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Found 40 papers, 17 papers with code
The Tensor Memory Hypothesis
We show how episodic memory and semantic memory can be realized and discuss how new memory traces can be generated from sensory input: Existing memories are the basis for perception and new memories are generated via perception.
Embedding Models for Episodic Knowledge Graphs
In this paper, we extend models for static knowledge graphs to temporal knowledge graphs.
Improving Information Extraction from Images with Learned Semantic Models
Many applications require an understanding of an image that goes beyond the simple detection and classification of its objects.
Improving Visual Relationship Detection using Semantic Modeling of Scene Descriptions
In this paper we consider scene descriptions which are represented as a set of triples (subject, predicate, object), where each triple consists of a pair of visual objects, which appear in the image, and the relationship between them (e. g. man-riding-elephant, man-wearing-hat).
Variational Quantum Circuit Model for Knowledge Graphs Embedding
In this work, we propose the first quantum Ans\"atze for the statistical relational learning on knowledge graphs using parametric quantum circuits.
Reasoning on Knowledge Graphs with Debate Dynamics
The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively.
Quantum Machine Learning Algorithm for Knowledge Graphs
We simplify the problem by making a plausible assumption that the tensor representation of a knowledge graph can be approximated by its low-rank tensor singular value decomposition, which is verified by our experiments.
Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs
The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to justify the fact being true (thesis) or the fact being false (antithesis), respectively.
The Tensor Brain: Semantic Decoding for Perception and Memory
In particular, we propose that explicit perception and declarative memories require a semantic decoder, which, in a simple realization, is based on four layers: First, a sensory memory layer, as a buffer for sensory input, second, an index layer representing concepts, third, a memoryless representation layer for the broadcasting of information ---the "blackboard", or the "canvas" of the brain--- and fourth, a working memory layer as a processing center and data buffer.
Causal Inference under Networked Interference and Intervention Policy Enhancement
After deriving causal effect estimators, we further study intervention policy improvement on the graph under capacity constraint.
Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs
The Hawkes process has become a standard method for modeling self-exciting event sequences with different event types.
Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score
Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups.
Controllable Multi-Character Psychology-Oriented Story Generation
In our work, we aim to design an emotional line for each character that considers multiple emotions common in psychological theories, with the goal of generating stories with richer emotional changes in the characters.
DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion
Product manifolds enable our approach to better reflect a wide variety of geometric structures on temporal KGs.
xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links
We evaluate our model on four benchmark temporal knowledge graphs for the link forecasting task.
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs
We evaluate our model on four benchmark temporal knowledge graphs for the link forecasting task.
KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation
We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformer-based sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal inputs of images and texts.
Contrastive Learning for Recommender System
To solve this problem, we propose a graph contrastive learning module for a general recommender system that learns the embeddings in a self-supervised manner and reduces the randomness of message dropout.
Temporal Knowledge Graph Forecasting with Neural ODE
In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i. e., edge formation and dissolution.
Adaptive Multi-Resolution Attention with Linear Complexity
Transformers have improved the state-of-the-art across numerous tasks in sequence modeling.
TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting
Compared with the completion task, the forecasting task is more difficult that faces two main challenges: (1) how to effectively model the time information to handle future timestamps?
The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding
Although memory appears to be about the past, its main purpose is to support the agent in the present and the future.
3D-RETR: End-to-End Single and Multi-View 3D Reconstruction with Transformers
3D-RETR is capable of 3D reconstruction from a single view or multiple views.
SEA: Graph Shell Attention in Graph Neural Networks
Intuitively, by increasing the number of experts, the models gain in expressiveness such that a node's representation is solely based on nodes that are located within the receptive field of an expert.
APPTeK: Agent-Based Predicate Prediction in Temporal Knowledge Graphs
In temporal Knowledge Graphs (tKGs), the temporal dimension is attached to facts in a knowledge base resulting in quadruples between entities such as (Nintendo, released, Super Mario, Sep-13-1985), where the predicate holds within a time interval or at a timestamp.
A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion
Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time.
TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs
Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types.
Open-domain Dialogue Generation Grounded with Dynamic Multi-form Knowledge Fusion
To merge these two forms of knowledge into the dialogue effectively, we design a dynamic virtual knowledge selector and a controller that help to enrich and expand knowledge space.
Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction
In this paper, we follow the previous work that focuses on few-shot relational learning on static KGs and extend two fundamental TKG reasoning tasks, i. e., interpolated and extrapolated link prediction, to the one-shot setting.
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs
To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions.
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information
Similar problem exists in temporal knowledge graphs (TKGs), and no previous temporal knowledge graph completion (TKGC) method is developed for modeling newly-emerged entities.
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning
Most previous TKGC methods only consider predicting the missing links among the entities seen in the training set, while they are unable to achieve great performance in link prediction concerning newly-emerged unseen entities.
Differentiable Quantum Architecture Search for Quantum Reinforcement Learning
However, the pre-defined circuit needs more flexibility for different tasks, and the circuit design based on various datasets could become intractable in the case of a large circuit.
GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models
Extensive experiments have shown that GenTKG outperforms conventional methods of temporal relational forecasting with low computation resources using extremely limited training data as few as 16 samples.
GraphextQA: A Benchmark for Evaluating Graph-Enhanced Large Language Models
The proposed dataset is designed to evaluate graph-language models' ability to understand graphs and make use of it for answer generation.
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models
We first input the text descriptions of KG relations into large language models (LLMs) for generating relation representations, and then introduce them into embedding-based TKGF methods.
Quantum Architecture Search with Unsupervised Representation Learning
Most QAS algorithms combine their search space and search algorithms together and thus generally require evaluating a large number of quantum circuits during the search process.
Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework
Various temporal knowledge graph (KG) completion models have been proposed in the recent literature.
Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs
In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i. e., edge formation and dissolution.
TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion
In this paper, we tackle the temporal knowledge graph completion task by proposing TempCaps, which is a Capsule network-based embedding model for Temporal knowledge graph completion.
