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Found 16 papers, 10 papers with code
Decentralized Word2Vec Using Gossip Learning
Advanced NLP models require huge amounts of data from various domains to produce high-quality representations.
Challenging the Assumption of Structure-based embeddings in Few- and Zero-shot Knowledge Graph Completion
In this paper, we report experiments on Few- and Zero-shot Knowledge Graph completion, where the objective is to add missing relational links between entities into an existing Knowledge Graph with few or no previous examples of the relation in question.
Are We Wasting Time? A Fast, Accurate Performance Evaluation Framework for Knowledge Graph Link Predictors
First, we empirically find and theoretically motivate why sampling uniformly at random vastly overestimates the ranking performance of a method.
Learning Cellular Coverage from Real Network Configurations using GNNs
Cellular coverage quality estimation has been a critical task for self-organized networks.
Open World Learning Graph Convolution for Latency Estimation in Routing Networks
Accurate routing network status estimation is a key component in Software Defined Networking.
Decentralized adaptive clustering of deep nets is beneficial for client collaboration
We study the problem of training personalized deep learning models in a decentralized peer-to-peer setting, focusing on the setting where data distributions differ between the clients and where different clients have different local learning tasks.
Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming Events
By considering a new event as a task, we design an actor-critic learning scheme to compute the optimal policy on estimating the viewers' high-bandwidth connections.
Jointly Learnable Data Augmentations for Self-Supervised GNNs
Recently, a number of SSL methods for graph representation learning have achieved performance comparable to SOTA semi-supervised GNNs.
A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming
We first formulate the user experience prediction problem as a classification task, accounting for the fact that most of the viewers at the beginning of an event have poor quality of experience due to low-bandwidth connections and limited interactions with the tracker.
Federated Word2Vec: Leveraging Federated Learning to Encourage Collaborative Representation Learning
The results show that neither the quality of the results nor the convergence time in Federated Word2Vec deteriorates as compared to centralised Word2Vec.
Self-supervised Graph Neural Networks without explicit negative sampling
This calls for unsupervised learning techniques that are powerful enough to achieve comparable results as semi-supervised/supervised techniques.
EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming Events
We evaluate our proposed model on the link prediction task on three real-world datasets, generated by live video streaming events.
Dynamic Embeddings for Interaction Prediction
In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention.
Pedestrian Trajectory Prediction with Convolutional Neural Networks
In this work, we propose a new approach to pedestrian trajectory prediction, with the introduction of a novel 2D convolutional model.
Gossip and Attend: Context-Sensitive Graph Representation Learning
In this study we show that in-order to extract high-quality context-sensitive node representations it is not needed to rely on supplementary node features, nor to employ computationally heavy and complex models.
Graph Neighborhood Attentive Pooling
Network representation learning (NRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and sparse graphs.
