#params — Public Fediverse posts

MK-UNet: Multi-kernel Lightweight CNN for Medical Image Segmentation

Md Mostafijur Rahman, Radu Marculescu
https://arxiv.org/abs/2509.18493 https://arxiv.org/pdf/2509.18493 https://arxiv.org/html/2509.18493

arXiv:2509.18493v1 Announce Type: new
Abstract: In this paper, we introduce MK-UNet, a paradigm shift towards ultra-lightweight, multi-kernel U-shaped CNNs tailored for medical image segmentation. Central to MK-UNet is the multi-kernel depth-wise convolution block (MKDC) we design to adeptly process images through multiple kernels, while capturing complex multi-resolution spatial relationships. MK-UNet also emphasizes the images salient features through sophisticated attention mechanisms, including channel, spatial, and grouped gated attention. Our MK-UNet network, with a modest computational footprint of only 0.316M parameters and 0.314G FLOPs, represents not only a remarkably lightweight, but also significantly improved segmentation solution that provides higher accuracy over state-of-the-art (SOTA) methods across six binary medical imaging benchmarks. Specifically, MK-UNet outperforms TransUNet in DICE score with nearly 333$\times$ and 123$\times$ fewer parameters and FLOPs, respectively. Similarly, when compared against UNeXt, MK-UNet exhibits superior segmentation performance, improving the DICE score up to 6.7% margins while operating with 4.7$\times$ fewer #Params. Our MK-UNet also outperforms other recent lightweight networks, such as MedT, CMUNeXt, EGE-UNet, and Rolling-UNet, with much lower computational resources. This leap in performance, coupled with drastic computational gains, positions MK-UNet as an unparalleled solution for real-time, high-fidelity medical diagnostics in resource-limited settings, such as point-of-care devices. Our implementation is available at https://github.com/SLDGroup/MK-UNet.

toXiv_bot_toot

Knowledge-aware Evolutionary Graph Neural Architecture Search

Chao Wang, Jiaxuan Zhao, Lingling Li, Licheng Jiao, Fang Liu, Xu Liu, Shuyuan Yang
https://arxiv.org/abs/2411.17339 https://arxiv.org/pdf/2411.17339 https://arxiv.org/html/2411.17339

arXiv:2411.17339v1 Announce Type: new
Abstract: Graph neural architecture search (GNAS) can customize high-performance graph neural network architectures for specific graph tasks or datasets. However, existing GNAS methods begin searching for architectures from a zero-knowledge state, ignoring the prior knowledge that may improve the search efficiency. The available knowledge base (e.g. NAS-Bench-Graph) contains many rich architectures and their multiple performance metrics, such as the accuracy (#Acc) and number of parameters (#Params). This study proposes exploiting such prior knowledge to accelerate the multi-objective evolutionary search on a new graph dataset, named knowledge-aware evolutionary GNAS (KEGNAS). KEGNAS employs the knowledge base to train a knowledge model and a deep multi-output Gaussian process (DMOGP) in one go, which generates and evaluates transfer architectures in only a few GPU seconds. The knowledge model first establishes a dataset-to-architecture mapping, which can quickly generate candidate transfer architectures for a new dataset. Subsequently, the DMOGP with architecture and dataset encodings is designed to predict multiple performance metrics for candidate transfer architectures on the new dataset. According to the predicted metrics, non-dominated candidate transfer architectures are selected to warm-start the multi-objective evolutionary algorithm for optimizing the #Acc and #Params on a new dataset. Empirical studies on NAS-Bench-Graph and five real-world datasets show that KEGNAS swiftly generates top-performance architectures, achieving 4.27% higher accuracy than advanced evolutionary baselines and 11.54% higher accuracy than advanced differentiable baselines. In addition, ablation studies demonstrate that the use of prior knowledge significantly improves the search performance.

TODAY (March 31) at 9:00 PM CET my generative token "nd-genuary32nd-plants-chatgpt3-001" will be released on #fxhash.

The foundation was built by using #ChatGPT and this is the result of me transforming that code into a NFT.

https://www.fxhash.xyz/generative/slug/nd-genuary32nd-plants-chatgpt3-001

#generativeart #genart #nd #chatgpt3 #plants #flower #sun #midnight #nerddisco #agpl #opensource #aiinfluenced #canvas2d #params #javascript #generative #nft #crypto #tezos #blockchain #genart #art #mastoart

TODAY (March 31) at 9:00 PM CET my generative token "nd-genuary32nd-plants-chatgpt3-001" will be released on #fxhash.

The foundation was built by using #ChatGPT and this is the result of me transforming that code into a NFT.

https://www.fxhash.xyz/generative/slug/nd-genuary32nd-plants-chatgpt3-001

#generativeart #genart #nd #chatgpt3 #plants #flower #sun #midnight #nerddisco #agpl #opensource #aiinfluenced #canvas2d #params #javascript #generative #nft #crypto #tezos #blockchain #genart #art #mastoart

TODAY (March 31) at 9:00 PM CET my generative token "nd-genuary32nd-plants-chatgpt3-001" will be released on #fxhash.

The foundation was built by using #ChatGPT and this is the result of me transforming that code into a NFT.

https://www.fxhash.xyz/generative/slug/nd-genuary32nd-plants-chatgpt3-001

#generativeart #genart #nd #chatgpt3 #plants #flower #sun #midnight #nerddisco #agpl #opensource #aiinfluenced #canvas2d #params #javascript #generative #nft #crypto #tezos #blockchain #genart #art #mastoart

TODAY (March 31) at 9:00 PM CET my generative token "nd-genuary32nd-plants-chatgpt3-001" will be released on #fxhash.

The foundation was built by using #ChatGPT and this is the result of me transforming that code into a NFT.

https://www.fxhash.xyz/generative/slug/nd-genuary32nd-plants-chatgpt3-001

#generativeart #genart #nd #chatgpt3 #plants #flower #sun #midnight #nerddisco #agpl #opensource #aiinfluenced #canvas2d #params #javascript #generative #nft #crypto #tezos #blockchain #genart #art #mastoart

TODAY (March 31) at 9:00 PM CET my generative token "nd-genuary32nd-plants-chatgpt3-001" will be released on #fxhash.

The foundation was built by using #ChatGPT and this is the result of me transforming that code into a NFT.

https://www.fxhash.xyz/generative/slug/nd-genuary32nd-plants-chatgpt3-001

#generativeart #genart #nd #chatgpt3 #plants #flower #sun #midnight #nerddisco #agpl #opensource #aiinfluenced #canvas2d #params #javascript #generative #nft #crypto #tezos #blockchain #genart #art #mastoart

The NFT itself will be released on March 31 at 9:00 PM CET on
#fxhash and it’s limited to 32 editions because of #genuary32nd. It's also using #params, so make sure to check it out 🙏

https://www.fxhash.xyz/generative/slug/nd-genuary32nd-plants-chatgpt3-001

25% will go to #TezQuakeAid

I'm also curious how people are using dry-validation/dry-schema/dry-struct to handle sinatra params from forms or sidekiq job arguments. Do people use the dry-* libraries directly, or do they use one of the other plugin libraries such as sinatra-validation, sinatra-dry_params, or sidekiq-dry?
https://github.com/IzumiSy/sinatra-validation
https://github.com/tiev/sinatra-dry_param
https://github.com/zorbash/sidekiq-dry
#dryrb #sinatra #sidekiq #params

📝 ZiCo: Zero-Shot NAS via Inverse Coefficient of Variation on Gradients 🧠

"ZiCo is the first training-free proxy that works consistently better than the number of network parameters (#Params), the previous SOTA training-free proxy." [gal30b+] 🤖 #LG

🔗 https://arxiv.org/abs/2301.11300v1 #arxiv

Today I learned about URLSearchParams: A nice way to handle url parameters with vanilla javascript!

https://developer.mozilla.org/en-US/docs/Web/API/URLSearchParams

#javascript #url #params #mdn #mozilla #nativeApisBestApis