Search Results for author:
Found 42 papers, 11 papers with code
Multi-Sensor Fusion of Active and Passive Measurements for Extended Object Tracking
This paper addresses the challenge of achieving robust and reliable positioning of a radio device carried by an agent, in scenarios where direct line-of-sight (LOS) radio links are obstructed by the agent.
Pseudo-Measurement Enhancement in Power Distribution Systems
Then, a completion model is established based on the characteristics of measurement data and the low rank of the completion tensor, and the alternating direction method of multipliers (ADMM) is used to solve it iteratively.
Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling
Cross-domain recommendation (CDR) is a task that aims to improve the recommendation performance in a target domain by leveraging the information from source domains.
LIBER: Lifelong User Behavior Modeling Based on Large Language Models
On the one hand, it presents difficulties for LLMs in effectively capturing the dynamic shifts in user interests within these sequences, and on the other hand, there exists the issue of substantial computational overhead if the LLMs necessitate recurrent calls upon each update to the user sequences.
SimpleBEV: Improved LiDAR-Camera Fusion Architecture for 3D Object Detection
More and more research works fuse the LiDAR and camera information to improve the 3D object detection of the autonomous driving system.
Efficient and Deployable Knowledge Infusion for Open-World Recommendations via Large Language Models
Recommender systems (RSs) play a pervasive role in today's online services, yet their closed-loop nature constrains their access to open-world knowledge.
SimpleLLM4AD: An End-to-End Vision-Language Model with Graph Visual Question Answering for Autonomous Driving
By reasoning each VQA pair in the GVQA through VLM stage by stage, our method could achieve e2e driving with language.
ScenEval: A Benchmark for Scenario-Based Evaluation of Code Generation
The methodology proposed in this paper is to construct a benchmark and attach metadata to each test case.
Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review
With the rapid development of Large Language Models (LLMs), a large number of machine learning models have been developed to assist programming tasks including the generation of program code from natural language input.
A Practice-Friendly LLM-Enhanced Paradigm with Preference Parsing for Sequential Recommendation
Specifically, in the information reconstruction stage, we design a new user-level SFT task for collaborative information injection with the assistance of a pre-trained SRS model, which is more efficient and compatible with limited text information.
Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty
In summary, our contributions underscore the importance of stable model training frameworks and the efficacy of collaborative-enhanced models in predicting user spending behavior in mobile gaming.
Fusion of Active and Passive Measurements for Robust and Scalable Positioning
Then, a joint tracking algorithm that utilizes both active and passive measurements is developed for the extended object.
Confidence-Aware Multi-Field Model Calibration
Accurately predicting the probabilities of user feedback, such as clicks and conversions, is critical for advertisement ranking and bidding.
User Centric Evaluation of Code Generation Tools
The paper also reports a case study with the method in the evaluation of ChatGPT's usability as a code generation tool for the R programming language.
Efficient LLM inference solution on Intel GPU
A customized Scaled-Dot-Product-Attention kernel is designed to match our fusion policy based on the segment KV cache solution.
CoCoFormer: A controllable feature-rich polyphonic music generation method
This paper explores the modeling method of polyphonic music sequence.
Robust Long-Tailed Learning via Label-Aware Bounded CVaR
Data in the real-world classification problems are always imbalanced or long-tailed, wherein the majority classes have the most of the samples that dominate the model training.
Choir Transformer: Generating Polyphonic Music with Relative Attention on Transformer
We also proposed a music representation suitable for polyphonic music generation.
A Scenario-Based Functional Testing Approach to Improving DNN Performance
This paper proposes a scenario-based functional testing approach for enhancing the performance of machine learning (ML) applications.
Differential Privacy May Have a Potential Optimization Effect on Some Swarm Intelligence Algorithms besides Privacy-preserving
For this reason, this paper attempts to combine DP and SI for the first time, and proposes a general differentially private swarm intelligence algorithm framework (DPSIAF).
Contrastive Multi-view Framework for Customer Lifetime Value Prediction
Accurate customer lifetime value (LTV) prediction can help service providers optimize their marketing policies in customer-centric applications.
Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
In this work, we propose an Open-World Knowledge Augmented Recommendation Framework with Large Language Models, dubbed KAR, to acquire two types of external knowledge from LLMs -- the reasoning knowledge on user preferences and the factual knowledge on items.
Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors
In this paper, we empirically show that sample-wise AT has limited improvement on OOD performance.
OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
To this end, we propose an optimal embedding table learning framework OptEmbed, which provides a practical and general method to find an optimal embedding table for various base CTR models.
Ranked #3 on
Click-Through Rate Prediction
on KDD12
DIWIFT: Discovering Instance-wise Influential Features for Tabular Data
Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce.
Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms
Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms receive increasing attention.
CoDo: Contrastive Learning with Downstream Background Invariance for Detection
To bridge the performance gap, we propose a novel object-level self-supervised learning method, called Contrastive learning with Downstream background invariance (CoDo).
AI-Lancet: Locating Error-inducing Neurons to Optimize Neural Networks
With the knowledge of error-inducing neurons, we propose two methods to fix the errors: the neuron-flip and the neuron-fine-tuning.
Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning
With this method, Yuan 1. 0, the current largest singleton language model with 245B parameters, achieves excellent performance on thousands GPUs during training, and the state-of-the-art results on NLP tasks.
Discovering Boundary Values of Feature-based Machine Learning Classifiers through Exploratory Datamorphic Testing
This paper proposes a set of testing strategies for testing machine learning applications in the framework of the datamorphism testing methodology.
Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation
It is still an open question to leverage various types of information under the BERT framework.
Deep Layout of Custom-size Furniture through Multiple-domain Learning
In this paper, we propose a multiple-domain model for producing a custom-size furniture layout in the interior scene.
End-to-end Generative Floor-plan and Layout with Attributes and Relation Graph
We conduct our experiments on the proposed real-world interior layout dataset that contains $191208$ designs from the professional designers.
Structural Plan of Indoor Scenes with Personalized Preferences
In this paper, we propose an assistive model that supports professional interior designers to produce industrial interior decoration solutions and to meet the personalized preferences of the property owners.
Adversarial Model for Rotated Indoor Scenes Planning
In this paper, we propose an adversarial model for producing furniture layout for interior scene synthesis when the interior room is rotated.
Hyper-Parameter Optimization: A Review of Algorithms and Applications
This study next reviews major services and toolkits for HPO, comparing their support for state-of-the-art searching algorithms, feasibility with major deep learning frameworks, and extensibility for new modules designed by users.
Morphy: A Datamorphic Software Test Automation Tool
This paper focuses on the datamorphism combination strategies by giving their definitions and implementation algorithms.
Datamorphic Testing: A Methodology for Testing AI Applications
With the rapid growth of the applications of machine learning (ML) and other artificial intelligence (AI) techniques, adequate testing has become a necessity to ensure their quality.
Less Is Better: Unweighted Data Subsampling via Influence Function
In the time of Big Data, training complex models on large-scale data sets is challenging, making it appealing to reduce data volume for saving computation resources by subsampling.
Anion charge-lattice volume dependent Li ion migration in compounds with the face-centered cubic anion frameworks
In this work, the face-centered cubic (fcc) anion frameworks were creatively constructed to study the effects of anion charge and lattice volume on the stability of lithium ion occupation and lithium ion migration.
Applied Physics
Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition
In this paper, we propose a three-stream convolutional neural network (3SCNN) for action recognition from skeleton sequences, which aims to thoroughly and fully exploit the skeleton data by extracting, learning, fusing and inferring multiple motion-related features, including 3D joint positions and joint displacements across adjacent frames as well as oriented bone segments.
Ranked #68 on
Skeleton Based Action Recognition
on NTU RGB+D
Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors
In this paper, we presented systematic solutions to build robust and practical AEs against real world object detectors.
