Warum Vergessen eine Superkraft ist

#Vergessen #Superkraft #Gedächtnis #Neurobiologie #Schlaf #SmartForgetting #InformationOverload #Demenz #Overfitting #KI

Lesen: www.matthiaszehnder.ch/wochenkommen...
Hören: www.buzzsprout.com/1788913/epis...
Sehen: youtu.be/yJgcQVNO-y8

You’ve developed muscle-memory specific to that deal, I’d say #overfitting 😂 1:09 is crazy fast, yes.

Btw, challenge of the day* will be in draw three 😉♥️ Will be posted in 1hr from now.

*: Saturday, but maybe it’ll be Sunday already for you 😐 In which area of Australia do you live, if I may ask?

Abstract: As a preprocessing step of spectroscopic techniques such as Raman spectroscopy, infrared spectroscopy, electrophoresis, etc., the baseline correction is very important for improving the signal quality, thereby ensuring the reliability and accuracy of the data analysis. Methods such as polynomial fitting, wavelet transforms, and frequency-domain filtering are widely used for baseline correction, effectively reducing interference and enhancing the reliability of signal analysis. However, these methods have certain limitations: (i) Polynomial fitting faces challenges in determining the optimal order, which may affect the fitting quality, (ii) wavelet transforms are complex and require fine adjustments, and (iii) frequency-domain filtering may cause signal distortion. These shortcomings affect the implementation of the algorithm in spectral related industries. Therefore, finding an appropriate algorithm to optimize baseline removal is crucial for the development of automated spectral analysis equipment. Here, we propose a rolling ball baseline removal algorithm based on morphological operations. With its simple implementation and excellent baseline removal performance, this method effectively avoids the overfitting problems. It is suitable for baseline correction in not only Raman spectroscopy, but also various other types of spectral data. In all, this approach offers a convenient and efficient general solution for the processing of various spectral data.

New from Applied Spectroscopy!
Morphology-Enhanced Rolling Ball Algorithm for Baseline Removal
Read more: https://doi.org/10.1177/00037028251384654
#SAS #Spectroscopy #Raman #Baseline #Removal #overfitting

AI writing is "bad"... so now what? YouTube video by Mina Le

Mina Le: "AI writing is 'bad'... so now what?" | #ChatBGT #LLM #Overfitting #CognitiveDecline #CognitiveOffloading #OutsourcingThinking #CriticalThinking #ReadingIsEssential #ReadingIsAVice #ReadForPleasure
www.youtube.com/watch?v=ECWp...

Letzteres ist die zentrale Frage in jedem Fach.
— „Ach was!?!“

Bei Mathe Grundschule gibt es dann diese 80 Millionen Mathelehrer:innen … plus „hat mir auch nicht geschadet“.

Wie geht lernen und was ist Basis?
Zwei Fragen, eine Medaille; um noch einen rauszuhauen.

Neues Lieblingswort #overfitting

THE ONTO-LOGISTIC STERILITY REGIME:
Protocolary Sovereignty, The Pharmakon Crisis, and Normative Closure
papers.ssrn.com/sol3/papers....
📦 #LogisticCapitalism ⚙️ #ProtocolarySovereignty ⚕️ #Pharmakon 🏃 #Dromology 🤖 #AlgorithmicGovernmentality 🎯 #Overfitting

Overfitting vs. Underfitting: Making Sense of the Bias-Variance Trade-Off

The best models live in the sweet spot: generalizing well, learning enough, but not too much

Telegram AI Digest
#ai #overfitting #underfitting

Переобучение vs. Недообучение: Понимание компромисса между смещением и дисперсией

Лучшие модели живут в сладком месте: обобщают хорошо, учатся достаточно, но не слишком много

Telegram ИИ Дайджест
#ai #overfitting #underfitting

BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation

Peijia Qin, Ruiyi Zhang, Pengtao Xie

Action editor: Samira Kahou

https://openreview.net/forum?id=v2xCm3VYl4

#adapting #overfitting #adaptation

Diffusion-RainbowPA: Improvements Integrated Preference Alignment for Diffusion-based Text-to-Ima...

Haoyuan Sun, Bin Liang, Bo Xia, Jiaqi Wu, Yifei Zhao, Kai Qin, Yongzhe Chang, Xueqian Wang

Action editor: Ying Nian Wu

https://openreview.net/forum?id=KY0TSY2bx8

#alignment #overfitting

Eine große Fehleinschätzung ist, dass #KünstlicheNeuronaleNetzwerke umso besser werden, je komplexer sie sind und je größer der Datensatz ist, mit dem sie trainiert werden. Die aktuell völlig unterschätzte Problematik von #Overfitting & #Overtraining sind potentielle Treiber des nächsten KI-Winters.

Negotiated Representations Reduce Overfitting in Machine Learning

Researchers introduced a negotiation‑based method that boosts classification accuracy and cuts overfitting on CIFAR‑10, CIFAR‑100 and MNIST. Published Sep 2025. getnews.me/negotiated-representatio... #negotiatedlearning #overfitting #cifar10

Anton Vorobets (@antonvorobets) Backtest overfitting only matters if your foundation is solid. There is a lot of focus on backtest overfitting, because it is an easy mistake to make and requires great attention to detail to avoid. ...

Some important perspectives on backtest overfitting:

substack.com/profile/1707...

#quant #quantsky #finance #markets #python #data #investing #investment #overfitting

#Overfitting is not binary.
It's a spectrum with measurable probability.
Out-of-sample validation beats in-sample perfection.
#Markets change faster than models adapt.
I validate before I celebrate.
I adapt to #uncertainty, not certainty.
I am akerue.

If your model is too good to be true…

It probably is.

#Overfitting #AI #ML #100DaysOfCode

Discover the fundamentals of Ridge Regression, a powerful biased regression technique for handling multicollinearity and overfitting.
#ridgeregression #regularization #multicollinearity #overfitting #biasedregression #basicstatistics #statisticsmcqs #statisticsquiz #itfeature #gmstat #rfaqs

Is Grok 4 Just a Benchmark Winner? xAI's Flagship Model Raises Red Flags, Appears Heavily Overfitted to Score Well

#AI #Grok4 #xAI #AIEthics #Overfitting #AIModels

winbuzzer.com/2025/07/16/i...

If your model performs perfectly on training data:
→ Celebrate
→ Then panic
→ Then add Dropout
→ Then repeat

#Overfitting #AI #Coding #DataScience

Cross Entropy versus Label Smoothing: A Neural Collapse Perspective

Li Guo, George Andriopoulos, Zifan Zhao, Zixuan Dong, Shuyang Ling, Keith W. Ross

Action editor: Qing Qu

https://openreview.net/forum?id=FEo55EIvGI

#overfitting #label #losses

⚠️ Friday AI Fact: Overfitting! 🧠📈

Overfitting happens when a machine learning model learns the training data too well, including the noise and random details, making it perform poorly on new, unseen data.

eloquenceai.eu/ai-dictionary/

#FridayFacts #Overfitting #MachineLearning #ELOQUENCEAI

11/15 Benchmark reliability is questioned! 🤔 Are models overfitting to existing tests? ramesh31 doubts the benchmark numbers for a 7B model. #Benchmarks #Overfitting #LLMs

On Using Certified Training towards Empirical Robustness

Alessandro De Palma, Serge Durand, Zakaria Chihani, François Terrier, Caterina Urban

Action editor: W Ronny Huang

https://openreview.net/forum?id=UaaT2fI9DC

#adversarial #overfitting #robustness

Entia non sunt multiplicanda praeter necessitatem
Occam

#Overfitting #DataScience #MachineLearning

Resolution of misconception of overfitting: Differentiating learning curves from Occam curves Preamble   Occam (Wikipedia) A misconception that overfitted model can be identified with the  amount of  generalisation gap  between model'...

The concept of Occam's gap

Resolution of misconception of overfitting: Differentiating learning curves from Occam curves
science-memo.blogspot.com/2023/04/Occa...

#Overfitting #MachineLearning #DeepLearning #AI

Diagnosing and Fixing Overfitting in Machine Learning with Python

Диагностика и исправление переобучения в машинном обучении с помощью Python

Переобучение - одна из наиболее (если не самая!) распространенных проблем, с которыми сталкиваются при построении моделей машинного обучения (МО).

#ai #machinelearning #overfitting

latent features, implying that their previous success in similar tasks may be attributed to #regularisation effects rather than intrinsic informativeness."

#ML #autoencoders #Overfitting

orig https://fediscience.org/@davdittrich/114180044148130177 3/3

When Dimensionality Hurts: The Role of #LLM Embedding Compression for Noisy Regression Tasks d.repec.org/n
"… suggest that the optimal dimensionality is dependent on the signal-to-noise ratio, exposing the necessity of feature compression in high […]

Diagnosing and Fixing Overfitting in Machine Learning with Python Overfitting is one of the most (if not the most!) common problems encountered when building machine learning (ML) models.

Diagnosing and Fixing Overfitting in Machine Learning with Python

Overfitting is one of the most (if not the most!) common problems encountered when building machine learning (ML) models.

#machinelearning #ml #overfitting

Data Augmentation Policy Search for Long-Term Forecasting

Liran Nochumsohn, Omri Azencot

Action editor: Mingsheng Long

https://openreview.net/forum?id=Wnd0XY0twh

#augmentation #overfitting #forecasting

Machine Learning Interview Questions & Answers (2025) Prepare for your Machine Learning interview with top questions on ML algorithms, data preprocessing, model evaluation, and deep learning

💡Preparing for a #MachineLearning #Interview? These are the top #questions you must know!

🚀Interview Topics:
✔️What is #Overfitting & how to prevent it?
✔️Bias-Variance Tradeoff explained.
✔️How do Decision Trees work?
✔️Supervised vs. Unsupervised Learning.

📖 www.kbstraining.com/blog/machine...