Search Results for author:
Found 24 papers, 6 papers with code
Graph Machine Learning in the Era of Large Language Models (LLMs)
Meanwhile, graphs, especially knowledge graphs, are rich in reliable factual knowledge, which can be utilized to enhance the reasoning capabilities of LLMs and potentially alleviate their limitations such as hallucinations and the lack of explainability.
MEDPNet: Achieving High-Precision Adaptive Registration for Complex Die Castings
Due to their complex spatial structure and diverse geometric features, achieving high-precision and robust point cloud registration for complex Die Castings has been a significant challenge in the die-casting industry.
Image Restoration with Point Spread Function Regularization and Active Learning
Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae.
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science
We propose an instruction-based process for trustworthy data curation in materials science (MatSci-Instruct), which we then apply to finetune a LLaMa-based language model targeted for materials science (HoneyBee).
Learning on Graphs with Out-of-Distribution Nodes
Graph Neural Networks (GNNs) are state-of-the-art models for performing prediction tasks on graphs.
4D Feet: Registering Walking Foot Shapes Using Attention Enhanced Dynamic-Synchronized Graph Convolutional LSTM Network
4D scans of dynamic deformable human body parts help researchers have a better understanding of spatiotemporal features.
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
If yes, we benchmark these existing adapters, which have been shown to be effective in NLP and CV tasks, in item recommendation tasks.
MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling
Our experiments in this low-resource training setting show that language models pretrained on scientific text outperform BERT trained on general text.
Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble
Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis.
Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited
In fact, this question was answered ten years ago when IDRec beats MoRec by a strong margin in both recommendation accuracy and efficiency.
Impact of Event Encoding and Dissimilarity Measures on Traffic Crash Characterization Based on Sequence of Events
Two encoding schemes and five optimal matching based dissimilarity measures were compared by evaluating the sequence clustering results.
Temporal-spatial Representation Learning Transformer for EEG-based Emotion Recognition
Both the temporal dynamics and spatial correlations of Electroencephalogram (EEG), which contain discriminative emotion information, are essential for the emotion recognition.
Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers
We embed prior information of strongly lensed arcs at cluster-scale into the training data through simulation and then train the detection algorithm with simulated images.
A Framework for Multi-stage Bonus Allocation in meal delivery Platform
The semi-black-box acceptance probability model is employed to forecast the relationship between the bonus allocated to order and its acceptance probability, the Lagrangian dual-based dynamic programming algorithm aims to calculate the empirical Lagrangian multiplier for each allocation stage offline based on the historical data set, and the online allocation algorithm uses the results attained in the offline part to calculate a proper delivery bonus for each order.
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations
Then we propose a hierarchical CB (HCB) algorithm to explore users' interest in the hierarchy tree.
Convolutional Networks on Enhanced Message-Passing Graph Improve Semi-Supervised Classification with Few Labels
The key idea is node classification can benefit from various variants of the original graph that are more efficient for message propagation, based upon the assumption that each variant is a potential structure as more nodes are properly labeled.
Interplay between charge order and superconductivity in the kagome metal KV$_3$Sb$_5$
Beyond $p\approx10$ GPa, a second superconducting dome emerges with maximum $T_{\rm c}\approx1. 0$ K at $p_{\rm c2}\approx22$ GPa, which becomes fully suppressed at $p\approx28$ GPa.
Superconductivity
Globally Optimal and Efficient Manhattan Frame Estimation by Delimiting Rotation Search Space
This paper proves that the volume of the space that just contains all MF rotations (called the "MFR space") is only 1 / 24 of that of the whole rotation space, and then an exact MFR space is delimited from the rotation space.
Deep Learning-Based Automated Image Segmentation for Concrete Petrographic Analysis
The standard petrography test method for measuring air voids in concrete (ASTM C457) requires a meticulous and long examination of sample phase composition under a stereomicroscope.
Learning from Sparse Datasets: Predicting Concrete's Strength by Machine Learning
Here, based on the analysis of a large dataset (>10, 000 observations) of measured compressive strengths from industrially-produced concretes, we compare the ability of select ML algorithms to "learn" how to reliably predict concrete strength as a function of the size of the dataset.
Solar-Sail Trajectory Design for Multiple Near Earth Asteroid Exploration Based on Deep Neural Networks
In the preliminary trajectory design of the multi-target rendezvous problem, a model that can quickly estimate the cost of the orbital transfer is essential.
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments
In this paper, we propose real-time monocular plane SLAM to demonstrate that scene understanding could improve both state estimation and dense mapping especially in low-texture environments.
Laplacian regularized low rank subspace clustering
This problem is solved in the low rank subspace clustering model which decomposes the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and gross errors.
Subspace clustering based on low rank representation and weighted nuclear norm minimization
Subspace clustering refers to the problem of segmenting a set of data points approximately drawn from a union of multiple linear subspaces.
