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Found 10 papers, 6 papers with code
Unified Uncertainty Estimation for Cognitive Diagnosis Models
Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets.
Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms
Machine learning algorithms have become ubiquitous in a number of applications (e. g. image classification).
Continuous-Time User Preference Modelling for Temporal Sets Prediction
Previous studies for temporal sets prediction mainly focus on the modelling of elements and implicitly represent each user's preference based on his/her interacted elements.
GraphMI: Extracting Private Graph Data from Graph Neural Networks
Then we design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
Heterogeneous Graph Representation Learning with Relation Awareness
Moreover, a semantic fusing module is presented to aggregate relation-aware node representations into a compact representation with the learned relation representations.
Ranked #21 on
Node Property Prediction
on ogbn-mag
Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning
Representation learning on heterogeneous graphs aims to obtain low-dimensional node representations that could preserve both node attributes and relation information.
Ranked #23 on
Node Property Prediction
on ogbn-mag
Predicting Temporal Sets with Deep Neural Networks
Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set.
Exploiting Cognitive Structure for Adaptive Learning
Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e. g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items.
Skeptical Deep Learning with Distribution Correction
Recently deep neural networks have been successfully used for various classification tasks, especially for problems with massive perfectly labeled training data.
Seeing the Forest from the Trees in Two Looks: Matrix Sketching by Cascaded Bilateral Sampling
Given a matrix A of size m by n, state-of-the-art randomized algorithms take O(m * n) time and space to obtain its low-rank decomposition.
