Search Results for author:
Found 35 papers, 15 papers with code
Integrated Sensing and Communication for Edge Inference with End-to-End Multi-View Fusion
Integrated sensing and communication (ISAC) is a promising solution to accelerate edge inference via the dual use of wireless signals.
CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)
To overcome these challenges, we have developed a framework based on a hierarchical visual Transformer with a sliding window technique to search for strong lensing systems within entire images.
TablePuppet: A Generic Framework for Relational Federated Learning
In this paper, we formalize this problem as relational federated learning (RFL).
LLM-Enhanced Data Management
Fortunately, large language models (LLMs) have shown high generalizability and human-competitive abilities in understanding context, which are promising for data management tasks (e. g., database diagnosis, database tuning).
Cost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration
However, existing ICL approaches to ER typically necessitate providing a task description and a set of demonstrations for each entity pair and thus have limitations on the monetary cost of interfacing LLMs.
D-Bot: Database Diagnosis System using Large Language Models
Database administrators (DBAs) play an important role in managing, maintaining and optimizing database systems.
CASTER: A Computer-Vision-Assisted Wireless Channel Simulator for Gesture Recognition
In the proposed CASTER simulator, however, the training dataset can be simulated via existing videos.
SkyMath: Technical Report
Large language models (LLMs) have shown great potential to solve varieties of natural language processing (NLP) tasks, including mathematical reasoning.
Integrated Robotics Networks with Co-optimization of Drone Placement and Air-Ground Communications
Terrestrial robots, i. e., unmanned ground vehicles (UGVs), and aerial robots, i. e., unmanned aerial vehicles (UAVs), operate in separate spaces.
LLM As DBA
Database administrators (DBAs) play a crucial role in managing, maintaining and optimizing a database system to ensure data availability, performance, and reliability.
Interleaving Pre-Trained Language Models and Large Language Models for Zero-Shot NL2SQL Generation
PLMs can perform well in schema alignment but struggle to achieve complex reasoning, while LLMs is superior in complex reasoning tasks but cannot achieve precise schema alignment.
RITA: Group Attention is All You Need for Timeseries Analytics
Timeseries analytics is of great importance in many real-world applications.
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs
Our emphasis on database values highlights the new challenges of dirty database contents, external knowledge between NL questions and database contents, and SQL efficiency, particularly in the context of massive databases.
Ranked #1 on
Text-To-SQL
on BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation)
(Execution Accurarcy (Human) metric)
Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration
The widely used practice is to build task-specific or even dataset-specific solutions, which are hard to generalize and disable the opportunities of knowledge sharing that can be learned from different datasets and multiple tasks.
Multi-Point Integrated Sensing and Communication: Fusion Model and Functionality Selection
Integrated sensing and communication (ISAC) represents a paradigm shift, where previously competing wireless transmissions are jointly designed to operate in harmony via the shared use of the hardware platform for improving the spectral and energy efficiencies.
Stochastic Gradient Descent without Full Data Shuffle
In this paper, we first conduct a systematic empirical study on existing data shuffling strategies, which reveals that all existing strategies have room for improvement -- they all suffer in terms of I/O performance or convergence rate.
Domain Adaptation for Deep Entity Resolution: A Design Space Exploration
Entity resolution (ER) is a core problem of data integration.
Ranked #2 on
Entity Resolution
on WDC Watches-small
nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task
NL2VIS - which translates natural language (NL) queries to corresponding visualizations (VIS) - has attracted more and more attention both in commercial visualization vendors and academic researchers.
Recurrent multiple shared layers in Depth for Neural Machine Translation
Compared to the deep Transformer(20-layer encoder, 6-layer decoder), our model has similar model performance and infer speed, but our model parameters are 54. 72% of the former.
Accelerating Edge Intelligence via Integrated Sensing and Communication
Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time.
Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach
Characterizing the sensing and communication performance tradeoff in integrated sensing and communication (ISAC) systems is challenging in the applications of learning-based human motion recognition.
Residual Tree Aggregation of Layers for Neural Machine Translation
In transformer, it only uses the top layer of encoder and decoder in the subsequent process, which makes it impossible to take advantage of the useful information in other layers.
Hierarchical Temperature Imaging Using Pseudo-Inversed Convolutional Neural Network Aided TDLAS Tomography
As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for imaging of two-dimensional temperature distributions in reactive flows.
Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model
Although wireless channel models can be adopted for dataset generation, current channel models are mostly designed for communication rather than sensing.
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation
RPT is pre-trained for a tuple-to-tuple model by corrupting the input tuple and then learning a model to reconstruct the original tuple.
Representation Learning from Limited Educational Data with Crowdsourced Labels
Extensive experiments conducted on three real-world data sets demonstrate the superiority of our framework on learning representations from limited data with crowdsourced labels, comparing with various state-of-the-art baselines.
Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration
We conduct extensive experiments to explore the design space and compare with traditional data synthesis approaches.
Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms
Asking questions is one of the most crucial pedagogical techniques used by teachers in class.
Temporal Network Representation Learning via Historical Neighborhoods Aggregation
More specifically, we first propose a temporal random walk that can identify relevant nodes in historical neighborhoods which have impact on edge formations.
Towards Interpretable and Learnable Risk Analysis for Entity Resolution
Machine-learning-based entity resolution has been widely studied.
An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms
In this paper, we propose a Deep Reinforcement Learning (RL) framework for task arrangement, which is a critical problem for the success of crowdsourcing platforms.
Kingsoft's Neural Machine Translation System for WMT19
This paper describes the Kingsoft AI Lab{'}s submission to the WMT2019 news translation shared task.
VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
Researchers currently rely on ad hoc datasets to train automated visualization tools and evaluate the effectiveness of visualization designs.
Crowd-Powered Data Mining
Many data mining tasks cannot be completely addressed by auto- mated processes, such as sentiment analysis and image classification.
PANDA: Facilitating Usable AI Development
In many complex applications such as healthcare, subject matter experts (e. g. Clinicians) are the ones who appreciate the importance of features that affect health, and their knowledge together with existing knowledge bases are critical to the end results.
