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Found 19 papers, 16 papers with code
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural network weights, and predicting generalization errors.
Can We Learn Communication-Efficient Optimizers?
Although many variants of these approaches have been proposed, they can sometimes lag behind state-of-the-art adaptive optimizers for deep learning.
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?
Pretraining a neural network on a large dataset is becoming a cornerstone in machine learning that is within the reach of only a few communities with large-resources.
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights
Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architecture search or knowledge distillation.
Model Zoos: A Dataset of Diverse Populations of Neural Network Models
With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of NN models for further research.
Hyper-Representations for Pre-Training and Transfer Learning
Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architecture search or knowledge distillation.
Pretraining a Neural Network before Knowing Its Architecture
A recently released Graph HyperNetwork (GHN) trained this way on one million smaller ImageNet architectures is able to predict parameters for large unseen networks such as ResNet-50.
On Evaluation Metrics for Graph Generative Models
While we focus on applying these metrics to GGM evaluation, in practice this enables the ability to easily compute the dissimilarity between any two sets of graphs regardless of domain.
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning
Discovering a solution in a combinatorial space is prevalent in many real-world problems but it is also challenging due to diverse complex constraints and the vast number of possible combinations.
Parameter Prediction for Unseen Deep Architectures
We introduce a large-scale dataset of diverse computational graphs of neural architectures - DeepNets-1M - and use it to explore parameter prediction on CIFAR-10 and ImageNet.
Ranked #1 on
Parameter Prediction
on CIFAR10
Context-Aware Scene Graph Generation With Seq2Seq Transformers
In this task, the model needs to detect objects and predict visual relationships between them.
Generative Compositional Augmentations for Scene Graph Prediction
However, test images might contain zero- and few-shot compositions of objects and relationships, e. g. <cup, on, surfboard>.
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
We show that such models can suffer the most in their ability to generalize to rare compositions, evaluating two different models on the Visual Genome dataset and its more recent, improved version, GQA.
Learning Temporal Attention in Dynamic Graphs with Bilinear Interactions
We consider a common case in which edges can be short term interactions (e. g., messaging) or long term structural connections (e. g., friendship).
Image Classification with Hierarchical Multigraph Networks
Graph Convolutional Networks (GCNs) are a class of general models that can learn from graph structured data.
Understanding Attention and Generalization in Graph Neural Networks
We aim to better understand attention over nodes in graph neural networks (GNNs) and identify factors influencing its effectiveness.
Ranked #25 on
Graph Classification
on D&D
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data.
Ranked #12 on
Graph Classification
on NCI109
Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video
In this paper we describe a solution to our entry for the emotion recognition challenge EmotiW 2017.
Recursive Autoconvolution for Unsupervised Learning of Convolutional Neural Networks
In visual recognition tasks, such as image classification, unsupervised learning exploits cheap unlabeled data and can help to solve these tasks more efficiently.
