic weighting strategy named Consensus Focus to identify both the malicious and irrelevant domains.
Knowledge Distillation
Multi-Source Unsupervised Domain Adaptation
+2
Multiple-element joint detection for Aspect-Based Sentiment Analysis
In this paper, we propose a novel end-to-end multiple-element joint detection model (MEJD), which effectively extracts all (target, aspect, sentiment) triples from a sentence.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
GFL: A Decentralized Federated Learning Framework Based On Blockchain
Federated learning(FL) is a rapidly growing field and many centralized and decentralized FL frameworks have been proposed.
Federated Unsupervised Representation Learning
FURL poses two new challenges: (1) data distribution shift (Non-IID distribution) among clients would make local models focus on different categories, leading to the inconsistency of representation spaces.
Federated Mutual Learning
The experiments show that FML can achieve better performance than alternatives in typical FL setting, and clients can be benefited from FML with different models and tasks.
Evaluation Framework For Large-scale Federated Learning
Federated learning is proposed as a machine learning setting to enable distributed edge devices, such as mobile phones, to collaboratively learn a shared prediction model while keeping all the training data on device, which can not only take full advantage of data distributed across millions of nodes to train a good model but also protect data privacy.
Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach
Peer-to-peer knowledge transfer in distributed environments has emerged as a promising method since it could accelerate learning and improve team-wide performance without relying on pre-trained teachers in deep reinforcement learning.
Galaxy Learning -- A Position Paper
Model training, as well as the communication, is achieved with blockchain and its smart contracts.
Deep Sequence Learning with Auxiliary Information for Traffic Prediction
Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved.
Ranked #1 on
Traffic Prediction
on Q-Traffic
Generative Creativity: Adversarial Learning for Bionic Design
Bionic design refers to an approach of generative creativity in which a target object (e. g. a floor lamp) is designed to contain features of biological source objects (e. g. flowers), resulting in creative biologically-inspired design.
Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security
However, current architecture of deep networks suffers the privacy issue that users need to give out their data to the model (typically hosted in a server or a cluster on Cloud) for training or prediction.
Semantic Image Synthesis via Adversarial Learning
In this paper, we propose a way of synthesizing realistic images directly with natural language description, which has many useful applications, e. g. intelligent image manipulation.
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different datasets without utilizing any hand-engineered features.
Ranked #4 on
Sleep Stage Detection
on MASS SS3
Unsupervised Image-to-Image Translation with Generative Adversarial Networks
It's useful to automatically transform an image from its original form to some synthetic form (style, partial contents, etc.
Mixed Neural Network Approach for Temporal Sleep Stage Classification
Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.
Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge
This paper presents the contribution to the third 'CHiME' speech separation and recognition challenge including both front-end signal processing and back-end speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
