Search Results for author:
Found 17 papers, 4 papers with code
Imperative Learning: A Self-supervised Neural-Symbolic Learning Framework for Robot Autonomy
The framework of IL consists of three primary components: a neural module, a reasoning engine, and a memory system.
M3GIA: A Cognition Inspired Multilingual and Multimodal General Intelligence Ability Benchmark
As recent multi-modality large language models (MLLMs) have shown formidable proficiency on various complex tasks, there has been increasing attention on debating whether these models could eventually mirror human intelligence.
PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction in Off-road Driving
To bridge this gap, we present PhysORD, a neural-symbolic approach integrating the conservation law, i. e., the Euler-Lagrange equation, into data-driven neural models for motion prediction in off-road driving.
DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation
Continuous Relation Extraction (CRE) aims to incrementally learn relation knowledge from a non-stationary stream of data.
Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolation
The objective of topic inference in research proposals aims to obtain the most suitable disciplinary division from the discipline system defined by a funding agency.
NEEDED: Introducing Hierarchical Transformer to Eye Diseases Diagnosis
It aims to evaluate the condition of both eyes of a patient respectively, and we formulate it as a particular multi-label classification task in this paper.
Hierarchical MixUp Multi-label Classification with Imbalanced Interdisciplinary Research Proposals
In response to this question, we propose a hierarchical mixup multiple-label classification framework, which we called H-MixUp.
Graph Soft-Contrastive Learning via Neighborhood Ranking
Moreover, as the neighborhood size exponentially expands with more hops considered, we propose neighborhood sampling strategies to improve learning efficiency.
Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification
Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals.
Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals
After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal.
RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training
In this paper, we propose a multi-task self-supervised learning-based researcher data pre-training model named RPT.
Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification
In this paper, we formulate the proposal classification problem into a hierarchical multi-label classification task.
Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification
However, data augmentation for graph-based models remains a challenging problem, as graph data is more complex than traditional data, which consists of two features with different properties: graph topology and node attributes.
TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution
We propose a frequency domain-based spatio-temporal remote sensingsingle image super-resolution technique to reconstruct the HR image combined with generative adversarialnetworks (GANs) on various frequency bands (TWIST-GAN).
LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding
Knowledge graph embedding (KGE) models learn to project symbolic entities and relations into a continuous vector space based on the observed triplets.
Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion
Most researches for knowledge graph completion learn representations of entities and relations to predict missing links in incomplete knowledge graphs.
Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning
The integrated hierarchical aggregation module aims to preserve the tree structure by combining GNN with Gated Recurrent Unit to integrate the hierarchical and sequential neighborhood information on the tree structure to node representations.
