Tootfinder

"Christopher Bishop’s 2006 book “Pattern Recognition and Machine Learning,” arguably one of the triggers of the current popularity of machine learning, is quite literally a book about applied mathematics, diving into probabilities, linear algebra, neural networks, Markov models, and combinatorics. And rightfully so; if your objective is to find a job as an engineer at OpenAI, knowing a thing or two about eigenvalues and eigenvectors is definitely going to be useful."

In Praise Of Mathematics
How much math knowledge does a programmer need to know to fulfill their job? How deep should the mathematical knowledge of full-stack engineers, computer security experts, or large language model engineers be? The spectrum of possible answers to these questions is too large to be useful. Perhaps the eternal answer "it depends" is enough?

"AI in the guise of Machine Learning, Deep Learning, GenerativeAI (GenAI), or Large Language Models (LLMs)... can be very useful in certain application areas such as recognising or generating patterns in large data sets. However, their key drawback is that any correctness arguments will be inherently probabilistic as they are usually based on unknown data distributions and are therefore susceptible to errors (sometimes termed “hallucinations”). "

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- CLAReSNet: When Convolution Meets Latent Attention for Hyperspectral Image Classification
Asmit Bandyopadhyay, Anindita Das Bhattacharjee, Rakesh Das
https://arxiv.org/abs/2511.12346 https://mastoxiv.page/@arXiv_csCV_bot/115570753208147835
- Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without ...
Dimitris Oikonomou, Nicolas Loizou
https://arxiv.org/abs/2512.02342 https://mastoxiv.page/@arXiv_mathOC_bot/115654870924418771
- Predictive Modeling of I/O Performance for Machine Learning Training Pipelines: A Data-Driven App...
Karthik Prabhakar, Durgamadhab Mishra
https://arxiv.org/abs/2512.06699 https://mastoxiv.page/@arXiv_csPF_bot/115688618582182232
- Minimum Bayes Risk Decoding for Error Span Detection in Reference-Free Automatic Machine Translat...
Lyu, Song, Kamigaito, Ding, Tanaka, Utiyama, Funakoshi, Okumura
https://arxiv.org/abs/2512.07540 https://mastoxiv.page/@arXiv_csCL_bot/115689532163491162
- In-Context Learning for Seismic Data Processing
Fabian Fuchs, Mario Ruben Fernandez, Norman Ettrich, Janis Keuper
https://arxiv.org/abs/2512.11575 https://mastoxiv.page/@arXiv_csCV_bot/115723040285820239
- Journey Before Destination: On the importance of Visual Faithfulness in Slow Thinking
Rheeya Uppaal, Phu Mon Htut, Min Bai, Nikolaos Pappas, Zheng Qi, Sandesh Swamy
https://arxiv.org/abs/2512.12218 https://mastoxiv.page/@arXiv_csCV_bot/115729165330908574
- Non-Resolution Reasoning (NRR): A Computational Framework for Contextual Identity and Ambiguity P...
Kei Saito
https://arxiv.org/abs/2512.13478 https://mastoxiv.page/@arXiv_csCL_bot/115729234145554554
- Stylized Synthetic Augmentation further improves Corruption Robustness
Georg Siedel, Rojan Regmi, Abhirami Anand, Weijia Shao, Silvia Vock, Andrey Morozov
https://arxiv.org/abs/2512.15675 https://mastoxiv.page/@arXiv_csCV_bot/115740141862163631
- mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs
Jonas Pai, Liam Achenbach, Victoriano Montesinos, Benedek Forrai, Oier Mees, Elvis Nava
https://arxiv.org/abs/2512.15692 https://mastoxiv.page/@arXiv_csRO_bot/115739947869830764
toXiv_bot_toot

ChatGPT violated copyright laws, German court rules.
Are we surprised?
https://www.theguardian.com/technology/2025/nov/11/chatgpt-violated-copyright-laws-german-court-rules

Replaced article(s) found for q-bio.MN. https://arxiv.org/list/q-bio.MN/new
[1/1]:
- Markovian Promoter Models: A Mechanistic Alternative to Hill Functions in Gene Regulatory Networks
Tianyu Wu