Search Results for author:
Found 42 papers, 18 papers with code
Real-Time Decision-Making for Digital Twin in Additive Manufacturing with Model Predictive Control using Time-Series Deep Neural Networks
Using Directed Energy Deposition (DED) additive manufacturing as a case study, we demonstrate the effectiveness of the proposed MPC in achieving melt pool temperature tracking to ensure part quality, while reducing porosity defects by regulating laser power to maintain melt pool depth constraints.
Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey
In Industry 4. 0, the digital twin is one of the emerging technologies, offering simulation abilities to predict, refine, and interpret conditions and operations, where it is crucial to emphasize a heightened concentration on the associated security and privacy risks.
LogLLM: Log-based Anomaly Detection Using Large Language Models
Software systems often record important runtime information in logs to help with troubleshooting.
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey
Building effective machine learning (ML) workflows to address complex tasks is a primary focus of the Automatic ML (AutoML) community and a critical step toward achieving artificial general intelligence (AGI).
Adaptive Guidance for Local Training in Heterogeneous Federated Learning
Model heterogeneity poses a significant challenge in Heterogeneous Federated Learning (HtFL).
OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any Person
Specifically, these models struggle to maintain a balance between control and consistency when generating images for virtual clothing trials.
DABL: Detecting Semantic Anomalies in Business Processes Using Large Language Models
In this paper, we introduce DABL, a novel approach for detecting semantic anomalies in business processes using large language models (LLMs).
Interpolating neural network: A novel unification of machine learning and interpolation theory
Artificial intelligence (AI) has revolutionized software development, shifting from task-specific codes (Software 1. 0) to neural network-based approaches (Software 2. 0).
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 task-specific knowledge sharing among clients with different model architectures while preserving privacy.
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.
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.
PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark
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.
Eliminating Domain Bias for Federated Learning in Representation Space
Recently, federated learning (FL) is popular for its privacy-preserving and collaborative learning abilities.
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.
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.
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.
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.
Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding
Soft-thresholding has been widely used in neural networks.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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 #12 on
Fine-Grained Image Classification
on Stanford Dogs
Fine-Grained Image Classification
Fine-Grained Visual Recognition
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.
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.
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).
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.
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.
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
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.
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.
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.
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.
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.
