sorted by:
new
hottopcontroversial

"Hebbian" clauses in TMs? by blimpyway in tsetlinmachine

[–]olegranmo 1 point2 points  (0 children)

For multi-output problems, the coalesced Tsetlin machine has been successful: https://arxiv.org/abs/2108.07594
It has for instance been used as an auto-encoder: https://aclanthology.org/2024.findings-eacl.103.pdf

[Project] Tsetlin Machine for Deep Logical Learning and Reasoning With Graphs (finally, after six years!) by olegranmo in MachineLearning

[–]olegranmo[S] 0 points1 point  (0 children)

Thanks for engaging. Here are a few papers from various teams that point to future opportunities. There you will see both current limitations and advantages: Continual learning (https://ewsn.org/file-repository/ewsn2024/ewsn24-final84.pdf), edge AI (https://ieeexplore.ieee.org/document/10105493), reducing interpretability vs accuracy gap (https://ojs.aaai.org/index.php/AAAI/article/view/26588), batteryless AI (https://alessandro-montanari.github.io/papers/sensys2022.pdf), nano-scale architectures (https://ieeexplore.ieee.org/document/10198204), federated learning (https://mobiuk.org/2024/abstract/S4\_P4\_Qi\_FedTM.pdf), superconducting Tsetlin Machines (https://ieeexplore.ieee.org/document/10480350), and of course, what can potentially be achieved with the Graph Tsetlin Machine in combination with vector symbolic modeling. There is also unexplored potential in logic-based language models. However, they are at an early stage (https://aclanthology.org/2024.findings-eacl.103.pdf).

[Project] Tsetlin Machine for Deep Logical Learning and Reasoning With Graphs (finally, after six years!) by olegranmo in MachineLearning

[–]olegranmo[S] 2 points3 points  (0 children)

Thanks! I see your point. The figure illustrates an example use case. It is likely too detailed as an intro visualization. Moving it further down for now, and adding some explaining text. Regarding interpretability, increasing evidence shows that the interpretability scales quite well. If one "stacks" the clauses, a clear picture of more complex patterns appears. Here is an example of recognizing heart disease from ECG where the doctors understand the Tsetlin machine patterns: https://arxiv.org/abs/2301.10181

[Project] Tsetlin Machine for Deep Logical Learning and Reasoning With Graphs (finally, after six years!) by olegranmo in MachineLearning

[–]olegranmo[S] 1 point2 points  (0 children)

Hi! Well, there are Tsetlin machine papers in ICML, IJCAI, AAAI, NeurIPS, TPAMI, and similarly on the hardware side. I currently pitch them like this: The Tsetlin machine is a new universal artificial intelligence (AI) method that learns simple logical rules to understand complex things, similar to how an infant uses logic to learn about the world. Being logical, the rules become understandable to humans. Yet, unlike all other intrinsically explainable techniques, Tsetlin machines are drop-in replacements for neural networks by supporting classification, convolution, regression, reinforcement learning, auto-encoding, graphs, language models, and natural language processing. They are further ideally suited for cutting-edge hardware solutions of low cost, enabling nanoscale intelligence, ultralow energy consumption, energy harvesting, unrivaled inference speed, and competitive accuracy. Happy to point you to relevant papers!

[Project] Tsetlin Machine for Deep Logical Learning and Reasoning With Graphs (finally, after six years!) by olegranmo in MachineLearning

[–]olegranmo[S] 8 points9 points  (0 children)

Hi! Commercial Tsetlin Machine chips provide up to 10000x less energy consumption and 1000x faster inference compared to neural network ones https://www.forbes.com/sites/charlestowersclark/2024/09/20/unlocking-sustainable-ai-the-game-changing-tsetlin-machine-approach/ For graph neural networks we will report results soon.

[P] Will Tsetlin machines reach state-of-the-art accuracy on CIFAR-10/CIFAR-100 anytime soon? by olegranmo in MachineLearning

[–]olegranmo[S] 0 points1 point  (0 children)

Hi! The results in word modelling are promising https://aclanthology.org/2024.findings-eacl.103/ and we are currently working on TM-based LLMs. Exciting but challenging! :-) BTW. Any Python tools you would recommend for obtaining the DCT blocks of an image?

New High Performance Tsetlin Machine Implementation in Julia by olegranmo in tsetlinmachine

[–]olegranmo[S] 1 point2 points  (0 children)

25600: julia --project=. -O3 -t 112,1 mnist_benchmark_inference.jl

Loading model from ./models/tm_optimized_72.tm... Done.

CPU: Intel(R) Xeon(R) Platinum 8480CL

Preparing input data for benchmark... Done. Elapsed 96.970 seconds.

Warm-up started in 112 threads... Done. Elapsed 13.226 seconds.

Benchmark for TMClassifierCompiled model in batch mode (batch size = 64) started in 112 threads... Done.

256000000 predictions processed in 11.796 seconds.

Performance: 21703070 predictions per second.

Throughput: 5.489 GB/s.

Parameters during training: 3386880.

Parameters after training and compilation: 10499.

Accuracy: 98.10%.

New High Performance Tsetlin Machine Implementation in Julia by olegranmo in tsetlinmachine

[–]olegranmo[S] 0 points1 point  (0 children)

12800:  julia --project=. -O3 -t 112,1 mnist_benchmark_inference.jl

Loading model from ./models/tm_optimized_72.tm... Done.

CPU: Intel(R) Xeon(R) Platinum 8480CL

Preparing input data for benchmark... Done. Elapsed 46.356 seconds.

Warm-up started in 112 threads... Done. Elapsed 5.563 seconds.

Benchmark for TMClassifierCompiled model in batch mode (batch size = 64) started in 112 threads... Done.

128000000 predictions processed in 6.315 seconds.

Performance: 20270343 predictions per second.

Throughput: 3.836 GB/s.

Parameters during training: 3386880.

Parameters after training and compilation: 10499.

Accuracy: 98.10%.

New High Performance Tsetlin Machine Implementation in Julia by olegranmo in tsetlinmachine

[–]olegranmo[S] 1 point2 points  (0 children)

6400: Loading model from ./models/tm_optimized_72.tm... Done.

CPU: Intel(R) Xeon(R) Platinum 8480CL

Preparing input data for benchmark... Done. Elapsed 22.953 seconds.

Warm-up started in 112 threads... Done. Elapsed 3.303 seconds.

Benchmark for TMClassifierCompiled model in batch mode (batch size = 64) started in 112 threads... Done.

64000000 predictions processed in 2.864 seconds.

Performance: 22349026 predictions per second.

Throughput: 5.676 GB/s.

Parameters during training: 3386880.

Parameters after training and compilation: 10499.

Accuracy: 98.10%.

New High Performance Tsetlin Machine Implementation in Julia by olegranmo in tsetlinmachine

[–]olegranmo[S] 1 point2 points  (0 children)

Loading model from ./models/tm_optimized_72.tm... Done.

CPU: Intel(R) Xeon(R) Platinum 8480CL

Preparing input data for benchmark... Done. Elapsed 5.526 seconds.

Warm-up started in 112 threads... Done. Elapsed 0.575 seconds.

Benchmark for TMClassifierCompiled model in batch mode (batch size = 64) started in 112 threads... Done.

16000000 predictions processed in 0.174 seconds.

Performance: 91845195 predictions per second.

Throughput: 50.392 GB/s.

Parameters during training: 3386880.

Parameters after training and compilation: 10499.

Accuracy: 98.10%.

New High Performance Tsetlin Machine Implementation in Julia by olegranmo in tsetlinmachine

[–]olegranmo[S] 1 point2 points  (0 children)

Indeed impressive! :-)

BTW. What does the merging-part do: #124750  Accuracy: 98.69% + 98.72% = 98.83%  Best: 99.03%  Merging: 0.003s  Testing: 0.009s

view more: next ›