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Found 9 papers, 6 papers with code
JaxPruner: A concise library for sparsity research
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research.
GRPE: Relative Positional Encoding for Graph Transformer
The former loses preciseness of relative position from linearization, while the latter loses a tight integration of node-edge and node-topology interaction.
Ranked #13 on
Graph Regression
on PCQM4Mv2-LSC
Multi-level Distance Regularization for Deep Metric Learning
These allow the parameters of the embedding network to be settle on a local optima with better generalization.
Diversified Mutual Learning for Deep Metric Learning
Mutual learning is an ensemble training strategy to improve generalization by transferring individual knowledge to each other while simultaneously training multiple models.
BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition
Moreover, we propose a novel compensation method to increase the number of referenced instances in the training stage.
GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition
In the field of face recognition, a model learns to distinguish millions of face images with fewer dimensional embedding features, and such vast information may not be properly encoded in the conventional model with a single branch.
Regularizing Neural Networks via Stochastic Branch Layers
We introduce a novel stochastic regularization technique for deep neural networks, which decomposes a layer into multiple branches with different parameters and merges stochastically sampled combinations of the outputs from the branches during training.
Discrete Infomax Codes for Supervised Representation Learning
In this paper we present a model that produces Discrete InfoMax Codes (DIMCO); we learn a probabilistic encoder that yields k-way d-dimensional codes associated with input data.
Relational Knowledge Distillation
Knowledge distillation aims at transferring knowledge acquired in one model (a teacher) to another model (a student) that is typically smaller.
