Search Results for author:
Found 38 papers, 15 papers with code
EXAONEPath 1.0 Patch-level Foundation Model for Pathology
To address this issue, we introduce EXAONEPath, a novel foundational model trained on patches that have undergone stain normalization.
Applying Fine-Tuned LLMs for Reducing Data Needs in Load Profile Analysis
This paper presents a novel method for utilizing fine-tuned Large Language Models (LLMs) to minimize data requirements in load profile analysis, demonstrated through the restoration of missing data in power system load profiles.
An Element-Wise Weights Aggregation Method for Federated Learning
A central challenge in FL is the effective aggregation of local model weights from disparate and potentially unbalanced participating clients.
A Novel Vision Transformer based Load Profile Analysis using Load Images as Inputs
This paper introduces ViT4LPA, an innovative Vision Transformer (ViT) based approach for Load Profile Analysis (LPA).
CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT
Foundation models (FMs) emerge as a promising solution to harness distributed and diverse environmental data by leveraging prior knowledge to understand the complicated temporal and spatial correlations within heterogeneous datasets.
Case-Based or Rule-Based: How Do Transformers Do the Math?
Through carefully designed intervention experiments on five math tasks, we confirm that transformers are performing case-based reasoning, no matter whether scratchpad is used, which aligns with the previous observations that transformers use subgraph matching/shortcut learning to reason.
InstructEdit: Instruction-based Knowledge Editing for Large Language Models
Knowledge editing for large language models can offer an efficient solution to alter a model's behavior without negatively impacting the overall performance.
Metacognition-Enhanced Few-Shot Prompting With Positive Reinforcement
Few-shot prompting elicits the remarkable abilities of large language models by equipping them with a few demonstration examples in the input.
BERT-PIN: A BERT-based Framework for Recovering Missing Data Segments in Time-series Load Profiles
To adopt a standard Transformer model structure for profile inpainting, we segment the load and temperature profiles into line segments, treating each segment as a word and the entire profile as a sentence.
Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learning
Internet of Things (IoT) technologies have enabled numerous data-driven mobile applications and have the potential to significantly improve environmental monitoring and hazard warnings through the deployment of a network of IoT sensors.
Intelligent Communication
Multi-agent Reinforcement Learning
+1
DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data
Despite the tremendous advances achieved over the past years by deep learning techniques, the latest risk prediction models for industrial applications still rely on highly handtuned stage-wised statistical learning tools, such as gradient boosting and random forest methods.
Is ChatGPT a Good Personality Recognizer? A Preliminary Study
Concretely, we employ a variety of prompting strategies to explore ChatGPT's ability in recognizing personality from given text, especially the level-oriented prompting strategy we designed for guiding ChatGPT in analyzing given text at a specified level.
Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding Models
Results from the experiments demonstrate that there is a significant performance gap between the in-distribution (ID) and OOD settings for document images, and that fine-grained analysis of distribution shifts can reveal the brittle nature of existing pre-trained VDU models and OOD generalization algorithms.
Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large Language Models
We also consider the ensemble of code prompting and CoT prompting to combine the strengths of both.
GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing
Careful placement of a computational application within a target device cluster is critical for achieving low application completion time.
Not All Pixels Are Equal: Learning Pixel Hardness for Semantic Segmentation
Yet, most existing hard pixel mining strategies for semantic segmentation often rely on pixel's loss value, which tends to decrease during training.
T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Mixed Large Language Model Signals for Science Question Answering
To address these issues, we propose a novel method termed T-SciQ that aims at teaching science question answering with LLM signals.
ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction
To this end, we propose a simple but effective in-context learning framework called ICL-D3IE, which enables LLMs to perform DIE with different types of demonstration examples.
A Modified Sequence-to-point HVAC Load Disaggregation Algorithm
This paper presents a modified sequence-to-point (S2P) algorithm for disaggregating the heat, ventilation, and air conditioning (HVAC) load from the total building electricity consumption.
Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation
We propose a Generator structure consisting of a coarse network and a fine-tuning network.
MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations
This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of synthetic load profiles (SLPs) simultaneously.
CSSAM:Code Search via Attention Matching of Code Semantics and Structures
By leveraging the residual interaction, a matching module is designed to preserve more code semantics and descriptive features, that enhances the adhesion between the code and its corresponding query text.
Discriminator-Free Generative Adversarial Attack
The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1), making the DNNs-based systems collapsed byadding the inconspicuous perturbations to the images.
Network Clustering for Multi-task Learning
The majority of current MTL studies adopt the hard parameter sharing structure, where hard layers tend to learn general representations over all tasks and specific layers are prone to learn specific representations for each task.
Resolving the two-dimensional ANNNI model using transfer matrices
The phase diagram of the two-dimensional ANNNI model has long been theoretically debated.
Statistical Mechanics Soft Condensed Matter
Cloud Cover and Aurora Contamination at Dome A in 2017 from KLCAM
Dome A in Antarctica has many characteristics that make it an excellent site for astronomical observations, from the optical to the terahertz.
Instrumentation and Methods for Astrophysics
Deep Multi-Interest Network for Click-through Rate Prediction
Then the Multi-Interest Extractor Layer is applied to extract multiple user interests.
Comment on "Kosterlitz-Thouless-type caging-uncaging transition in a quasi-one-dimensional hard disk system" [Phys. Rev. Research 2, 033351 (2020)]
Research 2, 033351 (2020)] report a power-law decay of positional order in numerical simulations of hard disks confined within hard parallel walls, which they interpret as a Kosterlitz-Thouless-type caging-uncaging transition.
Soft Condensed Matter Statistical Mechanics
Time-aware Graph Embedding: A temporal smoothness and task-oriented approach
Knowledge graph embedding, which aims to learn the low-dimensional representations of entities and relationships, has attracted considerable research efforts recently.
FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval
In this paper, we address the text and image matching in cross-modal retrieval of the fashion industry.
Deep Hierarchical Classification for Category Prediction in E-commerce System
In e-commerce system, category prediction is to automatically predict categories of given texts.
Vehicle Re-Identification Based on Complementary Features
Top performance in City-Scale Multi-Camera Vehicle Re-Identification demonstrated the advantage of our methods, and we got 5-th place in the vehicle Re-ID track of AIC2020.
Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN
It has achieved great success on many tasks of network analysis such as link prediction and node classification.
Application of Deep Q-Network in Portfolio Management
This paper will introduce a strategy based on the classic Deep Reinforcement Learning algorithm, Deep Q-Network, for portfolio management in stock market.
Optimal Delivery with Budget Constraint in E-Commerce Advertising
Online advertising in E-commerce platforms provides sellers an opportunity to achieve potential audiences with different target goals.
Important Attribute Identification in Knowledge Graph
The knowledge graph(KG) composed of entities with their descriptions and attributes, and relationship between entities, is finding more and more application scenarios in various natural language processing tasks.
Learning Theory and Algorithms for Revenue Management in Sponsored Search
Online advertisement is the main source of revenue for Internet business.
Dual Based DSP Bidding Strategy and its Application
In recent years, RTB(Real Time Bidding) becomes a popular online advertisement trading method.
