Google DeepMind has introduced a new hybrid architecture called TransNAR, combining Transformer model with a graph neural network-based neural algorithmic reasoner. The model shows improvements in out-of-distribution reasoning for algorithmic tasks.
Neural Algorithmic Reasoning for Transformers: The TransNAR Framework https://t.co/DSWhJOvaEO #TransNAR #AlgorithmicReasoning #AI #DeepLearning #AISolutions #ai #news #llm #ml #research #ainews #innovation #artificialintelligence #machinelearning #technology #deeplearning @v… https://t.co/rAQImSP7Rc
This new paper from @GoogleDeepMind claims Neural algorithmic reasoners (NARs) can hold perfect generalization even on 6X larger inputs than ones seen in the training set, for highly complex algorithmic tasks with long rollouts. 🤯 Paper - Transformers meet Neural Algorithmic… https://t.co/MluYL2xcVD
[CL] Transformers meet Neural Algorithmic Reasoners W Bounsi, B Ibarz, A Dudzik, J B. Hamrick… [Google DeepMind] (2024) https://t.co/unw0cTnNx9 - The paper proposes a hybrid architecture that combines a Transformer model with a pre-trained graph neural network-based neural… https://t.co/ARDwTbAmkC
Google DeepMind presents a new hybrid architecture which enables tokens in the LLM to cross-attend to node embeddings from a GNN-based neural algorithmic reasoner (NAR). The resulting model, called TransNAR, demonstrates improvements in OOD reasoning across algorithmic tasks.… https://t.co/q3tcRJuN2L
Google presents Transformers meet Neural Algorithmic Reasoners Significant gains over Transformer for algorithmic reasoning, both in and out of distribution https://t.co/O8fyc42rVs https://t.co/clCRGmOosv
Transformers meet Neural Algorithmic Reasoners Transformers have revolutionized machine learning with their simple yet effective architecture. Pre-training Transformers on massive text datasets from the Internet has led to unmatched generalization for natural language https://t.co/bTWb87o5hO