Tootfinder

Quantum Circuit $C^*$-algebra Net
Yuka Hashimoto, Ryuichiro Hataya
https://arxiv.org/abs/2404.06218 https://arxiv.org/pdf/2404.06218<…

Quantum Circuit $C^*$-algebra Net
This paper introduces quantum circuit $C^*$-algebra net, which provides a connection between $C^*$-algebra nets proposed in classical machine learning and quantum circuits. Using $C^*$-algebra, a generalization of the space of complex numbers, we can represent quantum gates as weight parameters of a neural network. By introducing additional parameters, we can induce interaction among multiple circuits constructed by quantum gates. This interaction enables the circuits to share information among…

This https://arxiv.org/abs/2403.03309 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data
Visual understanding and segmentation of materials and their states is fundamental for understanding the physical world. The infinite textures, shapes and often blurry boundaries formed by material make this task particularly hard to generalize. Whether it's identifying wet regions of a surface, minerals in rocks, infected regions in plants, or pollution in water, each material state has its own unique form. For neural nets to learn class-agnostic materials segmentation it is necessary to first…

Artificial Neural Nets are becoming Rorschach tests for researchers: everyone gets to project the latest trillion-parameter model into their preferred low-dimensional blabberspace.

There is more than LLMs, transformers and generative AI. Sometimes the discussion about AI seems to be very narrow. Don't forget there are many other machine learning technologies available like neural nets, decision trees, SVM etc. I especially like Lecun's V-JEPA as far as i can understand it.
Personally i think the way ahead lies in combining various approaches, which is already happening i think.

Breaching the Bottleneck: Evolutionary Transition from Reward-Driven Learning to Reward-Agnostic Domain-Adapted Learning in Neuromodulated Neural Nets
Solvi Arnold, Reiji Suzuki, Takaya Arita, Kimitoshi Yamazaki
https://arxiv.org/abs/2404.12631

Breaching the Bottleneck: Evolutionary Transition from Reward-Driven Learning to Reward-Agnostic Domain-Adapted Learning in Neuromodulated Neural Nets
Advanced biological intelligence learns efficiently from an information-rich stream of stimulus information, even when feedback on behaviour quality is sparse or absent. Such learning exploits implicit assumptions about task domains. We refer to such learning as Domain-Adapted Learning (DAL). In contrast, AI learning algorithms rely on explicit externally provided measures of behaviour quality to acquire fit behaviour. This imposes an information bottleneck that precludes learning from diverse …