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Found 30 papers, 16 papers with code
RFBFN: A Relation-First Blank Filling Network for Joint Relational Triple Extraction
However, most existing works either ignore the semantic information of relations or predict subjects and objects sequentially.
Characterizing the Influence of Topology on Graph Learning Tasks
Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations.
Temporal Generalization Estimation in Evolving Graphs
In synthetic random graphs, we further refine the former lower bound to show the inevitable distortion over time and empirically observe that Smart achieves good estimation performance.
PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture Search
Multiple object tracking is a critical task in autonomous driving.
Is Reference Necessary in the Evaluation of NLG Systems? When and Where?
The majority of automatic metrics for evaluating NLG systems are reference-based.
Entity Alignment with Unlabeled Dangling Cases
We investigate the entity alignment problem with unlabeled dangling cases, meaning that there are entities in the source or target graph having no counterparts in the other, and those entities remain unlabeled.
AceMap: Knowledge Discovery through Academic Graph
The exponential growth of scientific literature requires effective management and extraction of valuable insights.
Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs
Considering the NIE problem, LICAP adopts a novel sampling strategy called top nodes preferred hierarchical sampling to first group all interested nodes into a top bin and a non-top bin based on node importance scores, and then divide the nodes within top bin into several finer bins also based on the scores.
Graph Parsing Networks
GPN benefits from the discrete assignments generated by the graph parsing algorithm, allowing good memory efficiency while preserving node information intact.
G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection
To address this issue, we propose the Generalizable loss (G-loss), which is an OoD-aware objective, preventing NAS from over-fitting by using gradient descent to optimize parameters not only on a subset of easy-to-learn features but also the remaining predictive features for generalization, and the overall framework is named G-NAS.
Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models
However, such methods can not defend against adaptive attacks, in which an attacker takes a countermove against a known defence strategy.
GeoGalactica: A Scientific Large Language Model in Geoscience
To our best knowledge, it is the largest language model for the geoscience domain.
Domain Invariant Learning for Gaussian Processes and Bayesian Exploration
We further demonstrate the effectiveness of the DIL-GP Bayesian optimization method on a PID parameters tuning experiment for a quadrotor.
Towards Controlled Table-to-Text Generation with Scientific Reasoning
The sheer volume of scientific experimental results and complex technical statements, often presented in tabular formats, presents a formidable barrier to individuals acquiring preferred information.
Human-Readable Fingerprint for Large Language Models
However, identifying the original base model of an LLM is challenging due to potential parameter alterations.
Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus
Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields.
Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation
To this end, we investigate the limits of historical information for temporal knowledge graph extrapolation and propose a new event forecasting model called Contrastive Event Network (CENET) based on a novel training framework of historical contrastive learning.
Graph Out-of-Distribution Generalization with Controllable Data Augmentation
The spurious correlations over hybrid distribution deviation degrade the performance of previous GNN methods and show large instability among different datasets.
K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization
Large language models (LLMs) have achieved great success in general domains of natural language processing.
Exploring and Verbalizing Academic Ideas by Concept Co-occurrence
The results demonstrate that our system has broad prospects and can assist researchers in expediting the process of discovering new ideas.
Prediction with Incomplete Data under Agnostic Mask Distribution Shift
We focus on the case where the underlying joint distribution of complete features and label is invariant, but the missing pattern, i. e., mask distribution may shift agnostically between training and testing.
Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph
In this work, we propose Covidia, COVID-19 interdisciplinary academic knowledge graph to bridge the gap between knowledge of COVID-19 on different domains.
PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model
In the research of end-to-end dialogue systems, using real-world knowledge to generate natural, fluent, and human-like utterances with correct answers is crucial.
Text Classification in the Wild: a Large-scale Long-tailed Name Normalization Dataset
In this work, we first collect a large-scale institution name normalization dataset LoT-insts1, which contains over 25k classes that exhibit a naturally long-tailed distribution.
Ranked #1 on
Long-tail Learning
on Lot-insts
Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing
This is achieved by aligning the hierarchy of the rooted-tree of a central node with the ordered neurons in its node representation.
Ranked #4 on
Node Classification
on Cornell
KnowledgeShovel: An AI-in-the-Loop Document Annotation System for Scientific Knowledge Base Construction
Constructing a comprehensive, accurate, and useful scientific knowledge base is crucial for human researchers synthesizing scientific knowledge and for enabling Al-driven scientific discovery.
IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications
Understanding the origin and influence of the publication's idea is critical to conducting scientific research.
Disentangled Graph Contrastive Learning for Review-based Recommendation
In review-based recommendation methods, review data is considered as auxiliary information that can improve the quality of learned user/item or interaction representations for the user rating prediction task.
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL
Our model can incorporate almost all types of existing relations in the literature, and in addition, we propose introducing co-reference relations for the multi-turn scenario.
Ranked #1 on
Dialogue State Tracking
on CoSQL
3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds
Our method provides an automatic process that maps the raw data to the classification results.
