Search Results for author:
Found 13 papers, 2 papers with code
CausalBench: A Comprehensive Benchmark for Causal Learning Capability of Large Language Models
To address these challenges, this paper proposes a comprehensive benchmark, namely CausalBench, to evaluate the causality understanding capabilities of LLMs.
Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models
To the best of our knowledge, this work presents the first systematic evaluation of LLMs for numerical optimization, offering a progressive, wide-coverage, and behavioral analysis.
How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems
Recently, large language models (LLMs) have notably positioned them as capable tools for addressing complex optimization challenges.
Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap
Large Language Models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artificial general intelligence.
Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation
Following the extraction of embedding vectors for both algorithms and problems, the most suitable algorithm is determined through calculations of matching degrees.
Dual Graph Multitask Framework for Imbalanced Delivery Time Estimation
To address the issue, we propose a novel Dual Graph Multitask framework for imbalanced Delivery Time Estimation (DGM-DTE).
Inductive Graph Transformer for Delivery Time Estimation
Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase experiences.
An AIoT-enabled Autonomous Dementia Monitoring System
The system mainly implements two functions based on the activity inference of the sensor data, which are real time abnormal activity monitoring and trend prediction of disease related activities.
Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition
Deep neural networks such as BERT have made great progress in relation classification.
Multi-label Causal Variable Discovery: Learning Common Causal Variables and Label-specific Causal Variables
Analyzing these mechanisms, we provide the theoretical property of common causal variables, based on which the discovery and distinguishing algorithm is designed to identify these two types of variables.
Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning: A Field Experiment
Field experiment results suggest that DRCR is a more appropriate reward function than revenue, which is widely used by current literature.
Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning
We first extend the application of dynamic pricing to a continuous pricing action space.
Point Context: An Effective Shape Descriptor for RST-invariant Trajectory Recognition
Secondly, the shape context is nonlinearly mapped to a subspace by kernel nonparametric discriminant analysis (KNDA) to get a compact feature representation, and thus a trajectory is projected to a single point in a low-dimensional feature space.
