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Found 21 papers, 16 papers with code
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
TpuGraphs provides 25x more graphs than the largest graph property prediction dataset (with comparable graph sizes), and 770x larger graphs on average compared to existing performance prediction datasets on machine learning programs.
UGSL: A Unified Framework for Benchmarking Graph Structure Learning
We implement a wide range of existing models in our framework and conduct extensive analyses of the effectiveness of different components in the framework.
Learning Large Graph Property Prediction via Graph Segment Training
Here we propose Graph Segment Training (GST), a general framework that utilizes a divide-and-conquer approach to allow learning large graph property prediction with a constant memory footprint.
TF-GNN: Graph Neural Networks in TensorFlow
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow.
Implicit SVD for Graph Representation Learning
Recent improvements in the performance of state-of-the-art (SOTA) methods for Graph Representational Learning (GRL) have come at the cost of significant computational resource requirements for training, e. g., for calculating gradients via backprop over many data epochs.
Fast Graph Learning with Unique Optimal Solutions
We consider two popular Graph Representation Learning (GRL) methods: message passing for node classification and network embedding for link prediction.
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning
We propose Graph Traversal via Tensor Functionals(GTTF), a unifying meta-algorithm framework for easing the implementation of diverse graph algorithms and enabling transparent and efficient scaling to large graphs.
Zero-shot Synthesis with Group-Supervised Learning
Visual cognition of primates is superior to that of artificial neural networks in its ability to 'envision' a visual object, even a newly-introduced one, in different attributes including pose, position, color, texture, etc.
End-to-end Learning of Compressible Features
We propose a learned method that jointly optimizes for compressibility along with the task objective for learning the features.
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
The second, graph regularized neural networks, leverages graphs to augment neural network losses with a regularization objective for semi-supervised learning.
Meta Adaptation using Importance Weighted Demonstrations
Imitation learning has gained immense popularity because of its high sample-efficiency.
Human Languages in Source Code: Auto-Translation for Localized Instruction
The study is to the best of our knowledge the first on human-language in code and covers 2. 9 million Java repositories.
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships.
Identifying and Analyzing Cryptocurrency Manipulations in Social Media
Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scammers who wish to spread false hype about the cryptocurrency they are trying to pump.
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification
Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data.
Ranked #43 on
Node Classification
on Pubmed
Network of Graph Convolutional Networks Trained on Random Walks
Graph Convolutional Networks (GCNs) are a recently proposed architecture which has had success in semi-supervised learning on graph-structured data.
Watch Your Step: Learning Node Embeddings via Graph Attention
Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e. g. by sampling random walks).
Ranked #69 on
Node Classification
on Citeseer
Proportionate gradient updates with PercentDelta
In particular, at every batch, we want to update all trainable tensors, such that the relative change of the L1-norm of the tensors is the same, across all layers of the network, throughout training time.
Learning Edge Representations via Low-Rank Asymmetric Projections
Individually, both of these contributions improve the learned representations, especially when there are memory constraints on the total size of the embeddings.
YouTube-8M: A Large-Scale Video Classification Benchmark
Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow.
Ranked #1 on
Action Recognition In Videos
on ActivityNet
Detecting events and key actors in multi-person videos
In this paper, we propose a model which learns to detect events in such videos while automatically "attending" to the people responsible for the event.
