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Found 41 papers, 16 papers with code
Personalized Negative Reservoir for Incremental Learning in Recommender Systems
Every day the volume of training data is expanding and the number of user interactions is constantly increasing.
Multi-resolution Time-Series Transformer for Long-term Forecasting
Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens.
Interacting Diffusion Processes for Event Sequence Forecasting
Our model is composed of two diffusion processes, one for the time intervals and one for the event types.
Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN
Training an EDNN architecture is challenging as it consists of two intertwined components: the gating mechanism (GM) that controls early-exiting decisions and the intermediate inference modules (IMs) that perform inference from intermediate representations.
Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification
Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text, and has a wide range of application domains.
Graph Inductive Biases in Transformers without Message Passing
Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings.
Ranked #1 on
Node Classification
on PATTERN
Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems
In contrast, for users who have static preferences, model performance can benefit greatly from preserving as much of the user's long-term preferences as possible.
Diffusing Gaussian Mixtures for Generating Categorical Data
Learning a categorical distribution comes with its own set of challenges.
Spectral Augmentations for Graph Contrastive Learning
Contrastive learning has emerged as a premier method for learning representations with or without supervision.
Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network
This performance relies heavily on the configuration of the network parameters.
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations.
DyG2Vec: Efficient Representation Learning for Dynamic Graphs
Temporal graph neural networks have shown promising results in learning inductive representations by automatically extracting temporal patterns.
Ranked #1 on
Dynamic Link Prediction
on Social Evolution
Evaluation of Categorical Generative Models -- Bridging the Gap Between Real and Synthetic Data
We validate our evaluation procedure with synthetic experiments on both synthetic generative models and current state-of-the-art categorical generative models.
Contrastive Learning for Time Series on Dynamic Graphs
We propose a framework called GraphTNC for unsupervised learning of joint representations of the graph and the time-series.
Node Copying: A Random Graph Model for Effective Graph Sampling
In this work, we introduce the node copying model for constructing a distribution over graphs.
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user.
Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks
Since a meaningful graph representing dependencies between bags is rarely available, we propose to use a Bayesian GNN framework that can generate a likely graph structure for scenarios where there is uncertainty in the graph or when no graph is available.
Multi-relation Message Passing for Multi-label Text Classification
These examples motivate the modelling of multiple types of bi-directional relationships between labels.
Multi-Label Classification
Multi-Label Image Classification
+4
Spectral image clustering on dual-energy CT scans using functional regression mixtures
In this paper, we develop novel functional data analysis (FDA) techniques and adapt them to the analysis of DECT decay curves.
Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction
To address this limitation, we propose a framework that can directly learn embeddings for the given netlist to enhance the quality of our node features.
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks.
TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion
The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge.
Knowledge-Enhanced Top-K Recommendation in Poincaré Ball
To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs.
Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation
Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them.
On Batch-size Selection for Stochastic Training for Graph Neural Networks
In recent years deep learning has become an important framework for supervised learning.
GraphSeam: Supervised Graph Learning Framework for Semantic UV Mapping
In this work, we use the power of supervised GNNs for the first time to propose a fully automated UV mapping framework that enables users to replicate their desired seam styles while reducing distortion and seam length.
Graph Learning
Graphics
GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
We develop a Graph Structure Aware Incremental Learning framework, GraphSAIL, to address the commonly experienced catastrophic forgetting problem that occurs when training a model in an incremental fashion.
A Framework for Recommending Accurate and Diverse ItemsUsing Bayesian Graph Convolutional Neural Networks
Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences.
Node Copying for Protection Against Graph Neural Network Topology Attacks
With the increased interest in graph based machine learning techniques, there have been investigations which suggest that these models are also vulnerable to attacks.
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
Node classification in attributed graphs is an important task in multiple practical settings, but it can often be difficult or expensive to obtain labels.
Non-Parametric Graph Learning for Bayesian Graph Neural Networks
A Bayesian framework which targets posterior inference of the graph by considering it as a random quantity can be beneficial.
Multi-Graph Convolution Collaborative Filtering
In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process.
Memory Augmented Graph Neural Networks for Sequential Recommendation
In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items.
Bayesian Graph Convolutional Neural Networks using Node Copying
Graph convolutional neural networks (GCNN) have numerous applications in different graph based learning tasks.
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning
Graph convolutional neural networks (GCNN) have been successfully applied to many different graph based learning tasks including node and graph classification, matrix completion, and learning of node embeddings.
Learning Gaussian Graphical Models with Ordered Weighted L1 Regularization
We propose two novel estimators based on the Ordered Weighted $\ell_1$ (OWL) norm: 1) The Graphical OWL (GOWL) is a penalized likelihood method that applies the OWL norm to the lower triangle components of the precision matrix.
Bayesian graph convolutional neural networks for semi-supervised classification
Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion.
Cost Adaptation for Robust Decentralized Swarm Behaviour
To allay this problem, we use a meta-learning process -- cost adaptation -- which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic.
Microwave breast cancer detection using Empirical Mode Decomposition features
One of the most widely used feature extraction method is principle component analysis (PCA).
Semi-parametric Order-based Generalized Multivariate Regression
In this paper, we consider a generalized multivariate regression problem where the responses are monotonic functions of linear transformations of predictors.
Sparse Multivariate Factor Regression
The first matrix linearly transforms the predictors to a set of latent factors, and the second one regresses the responses on these factors.
