Papers Inductive Learning
“Inductive Learning” 태그가 달린 논문 153편 · 필터 해제
GraphCL: Contrastive Self-Supervised Learning of Graph Representations
We propose Graph Contrastive Learning (GraphCL), a general framework for learning node representations in a self supervised manner. GraphCL learns node embeddings by maximizing the similarity between the representations …
Contrastive LearningInductive LearningNode ClassificationSelf-Supervised LearningTRANSFER :- DEEP INDUCTIVE NETWORK FOR FACIAL EMOTION RECOGNITION
The image-based Facial Emotion Recognition (FER) aims to classify the image into basic emotions being communicated by it. FER is one of the most prominent research areas in computer vision. Most of the existing works are…
Emotion RecognitionFacial Emotion RecognitionInductive LearningTransfer LearningIterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly and iteratively learning graph structure and graph embedding. The key rationale of IDGL is to lea…
Graph EmbeddingGraph LearningInductive LearningMetric LearningIs deep learning necessary for simple classification tasks?
Automated machine learning (AutoML) and deep learning (DL) are two cutting-edge paradigms used to solve a myriad of inductive learning tasks. In spite of their successes, little guidance exists for when to choose one app…
AutoMLBinary ClassificationClassificationDeep Learning+2DFraud3- Multi-Component Fraud Detection freeof Cold-start
Fraud review detection is a hot research topic inrecent years. The Cold-start is a particularly new but significant problem referring to the failure of a detection system to recognize the authenticity of a new user. Stat…
Component ClassificationFraud DetectionGraph EmbeddingInductive Learning+1Deep Graph Contrastive Representation Learning
Graph representation learning nowadays becomes fundamental in analyzing graph-structured data. Inspired by recent success of contrastive methods, in this paper, we propose a novel framework for unsupervised graph represe…
AttributeContrastive LearningGraph Representation LearningInductive Learning+3Graphs, Entities, and Step Mixture
Existing approaches for graph neural networks commonly suffer from the oversmoothing issue, regardless of how neighborhoods are aggregated. Most methods also focus on transductive scenarios for fixed graphs, leading to p…
Graph Neural NetworkInductive LearningThe ILASP system for Inductive Learning of Answer Set Programs
The goal of Inductive Logic Programming (ILP) is to learn a program that explains a set of examples in the context of some pre-existing background knowledge. Until recently, most research on ILP targeted learning Prolog …
Common Sense ReasoningInductive LearningInductive logic programmingEvery Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks
Text classification is fundamental in natural language processing (NLP), and Graph Neural Networks (GNN) are recently applied in this task. However, the existing graph-based works can neither capture the contextual word …
ClassificationDocument EmbeddingGeneral ClassificationInductive Learning+2A Collective Learning Framework to Boost GNN Expressiveness
Graph Neural Networks (GNNs) have recently been used for node and graph classification tasks with great success, but GNNs model dependencies among the attributes of nearby neighboring nodes rather than dependencies among…
ClassificationGeneral ClassificationGraph ClassificationInductive Learning+2An Uncoupled Training Architecture for Large Graph Learning
Graph Convolutional Network (GCN) has been widely used in graph learning tasks. However, GCN-based models (GCNs) is an inherently coupled training framework repetitively conducting the complex neighboring aggregation, wh…
Graph LearningInductive LearningRegression via Implicit Models and Optimal Transport Cost Minimization
This paper addresses the classic problem of regression, which involves the inductive learning of a map, $y=f(x,z)$, $z$ denoting noise, $f:\mathbb{R}^n\times \mathbb{R}^k \rightarrow \mathbb{R}^m$. Recently, Conditional …
Inductive LearningregressionLearning to Generate Levels From Nothing
Machine learning for procedural content generation has recently become an active area of research. Levels vary in both form and function and are mostly unrelated to each other across games. This has made it difficult to …
Image GenerationInductive LearningReinforcement LearningFrom Anchor Generation to Distribution Alignment: Learning a Discriminative Embedding Space for Zero-Shot Recognition
In zero-shot learning (ZSL), the samples to be classified are usually projected into side information templates such as attributes. However, the irregular distribution of templates makes classification results confused. …
Inductive LearningZero-Shot LearningDeep Iterative and Adaptive Learning for Graph Neural Networks
In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneo…
Graph LearningGraph structure learningInductive LearningMetric Learning+1Deep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphs
From social networks to protein complexes to disease genomes to visual data, hypergraphs are everywhere. However, the scope of research studying deep learning on hypergraphs is still quite sparse and nascent, as there ha…
hyperedge classificationhypergraph embeddingInductive LearningNode ClassificationLearn to Explain Efficiently via Neural Logic Inductive Learning
The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems. In this work, we study the learning to explain problem in the scope of inductive log…
Inductive LearningInductive logic programmingOn the Equivalence between Positional Node Embeddings and Structural Graph Representations
This work provides the first unifying theoretical framework for node (positional) embeddings and structural graph representations, bridging methods like matrix factorization and graph neural networks. Using invariant the…
Inductive LearningLink PredictionNode ClassificationTriad PredictionNon-monotonic Logical Reasoning Guiding Deep Learning for Explainable Visual Question Answering
State of the art algorithms for many pattern recognition problems rely on deep network models. Training these models requires a large labeled dataset and considerable computational resources. Also, it is difficult to und…
Inductive LearningLogical ReasoningQuestion AnsweringVisual Question Answering+1DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing
Point cloud processing is very challenging, as the diverse shapes formed by irregular points are often indistinguishable. A thorough grasp of the elusive shape requires sufficiently contextual semantic information, yet f…
3D Part SegmentationInductive Learning