On the Turing Completeness of Modern Neural Network Architectures

10 January 2019

Papers citing "On the Turing Completeness of Modern Neural Network Architectures"

30 / 30 papers shown

Title
Can Large Language Models Learn Formal Logic? A Data-Driven Training and Evaluation Framework Yuan Xia Akanksha Atrey Fadoua Khmaissia Kedar S. Namjoshi LRM ELM 45 0 0 28 Apr 2025
Looped ReLU MLPs May Be All You Need as Practical Programmable Computers Yingyu Liang Zhizhou Sha Zhenmei Shi Zhao-quan Song Yufa Zhou 96 18 0 21 Feb 2025
Lower Bounds for Chain-of-Thought Reasoning in Hard-Attention Transformers Alireza Amiri Xinting Huang Mark Rofin Michael Hahn LRM 180 0 0 04 Feb 2025
Are Transformers Able to Reason by Connecting Separated Knowledge in Training Data? Yutong Yin Zhaoran Wang LRM ReLM 143 0 0 27 Jan 2025
Can Transformers Reason Logically? A Study in SAT Solving Leyan Pan Vijay Ganesh Jacob Abernethy Chris Esposo Wenke Lee ReLM LRM 33 0 0 09 Oct 2024
On the Complexity of Neural Computation in Superposition Micah Adler Nir Shavit 115 3 0 05 Sep 2024
LifeGPT: Topology-Agnostic Generative Pretrained Transformer Model for Cellular Automata Jaime Berkovich Markus J. Buehler AI4CE 30 2 0 03 Sep 2024
Representing Rule-based Chatbots with Transformers Dan Friedman Abhishek Panigrahi Danqi Chen 63 1 0 15 Jul 2024
Separations in the Representational Capabilities of Transformers and Recurrent Architectures S. Bhattamishra Michael Hahn Phil Blunsom Varun Kanade GNN 41 9 0 13 Jun 2024
Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory Nikola Zubić Federico Soldá Aurelio Sulser Davide Scaramuzza LRM BDL 52 5 0 26 May 2024
Chain of Thought Empowers Transformers to Solve Inherently Serial Problems Zhiyuan Li Hong Liu Denny Zhou Tengyu Ma LRM AI4CE 28 96 0 20 Feb 2024
Sample, estimate, aggregate: A recipe for causal discovery foundation models Menghua Wu Yujia Bao Regina Barzilay Tommi Jaakkola CML 49 7 0 02 Feb 2024
Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining Licong Lin Yu Bai Song Mei OffRL 32 43 0 12 Oct 2023
Self-attention Dual Embedding for Graphs with Heterophily Yurui Lai Taiyan Zhang Rui Fan GNN 35 0 0 28 May 2023
E(n)-equivariant Graph Neural Cellular Automata G. Gala Daniele Grattarola Erik Quaeghebeur GNN 45 3 0 25 Jan 2023
Memory Augmented Large Language Models are Computationally Universal Dale Schuurmans 32 45 0 10 Jan 2023
Attention-based Neural Cellular Automata Mattie Tesfaldet Derek Nowrouzezahrai C. Pal ViT 31 17 0 02 Nov 2022
HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences Siqiao Xue X. Shi James Y. Zhang Hongyuan Mei AI4TS 19 34 0 04 Oct 2022
Provably expressive temporal graph networks Amauri Souza Diego Mesquita Samuel Kaski Vikas K. Garg 89 54 0 29 Sep 2022
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes Shivam Garg Dimitris Tsipras Percy Liang Gregory Valiant 24 449 0 01 Aug 2022
Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity Sophie Hao Dana Angluin Robert Frank 11 71 0 13 Apr 2022
Can Vision Transformers Perform Convolution? Shanda Li Xiangning Chen Di He Cho-Jui Hsieh ViT 46 19 0 02 Nov 2021
Pairing Conceptual Modeling with Machine Learning W. Maass V. Storey HAI 24 33 0 27 Jun 2021
Vector Symbolic Architectures as a Computing Framework for Emerging Hardware Denis Kleyko Mike Davies E. P. Frady P. Kanerva Spencer J. Kent ... Evgeny Osipov J. Rabaey D. Rachkovskij Abbas Rahimi Friedrich T. Sommer 32 57 0 09 Jun 2021
On the Expressive Power of Self-Attention Matrices Valerii Likhosherstov K. Choromanski Adrian Weller 37 33 0 07 Jun 2021
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth Yihe Dong Jean-Baptiste Cordonnier Andreas Loukas 37 373 0 05 Mar 2021
Transformers in Vision: A Survey Salman Khan Muzammal Naseer Munawar Hayat Syed Waqas Zamir F. Khan M. Shah ViT 227 2,430 0 04 Jan 2021
On the Computational Power of Transformers and its Implications in Sequence Modeling S. Bhattamishra Arkil Patel Navin Goyal 25 64 0 16 Jun 2020
How hard is to distinguish graphs with graph neural networks? Andreas Loukas GNN 25 6 0 13 May 2020
It's Not What Machines Can Learn, It's What We Cannot Teach Gal Yehuda Moshe Gabel Assaf Schuster FaML 14 37 0 21 Feb 2020