Graph Edit Distance Computation via Graph Neural Networks Machine Learning

Graph similarity search is among the most important graph-based applications, e.g. finding the chemical compounds that are most similar to a query compound. Graph similarity/distance computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other applications, which is usually very costly to compute. Inspired by the recent success of neural network approaches to several graph applications, such as node classification and graph classification, we propose a novel neural network-based approach to address this challenging while classical graph problem, with the hope to alleviate the computational burden while preserving a good performance. Our model generalizes to unseen graphs, and in the worst case runs in linear time with respect to the number of nodes in two graphs. Taking GED computation as an example, experimental results on three real graph datasets demonstrate the effectiveness and efficiency of our approach. Specifically, our model achieves smaller error and great time reduction compared against several approximate algorithms on GED computation. To the best of our knowledge, we are among the first to adopt neural networks to model the similarity between two graphs, and provide a new direction for future research on graph similarity computation and graph similarity search.

RLgraph: Flexible Computation Graphs for Deep Reinforcement Learning Artificial Intelligence

Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communication patterns. We argue for the separation of logical component composition, backend graph definition, and distributed execution. To this end, we introduce RLgraph, a library for designing and executing high performance RL computation graphs in both static graph and define-by-run paradigms. The resulting implementations yield high performance across different deep learning frameworks and distributed backends.

TensorFlow Fold: Deep Learning With Dynamic Computation Graphs - ADR Toolbox


In much of machine learning, data used for training and inference undergoes a preprocessing step, where multiple inputs (such as images) are scaled to the same dimensions and stacked into batches. This lets high-performance deep learning libraries like TensorFlow run the same computation graph across all the inputs in the batch in parallel. Batching exploits the SIMD capabilities of modern GPUs and multi-core CPUs to speed up execution. However, there are many problem domains where the size and structure of the input data varies, such as parse trees in natural language understanding, abstract syntax trees in source code, DOM trees for web pages and more. In these cases, the different inputs have different computation graphs that don't naturally batch together, resulting in poor processor, memory, and cache utilization.

Fitting larger networks into memory. – Yaroslav Bulatov – Medium


TLDR; we release the python/Tensorflow package openai/gradient-checkpointing, that lets you fit 10x larger neural nets into memory at the cost of an additional 20% computation time.

Fitting larger networks into memory. – TensorFlow – Medium


TLDR; we release the python/Tensorflow package openai/gradient-checkpointing, that lets you fit 10x larger neural nets into memory at the cost of an additional 20% computation time. GPU memory is often the limiting factor for modern neural network architectures. Memory requirement to train a neural network increases linearly with both network depth and batch-size. You want to go deeper for standard reasons, but also to increase the batch-size to make use of second order methods like KFAC. Such methods need fewer examples to learn compared to mini-batch SGD.