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Found 35 papers, 13 papers with code
GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning
Federated Learning (FL) is popular for its privacy-preserving and collaborative learning capabilities.
Eliminating Domain Bias for Federated Learning in Representation Space
Recently, federated learning (FL) is popular for its privacy-preserving and collaborative learning abilities.
PFLlib: Personalized Federated Learning Algorithm Library
Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm that allows collaborative learning with data privacy protection, personalized FL (pFL) has gained significant prominence as a research direction within the FL domain.
RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning
Most of the existing methods rely on a multiple instance learning framework that requires densely sampling local patches at high magnification.
Masked Distillation with Receptive Tokens
To obtain a group of masks, the receptive tokens are learned via the regular task loss but with teacher fixed, and we also leverage a Dice loss to enrich the diversity of learned masks.
RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization
We are the first to propose the more constrained but hardware-friendly Power-of-Two quantization scheme for low-bit PTQ specially and prove that it can achieve nearly the same accuracy as SOTA PTQ method.
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
To reduce the high communication cost of transmitting model parameters, a major challenge in HtFL, prototype-based HtFL methods are proposed to solely share class representatives, a. k. a, prototypes, among heterogeneous clients while maintaining the privacy of clients' models.
An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning
Heterogeneous Federated Learning (HtFL) enables collaborative learning on multiple clients with different model architectures while preserving privacy.
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty
Knowledge distillation is an effective paradigm for boosting the performance of pocket-size model, especially when multiple teacher models are available, the student would break the upper limit again.
Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification
In the numerical and experimental examples, the effectiveness of adding auxiliary training data and using the pretrained model on training efficiency and prediction accuracy, as well as the ability to identify unknown parameters with partially observed data, are demonstrated.
Underwater Object Tracker: UOSTrack for Marine Organism Grasping of Underwater Vehicles
The UOHT training paradigm is designed to train the sample-imbalanced underwater tracker so that the tracker is exposed to a great number of underwater domain training samples and learns the feature expressions.
Scalable Gaussian-process regression and variable selection using Vecchia approximations
Gaussian process (GP) regression is a flexible, nonparametric approach to regression that naturally quantifies uncertainty.
A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes
As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run.
Toolpath design for additive manufacturing using deep reinforcement learning
Toolpath optimization of metal-based additive manufacturing processes is currently hampered by the high-dimensionality of its design space.
Layer Pruning via Fusible Residual Convolutional Block for Deep Neural Networks
For layer pruning, we convert convolutional layers of network into ResConv with a layer scaling factor.
An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution
We propose an efficient once-for-all budgeted pruning framework (OFARPruning) to find many compact network structures close to winner tickets in the early training stage considering the effect of input resolution during the pruning process.
FAST: A Fairness Assured Service Recommendation Strategy Considering Service Capacity Constraint
In this paper, we propose a novel metric Top-N Fairness to measure the individual fairness of multi-round recommendations of services with capacity constraints.
Evaluation of the systematic shifts of a ${}^{40}\textrm{Ca}^+-{}^{27}\textrm{Al}^+$ optical clock
Quantum-logic-based ${}^{27}\textrm{Al}^+$ optical clock has been demonstrated in several schemes as there are different choices of the auxiliary ion species.
Atomic Physics
Dynamic Social Media Monitoring for Fast-Evolving Online Discussions
However, collecting social media data using a static set of keywords fails to satisfy the growing need to monitor dynamic conversations and to study fast-changing topics.
A Lossless Intra Reference Block Recompression Scheme for Bandwidth Reduction in HEVC-IBC
The reference frame memory accesses in inter prediction result in high DRAM bandwidth requirement and power consumption.
TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers
In this paper, we consider recommendation scenarios from the perspective of two sides (customers and providers).
Distilling Neuron Spike with High Temperature in Reinforcement Learning Agents
This method uses distillation to effectively avoid the weakness of STBP, which can achieve SOTA performance in classification, and can obtain a smaller, faster convergence and lower power consumption SNN reinforcement learning model.
AdaPruner: Adaptive Channel Pruning and Effective Weights Inheritance
While previous pruning methods have mostly focused on identifying unimportant channels, channel pruning is considered as a special case of neural architecture search in recent years.
A free lunch from ViT:Adaptive Attention Multi-scale Fusion Transformer for Fine-grained Visual Recognition
Learning subtle representation about object parts plays a vital role in fine-grained visual recognition (FGVR) field.
Ranked #10 on
Fine-Grained Image Classification
on Stanford Dogs
Fine-Grained Image Classification
Fine-Grained Visual Recognition
Boosting Pruned Networks with Linear Over-parameterization
To address this challenge, we propose a novel method that first linearly over-parameterizes the compact layers in pruned networks to enlarge the number of fine-tuning parameters and then re-parameterizes them to the original layers after fine-tuning.
Alternating Deep Low-Rank Approach for Exponential Function Reconstruction and Its Biomedical Magnetic Resonance Applications
Undersampling can accelerate the signal acquisition but at the cost of bringing in artifacts.
A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization
Recent theory starts to explain implicit regularization with the model of deep matrix factorization (DMF) and analyze the trajectory of discrete gradient dynamics in the optimization process.
GDOD: Effective Gradient Descent using Orthogonal Decomposition for Multi-Task Learning
GDOD decomposes gradients into task-shared and task-conflict components explicitly and adopts a general update rule for avoiding interference across all task gradients.
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization
To achieve scalable and accurate inference for latent Gaussian processes, we propose a variational approximation based on a family of Gaussian distributions whose covariance matrices have sparse inverse Cholesky (SIC) factors.
Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding
Soft-thresholding has been widely used in neural networks.
Towards Stable Human Pose Estimation via Cross-View Fusion and Foot Stabilization
Then the optimization-based method is introduced to reconstruct the foot pose and foot-ground contact for the general multi-view datasets including AIST++ and Human3. 6M.
Razor SNN: Efficient Spiking Neural Network with Temporal Embeddings
The event streams generated by dynamic vision sensors (DVS) are sparse and non-uniform in the spatial domain, while still dense and redundant in the temporal domain.
Statistical Parameterized Physics-Based Machine Learning Digital Twin Models for Laser Powder Bed Fusion Process
In the context of laser powder bed fusion (LPBF) additive manufacturing, a digital twin of the manufacturing process can offer predictions for the produced parts, diagnostics for manufacturing defects, as well as control capabilities.
Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization
Our work presents a digital twin (DT) framework for real-time predictive control of DED process parameters to meet specific design objectives.
Engineering software 2.0 by interpolating neural networks: unifying training, solving, and calibration
The evolution of artificial intelligence (AI) and neural network theories has revolutionized the way software is programmed, shifting from a hard-coded series of codes to a vast neural network.
