ARC Centre of Excellence for Plant Success in Nature and Agriculture’s Post

Check out my third paper 'n-dependent continuous theories and hyperdefinable sets' on arXiv! ¡Mi tercer artículo "n-dependent continuous theories and hyperdefinable sets" ya está en arXiv!

Yesterday, I discussed a recent paper with my team regarding semantic image inversion and editing using stochastic rectified differential equations. Today, the open-source Comfy community has implemented this approach within a day. The paper "Semantic Image Inversion and Editing using Stochastic Rectified Differential Equations" proposes a novel method for inverting and editing real images using stochastic equivalents of rectified flow models like Flux. This approach aims to overcome challenges faced by diffusion models in terms of faithfulness and editability. Example : The transformation of Oppenheimer into an anime character. While not perfect, it showcases the potential of this technique for image manipulation tasks without additional training. Key aspects of this work include: - Zero-shot inversion and editing capabilities - No requirement for additional training or latent variable optimization - Potential improvements in faithfulness and editability compared to diffusion models Kudos to the authors: Litu Rout 👏

I'm glad to announce that my first paper has just been published on preprints.org! In this paper, I derive some results regarding the sum of powers of natural numbers which has been an interesting topic in number theory even since the era of Blaise Pascal. It turns out that we can compute the sum of powers recursively. Furthermore, we can make use of this recursive relation to also compute Bernoulli numbers recursively. To read more on that, you can click the link below 👇🏻 (I have also added it to the publication section of my profile) https://lnkd.in/gGMQVsvj In fact, the manuscript was originally written back when I was 15, but there's a generalization that i added recently before i submitted it. I hope to submit the paper to a journal soon if possible.

✒️ RECALL this paper📄 use NEAT to build a population of networks optimized for mulitclass classification problems you can review NEAT paper summary https://lnkd.in/daEVeep2 Why NEAT was advantage ? ✅ The ability to evolve the structure of the network remains a major benefit of evolutionary approaches over optimization techniques such as backpropagation ✨ In this paper, NEAT is evaluated in several multiclass classification problems, and then extended via two ensemble approaches: One-vs-All and One-vs-One ✨These approaches decompose multiclass classification problems into a set of binary classification problems ✨ in which each binary problem is solved by an instance of NEAT. ✨These ensemble models exhibit reduced variance and increasingly superior accuracy as the number of classes increases. 🔎 Read more in this summary of this interseting paper 📄

[New preprint] Joint work with Sourav Dutta and Prabha Mandayam. Practical significance: A family of codes with low resource overhead that outperform existing codes in the literature. Theoretical significance: The codes developed satisfy a relaxed version of the Knill-Laflamme conditions, which we outline, and this version has the potential to be used in designing optimal codes for other noise processes. Comments welcome.

Excited to share our latest blog post on solving nonlinear optimization problems with fuzzy relational equations (FRE). In this paper, we introduce a two-phase-ACO algorithm, called FRE-ACO, designed to tackle the computational complexity associated with such problems. The algorithm combines the discrete ant colony optimization algorithm (ACO) and the continuous ant colony optimization algorithm (ACOR) to efficiently handle non-convex regions while preserving solution feasibility. Our results demonstrate higher convergence rates and fewer function evaluations compared to other algorithms. Check out the full article here: https://bit.ly/3Kope7H.

📢Our paper got accepted 😄 CORE-ReID is ranked 1st on "Papers with code" in Unsupervised Domain Adaptation for Re-Identification. Benchmark: https://lnkd.in/gNCmr3P7 Paper: https://lnkd.in/gFtRX7KS