Search Results for author:
Found 12 papers, 5 papers with code
Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering
To simulate real-world scenarios and evaluate the ability of LLMs to integrate internal and external knowledge, in this paper, we propose leveraging LLMs for QA under Incomplete Knowledge Graph (IKGQA), where the given KG doesn't include all the factual triples involved in each question.
Imagination Augmented Generation: Learning to Imagine Richer Context for Question Answering over Large Language Models
Retrieval-Augmented-Generation and Gener-ation-Augmented-Generation have been proposed to enhance the knowledge required for question answering over Large Language Models (LLMs).
Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs
However, these methods train KG embeddings and neural set operators concurrently on both simple (one-hop) and complex (multi-hop and logical) queries, which causes performance degradation on simple queries and low training efficiency.
GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark
With a fast developing pace of geographic applications, automatable and intelligent models are essential to be designed to handle the large volume of information.
MGeo: Multi-Modal Geographic Pre-Training Method
Single-modal PTMs can barely make use of the important GC and therefore have limited performance.
Hyper-GST: Predict Metro Passenger Flow Incorporating GraphSAGE, Hypergraph, Social-meaningful Edge Weights and Temporal Exploitation
Graph-based deep learning algorithms could utilise the graph structure but raise a few challenges, such as how to determine the weights of the edges and the shallow receptive field caused by the over-smoothing issue.
Boosting ship detection in SAR images with complementary pretraining techniques
In this paper, to resolve the problem of inconsistent imaging perspective between ImageNet and earth observations, we propose an optical ship detector (OSD) pretraining technique, which transfers the characteristics of ships in earth observations to SAR images from a large-scale aerial image dataset.
The QXS-SAROPT Dataset for Deep Learning in SAR-Optical Data Fusion
We obtain the SAR patches from SAR satellite GaoFen-3 images and the optical patches from Google Earth images.
Truncation-Free Matching System for Display Advertising at Alibaba
When receiving a user request, matching system (i) finds the crowds that the user belongs to; (ii) retrieves all ads that have targeted those crowds.
Towards GANs' Approximation Ability
Generative adversarial networks (GANs) have attracted intense interest in the field of generative models.
Training few-shot classification via the perspective of minibatch and pretraining
Few-shot classification is a challenging task which aims to formulate the ability of humans to learn concepts from limited prior data and has drawn considerable attention in machine learning.
Task-Driven Common Representation Learning via Bridge Neural Network
The experiments on the tasks, including pair matching, canonical correlation analysis, transfer learning, and reconstruction demonstrate the state-of-the-art performance of BNN, which may provide new insights into the aspect of common representation learning.
