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Found 30 papers, 11 papers with code
Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networks
Better understanding the natural world is a crucial task with a wide range of applications.
S-T CRF: Spatial-Temporal Conditional Random Field for Human Trajectory Prediction
Trajectory prediction is of significant importance in computer vision.
STF: Spatial Temporal Fusion for Trajectory Prediction
The main reason is that the trajectory is a kind of complex data, including spatial and temporal information, which is crucial for accurate prediction.
Zero-knowledge Proof Meets Machine Learning in Verifiability: A Survey
However, in practice, due to various challenges such as limited computational resources and data privacy concerns, users in need of models often cannot train machine learning models locally.
CSG: Curriculum Representation Learning for Signed Graph
Signed graphs are valuable for modeling complex relationships with positive and negative connections, and Signed Graph Neural Networks (SGNNs) have become crucial tools for their analysis.
SGA: A Graph Augmentation Method for Signed Graph Neural Networks
Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-world signed graphs containing positive and negative links.
Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical Reasoning
Despite their high performance on the original publicly available datasets, we find that all models perform poorly on these newly constructed datasets.
Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy
Learnersourcing offers great potential for scalable education through student content creation.
Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
When learnersourcing multiple-choice questions, creating explanations for the solution of a question is a crucial step; it helps other students understand the solution and promotes a deeper understanding of related concepts.
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner.
A Survey of Data Pricing for Data Marketplaces
Data pricing, as a key function of a data marketplace, demands quantifying the monetary value of data.
GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation
Instead, we propose a user-agnostic global trajectory flow map and a novel Graph Enhanced Transformer model (GETNext) to better exploit the extensive collaborative signals for a more accurate next POI prediction, and alleviate the cold start problem in the meantime.
Learning Density-Based Correlated Equilibria for Markov Games
In real-world multi-agent systems, in addition to being in an equilibrium, agents' policies are often expected to meet requirements with respect to safety, and fairness.
USER: Unsupervised Structural Entropy-based Robust Graph Neural Network
To this end, we propose USER, an unsupervised robust version of graph neural networks that is based on structural entropy.
MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring based on A Dual-CNN Model
Non-intrusive load monitoring (NILM) aims to decompose aggregated electrical usage signal into appliance-specific power consumption and it amounts to a classical example of blind source separation tasks.
Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification
Few-shot learning (FSL) is an emergent paradigm of learning that attempts to learn to reason with low sample complexity to mimic the way humans learn, generalise and extrapolate from only a few seen examples.
Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisation
In our model, reasoning is performed using an iterative memory neural network based on RNN with a gated attention mechanism.
From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory
Understanding, modelling and predicting human risky decision-making is challenging due to intrinsic individual differences and irrationality.
Individual-Level Inverse Reinforcement Learning for Mean Field Games
This limitation invalidates existing IRL methods on MFGs with non-cooperative environments.
DeepQR: Neural-based Quality Ratings for Learnersourced Multiple-Choice Questions
We propose DeepQR, a novel neural-network model for AQQR that is trained using multiple-choice-question (MCQ) datasets collected from PeerWise, a widely-used learnersourcing platform.
GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series
The main novelty of the model is the integration of time series of four different time granularities: the original time series, together with hourly, daily, and weekly time series.
Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting
We argue that such correlations are universal and play a pivotal role in traffic flow.
Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting
We argue that such correlations are universal and play a pivotal role in traffic flow.
Generating Relevant and Coherent Dialogue Responses using Self-separated Conditional Variational AutoEncoders
Conditional Variational AutoEncoder (CVAE) effectively increases the diversity and informativeness of responses in open-ended dialogue generation tasks through enriching the context vector with sampled latent variables.
THINK: A Novel Conversation Model for Generating Grammatically Correct and Coherent Responses
In this work, we proposed a conversation model named "THINK" (Teamwork generation Hover around Impressive Noticeable Keywords) to make the decoder more complicated and avoid generating duplicated and self-contradicting responses.
Adversarial Inverse Reinforcement Learning for Mean Field Games
However, existing IRL methods for MFGs are powerless to reason about uncertainties in demonstrated behaviours of individual agents.
HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention
This paper proposes a chatbot framework that adopts a hybrid model which consists of a knowledge graph and a text similarity model.
REM: From Structural Entropy to Community Structure Deception
This paper focuses on the privacy risks of disclosing the community structure in an online social network.
Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering
Furthermore, we also measure the model accuracies with different answer context lengths and different Bible translations.
From the Periphery to the Center: Information Brokerage in an Evolving Network
We model integration as the interplay between the newcomer and the dynamics network and capture information brokerage using a process of relationship building.
