Memory-Based Graph Networks | AISC

Channel:
Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
Subscribers:
22,300
Published on ● Video Link: https://www.youtube.com/watch?v=hYEPT9FlgR0


Duration: 57:52
647 views
15

Speaker(s): Amir Hosein Khasahmadi
Facilitator(s): Nabila Abraham

Find the recording, slides, and more info at https://ai.science/e/mem-gn-ns-memory-based-graph-networks--WDHBObBp3ILDK6dV0B9U

Motivation / Abstract
Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs. The authors propose an efficient memory layer for GNNs that can jointly learn node representations and but moreover, coarsen the graph representations which methods like GraphSAGE or GCN don't necessarily do. The authors introduce two new networks based on this layer: memory-based GNN (MemGNN) and graph memory network (GMN) that can learn hierarchical graph representations. The experimental results show that the proposed models achieve state-of-the-art results in eight out of nine graph classification and regression benchmarks. We also show that the learned representations could correspond to chemical features in the molecule data.

What was discussed?
- Relationship between GMN/MemGNNs and transformers
- Clustering loss function
- Interpreting the centroids learned
- Global topological embedding versus local topologies

------
#AISC hosts 3-5 live sessions like this on various AI research, engineering, and product topics every week! Visit https://ai.science for more details



Other Videos By LLMs Explained - Aggregate Intellect - AI.SCIENCE


2020-07-08Compact Neural Representation Using Attentive Network Pruning | AISC
2020-07-08Navigating the Idea Maze: Continuous discovery frameworks for (AI?) products | AISC
2020-07-02Nvidia's RAPIDS.ai: Massively Accelerated Modern Data-Science | AISC
2020-07-02Building AI Products; The Journey | Overview
2020-07-01Building your Product Strategy - A Guide | AISC
2020-06-30Conceptual understanding through efficient inverse-design of quantum optical experiments | AISC
2020-06-30An A-Z primer on the AI Product Lifecycle | AISC
2020-06-29Reducing Gender Bias in Google Translate | Summary and Takeaways | AISC
2020-06-29Reducing Gender Bias in Google Translate | Melvin Johnson | AISC Algorithmic Inclusion
2020-06-26A machine learning and informatics program package for modeling of chemical and materials data | AIS
2020-06-25Memory-Based Graph Networks | AISC
2020-06-25Paper Explained : PEGASUS, a SOTA abstractive summarization model by Google | AISC
2020-06-24Towards Amortized Ranking-Critical Training for Collaborative Filtering | AISC
2020-06-24Paper review - Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey | AISC
2020-06-23Decoding our thoughts: Tracking the contents of (non)-conscious working memory | AISC
2020-06-23[XAI] Explainable AI in Retail | AISC
2020-06-22Why you should be part of AISC community!
2020-06-18Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning | AISC
2020-06-17GrowNet: Gradient Boosting Neural Networks | AISC
2020-06-17LogoGAN: Creating Logos with Generative Adversarial Networks | Workshop Capstone
2020-06-11Meta-Graph: Few-Shot Link Prediction Using Meta-Learning | AISC