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Found 5 papers, 4 papers with code
Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types
We then show how double-equivariant architectures are able to self-supervise pre-train on distinct KG domains and zero-shot predict links on a new KG domain (with completely new entities and new relation types).
PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient
First, the difference in feature magnitude between the teacher and the student could enforce overly strict constraints on the student.
On the Equivalence Between Temporal and Static Graph Representations for Observational Predictions
This work formalizes the associational task of predicting node attribute evolution in temporal graphs from the perspective of learning equivariant representations.
Channel-wise Knowledge Distillation for Dense Prediction
Observing that in semantic segmentation, some layers' feature activations of each channel tend to encode saliency of scene categories (analogue to class activation mapping), we propose to align features channel-wise between the student and teacher networks.
Infinity Learning: Learning Markov Chains from Aggregate Steady-State Observations
We consider the task of learning a parametric Continuous Time Markov Chain (CTMC) sequence model without examples of sequences, where the training data consists entirely of aggregate steady-state statistics.
