Search Results for author:
Found 31 papers, 11 papers with code
NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment
A total of 211 participants have registered in the structure track.
Enforcing Hard Linear Constraints in Deep Learning Models with Decision Rules
The architecture combines a task network trained for prediction accuracy with a safe network trained using decision rules from the stochastic and robust optimization literature to ensure feasibility across the entire input space.
Hardware-Adaptive and Superlinear-Capacity Memristor-based Associative Memory
Here we introduce and experimentally demonstrate on integrated memristor hardware a new hardware-adaptive learning algorithm for associative memories that significantly improves defect tolerance and capacity, and naturally extends to scalable multilayer architectures capable of handling both binary and continuous patterns.
TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting
CFD blocks adopt a bottom-up cascading approach to obtain series representations for each frequency band.
Current Opinions on Memristor-Accelerated Machine Learning Hardware
The unprecedented advancement of artificial intelligence has placed immense demands on computing hardware, but traditional silicon-based semiconductor technologies are approaching their physical and economic limit, prompting the exploration of novel computing paradigms.
OptiChat: Bridging Optimization Models and Practitioners with Large Language Models
Optimization models have been applied to solve a wide variety of decision-making problems.
FaultExplainer: Leveraging Large Language Models for Interpretable Fault Detection and Diagnosis
Machine learning algorithms are increasingly being applied to fault detection and diagnosis (FDD) in chemical processes.
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators
This paper proposes a new data-driven methodology for predicting intervals of post-fault voltage trajectories in power systems.
FeBiM: Efficient and Compact Bayesian Inference Engine Empowered with Ferroelectric In-Memory Computing
As the first FeFET-based in-memory Bayesian inference engine, FeBiM achieves an impressive storage density of 26. 32 Mb/mm$^{2}$ and a computing efficiency of 581. 40 TOPS/W in a representative Bayesian classification task.
Deep-Learning Recognition of Scanning Transmission Electron Microscopy: Quantifying and Mitigating the Influence of Gaussian Noises
The Mask R-CNN model was tested on simulated STEM-HAADF results with different Gaussian noises, particle shapes and particle sizes, and the results indicated that Gaussian noise has determining influence on the accuracy of recognition.
Physics-Informed Neural Networks with Hard Linear Equality Constraints
The incorporation of physics into neural networks can improve generalization and data efficiency.
UniMOS: A Universal Framework For Multi-Organ Segmentation Over Label-Constrained Datasets
However, due to the fact that medical image annotation requires a great deal of manpower and expertise, as well as the fact that clinical departments perform image annotation based on task orientation, there is the problem of having fewer medical image annotation data with more unlabeled data and having many datasets that annotate only a single organ.
FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation
Liver transplantation often faces fairness challenges across subgroups defined by sensitive attributes such as age group, gender, and race/ethnicity.
DiskANN++: Efficient Page-based Search over Isomorphic Mapped Graph Index using Query-sensitivity Entry Vertex
The main drawback of graph-based ANNS is that a graph index would be too large to fit into the memory especially for a large-scale $\mathcal{X}$.
SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features
In comparison to conventional RGB cameras, the superior temporal resolution of event cameras allows them to capture rich information between frames, making them prime candidates for object tracking.
Diagnosing Infeasible Optimization Problems Using Large Language Models
Decision-making problems can be represented as mathematical optimization models, finding wide applications in fields such as economics, engineering and manufacturing, transportation, and health care.
Realizing In-Memory Baseband Processing for Ultra-Fast and Energy-Efficient 6G
To support emerging applications ranging from holographic communications to extended reality, next-generation mobile wireless communication systems require ultra-fast and energy-efficient baseband processors.
Combinatorial-restless-bandit-based Transmitter-Receiver Online Selection for Distributed MIMO Radars With Non-Stationary Channels
We track moving targets with a distributed multiple-input multiple-output (MIMO) radar, for which the transmitters and receivers are appropriately paired and selected with a limited number of radar stations.
A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factors
Specifically, we proposed a deep-learning model to predict multiple risk factors after a liver transplant.
Robust Multitarget Tracking in Interference Environments: A Message-Passing Approach
Then, a unified MP algorithm is used to infer the marginal posterior probability distributions of targets, clutter, and data association by splitting the joint probability distribution into a mean-field approximate part and a belief propagation part.
A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications
Transportation is the backbone of the economy and urban development.
DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding
Compared with the previous opensource OCR toolbox, DavarOCR has relatively more complete support for the sub-tasks of the cutting-edge technology of document understanding.
TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents
Most existing methods divide this task into two subparts: the text reading part for obtaining the plain text from the original document images and the information extraction part for extracting key contents.
Unsupervised Knowledge Adaptation for Passenger Demand Forecasting
These multimodal forecasting models can improve accuracy but be less practical when different parts of multimodal datasets are owned by different institutions who cannot directly share data among them.
Experimentally realized memristive memory augmented neural network
Memory augmented neural network has been proposed to achieve the goal, but the memory module has to be stored in an off-chip memory due to its size.
Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine
Causal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care.
VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations
To address the above limitations, we propose a unified framework VSR for document layout analysis, combining vision, semantics and relations.
Ranked #3 on
Document Layout Analysis
on PubLayNet val
Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks
However if there exists strong atmospheric turbulence or the brightness of guide stars is low, the accuracy of wavefront measurements will be affected.
Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network
Accurate demand forecasting of different public transport modes(e. g., buses and light rails) is essential for public service operation. However, the development level of various modes often varies sig-nificantly, which makes it hard to predict the demand of the modeswith insufficient knowledge and sparse station distribution (i. e., station-sparse mode).
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.
Ranked #2 on
Weather Forecasting
on SD
Long short-term memory networks in memristor crossbars
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence.
Emerging Technologies Applied Physics
