Search Results for author:
Found 33 papers, 8 papers with code
Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology
A stable, reliable, and controllable orbit lock system is crucial to an electron (or ion) accelerator because the beam orbit and beam energy instability strongly affect the quality of the beam delivered to experimental halls.
A Survey on Large Language Model based Autonomous Agents
In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective.
User Behavior Simulation with Large Language Model based Agents
Simulating high quality user behavior data has always been a fundamental problem in human-centered applications, where the major difficulty originates from the intricate mechanism of human decision process.
Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration
In this way, however, it is hard to generate realistic fragments with complicated structures.
Estimating Continuous Muscle Fatigue For Multi-Muscle Coordinated Exercise: A Pilot Study
Assessing the progression of muscle fatigue for daily exercises provides vital indicators for precise rehabilitation, personalized training dose, especially under the context of Metaverse.
MogaNet: Multi-order Gated Aggregation Network
Notably, MogaNet hits 80. 0\% and 87. 8\% accuracy with 5. 2M and 181M parameters on ImageNet-1K, outperforming ParC-Net and ConvNeXt-L, while saving 59\% FLOPs and 17M parameters, respectively.
Ranked #1 on
Pose Estimation
on COCO val2017
Student-centric Model of Learning Management System Activity and Academic Performance: from Correlation to Causation
Firstly, most of the current work is course-centered (i. e. models are built from data for a specific course) rather than student-centered; secondly, a vast majority of the models are correlational rather than causal.
FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset
In the coarse module, we generate a base parametric model from large-scale RGB-D images, which is able to predict accurate rough 3D face models in different genders, ages, etc.
Intent Matching based Customer Services Chatbot with Natural Language Understanding
Customer service is the lifeblood of any business.
HelixMO: Sample-Efficient Molecular Optimization in Scene-Sensitive Latent Space
Efficient exploration of the chemical space to search the candidate drugs that satisfy various constraints is a fundamental task of drug discovery.
GenURL: A General Framework for Unsupervised Representation Learning
For example, dimension reduction methods, t-SNE, and UMAP optimize pair-wise data relationships by preserving the global geometric structure, while self-supervised learning, SimCLR, and BYOL focus on mining the local statistics of instances under specific augmentations.
Video Person Re-identification using Attribute-enhanced Features
Video-based person re-identification (Re-ID) which aims to associate people across non-overlapping cameras using surveillance video is a challenging task.
Frequency Domain Convolutional Neural Network: Accelerated CNN for Large Diabetic Retinopathy Image Classification
The FDC and FDP layers are used to build a Frequency Domain Convolutional Neural Network (FDCNN) to accelerate the training of large images for DR classification.
An Automated Knowledge Mining and Document Classification System with Multi-model Transfer Learning
The performance of the proposed system has been evaluated by comparing with two robust baseline methods, BERT and BERT-CNN.
Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques
The intention of this research is to study and design an automated agriculture commodity price prediction system with novel machine learning techniques.
IDEAL: Independent Domain Embedding Augmentation Learning
Our IDEAL with MS loss also achieves the new state-of-the-art performance on three image retrieval benchmarks, \ie, \emph{Cars-196}, \emph{CUB-200}, and \emph{SOP}.
AutoMix: Unveiling the Power of Mixup for Stronger Classifiers
Specifically, AutoMix reformulates the mixup classification into two sub-tasks (i. e., mixed sample generation and mixup classification) with corresponding sub-networks and solves them in a bi-level optimization framework.
Ranked #8 on
Image Classification
on Places205
Reformulating HOI Detection as Adaptive Set Prediction
To attain this, we map a trainable interaction query set to an interaction prediction set with a transformer.
Ranked #29 on
Human-Object Interaction Detection
on HICO-DET
(using extra training data)
Incremental learning with online SVMs on LiDAR sensory data
In this research, incremental learning approach with online support vector machines (SVMs) is presented, which aims to deal with LiDAR sensory data only.
A Review of Machine Learning Techniques for Applied Eye Fundus and Tongue Digital Image Processing with Diabetes Management System
Diabetes is a global epidemic and it is increasing at an alarming rate.
Unsupervised Real Time Prediction of Faults Using the Support Vector Machine
The selected best performing classifiers are combined together with SVM classifier (with SMO training algorithm) by using the stacking ensemble method which is to create an efficient ensemble predictive model that can handle the issue of imbalanced data.
Attribute-aware Identity-hard Triplet Loss for Video-based Person Re-identification
Video-based person re-identification (Re-ID) is an important computer vision task.
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection
The augmented data is used for training the OCC algorithms.
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)
Recently, density-based methods have shown promising results, particularly among which Local Outlier Factor (LOF) is arguably dominating.
Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning
In this paper, we proposeaprobabilistic abductive reasoningapproach that augments an exist-ing rule-based IDS (snort [29]) to detect these evolved attacks by (a)Predicting rule conditions that are likely to occur (based on existingrules) and (b) able to generate new snort rules when provided withseed rule (i. e. a starting rule) to reduce the burden on experts toconstantly update them.
A Temporal Attentive Approach for Video-Based Pedestrian Attribute Recognition
In this paper, we first tackle the problem of pedestrian attribute recognition by video-based approach.
Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks
Machine learning models have been widely used in security applications such as intrusion detection, spam filtering, and virus or malware detection.
Lifelong Learning for Sentiment Classification
This paper proposes a novel lifelong learning (LL) approach to sentiment classification.
