Papers Inductive Learning
“Inductive Learning” 태그가 달린 논문 153편 · 필터 해제
GraphSAINT: Graph Sampling Based Inductive Learning Method
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "n…
Graph AttentionGraph EmbeddingGraph Representation LearningGraph Sampling+3Self-organized inductive reasoning with NeMuS
Neural Multi-Space (NeMuS) is a weighted multi-space representation for a portion of first-order logic designed for use with machine learning and neural network methods. It was demonstrated that it can be used to perform…
BIG-bench Machine LearningInductive LearningEfficient predicate invention using shared "NeMuS"
Amao is a cognitive agent framework that tackles the invention of predicates with a different strategy as compared to recent advances in Inductive Logic Programming (ILP) approaches like Meta-Intepretive Learning (MIL) t…
Inductive LearningInductive logic programmingBenchmarking Regression Methods: A comparison with CGAN
In recent years, impressive progress has been made in the design of implicit probabilistic models via Generative Adversarial Networks (GAN) and its extension, the Conditional GAN (CGAN). Excellent solutions have been dem…
BenchmarkingInductive LearningregressionGraph Attention Auto-Encoders
Auto-encoders have emerged as a successful framework for unsupervised learning. However, conventional auto-encoders are incapable of utilizing explicit relations in structured data. To take advantage of relations in grap…
DecoderGraph AttentionInductive LearningNode Classification+1Representation Learning for Attributed Multiplex Heterogeneous Network
Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and cannot scale well to handle large networks…
Graph EmbeddingInductive LearningLink PredictionNetwork Embedding+2Neural Logic Machines
We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning. NLMs exploit the power of both neural networks---as function approximators, and logic programming…
Decision MakingInductive LearningInductive logic programmingRelational ReasoningGraphNAS: Graph Neural Architecture Search with Reinforcement Learning
Graph Neural Networks (GNNs) have been popularly used for analyzing non-Euclidean data such as social network data and biological data. Despite their success, the design of graph neural networks requires a lot of manual …
General ClassificationInductive LearningNeural Architecture SearchNode Classification+3Learning STRIPS Action Models with Classical Planning
This paper presents a novel approach for learning STRIPS action models from examples that compiles this inductive learning task into a classical planning task. Interestingly, the compilation approach is flexible to diffe…
Inductive LearningDifferentiable Physics-informed Graph Networks
While physics conveys knowledge of nature built from an interplay between observations and theory, it has been considered less importantly in deep neural networks. Especially, there are few works leveraging physics behav…
Inductive LearningWeather ForecastingCoupled Analysis Dictionary Learning to inductively learn inversion: Application to real-time reconstruction of Biomedical signals
This work addresses the problem of reconstructing biomedical signals from their lower dimensional projections. Traditionally Compressed Sensing (CS) based techniques have been employed for this task. These are transducti…
compressed sensingDenoisingDictionary LearningInductive LearningOn Evaluating the Generalization of LSTM Models in Formal Languages
Recurrent Neural Networks (RNNs) are theoretically Turing-complete and established themselves as a dominant model for language processing. Yet, there still remains an uncertainty regarding their language learning capabil…
Inductive LearningCross-domain aspect extraction for sentiment analysis: a transductive learning approach
Aspect-Based Sentiment Analysis (ABSA) is a promising approach to analyze consumer reviews at a high level of detail, where the opinion about each fea- ture of the product or service is considered. ABSA usually explore…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Aspect ExtractionInductive Learning+4Deep Graph Infomax
We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations …
General ClassificationInductive LearningNode ClassificationInductive Learning of Answer Set Programs from Noisy Examples
In recent years, non-monotonic Inductive Logic Programming has received growing interest. Specifically, several new learning frameworks and algorithms have been introduced for learning under the answer set semantics, all…
Common Sense ReasoningInductive LearningInductive logic programmingLarge-Scale Learnable Graph Convolutional Networks
Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs. In CNNs, the trainable local filters en…
Document ClassificationInductive LearningNode ClassificationInductive Two-Layer Modeling with Parametric Bregman Transfer
Latent prediction models, exemplified by multi-layer networks, employ hidden variables that automate abstract feature discovery. They typically pose nonconvex optimization problems and effective semi-definite progra…
Inductive LearningPredictionTransductive LearningVocal Bursts Valence PredictionEmbedding Models for Episodic Knowledge Graphs
In recent years a number of large-scale triple-oriented knowledge graphs have been generated and various models have been proposed to perform learning in those graphs. Most knowledge graphs are static and reflect the wor…
Inductive LearningKnowledge Graph EmbeddingsKnowledge GraphsLearning Role-based Graph Embeddings
Random walks are at the heart of many existing network embedding methods. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features resulting from these methods are unable…
Inductive LearningNetwork EmbeddingRecursive Feature Generation for Knowledge-based Learning
When humans perform inductive learning, they often enhance the process with background knowledge. With the increasing availability of well-formed collaborative knowledge bases, the performance of learning algorithms coul…
General ClassificationInductive Learningtext-classificationText Classification