Explore tweets tagged as #undersampling
New preprint from our group! We propose SHAPES, a set of metrics to quantify the distributional coverage of generative models of protein structures with embeddings at different structural hierarchies and quantify undersampling / extrapolation behaviors.
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with random undersampling, too, they need to face a "war crimes tribunal"
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不均衡二値分類でundersampling + baggingは最適戦略か？ #Qiita . 結論、答えは出ずデータセットに応じていろいろ試して合ってそうな手法を使ってねとのこと。そんなもんかー。.
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Dive into the critical concept of handling class imbalance in datasets. This video explores key techniques like oversampling, undersampling, and adjusting class weights to ensure balanced predictions.
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Excited to present at NeurIPS 2024! 🎉 We propose a unified neural operator for Compressed Sensing MRI, adapting to multiple undersampling patterns/rates, with 11% SSIM & 4dB PSNR gains. Full paper & code: #NeurIPS2024 #ML #MRI #NeuralOperators
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The Two Distributions of the Weird. A key insight from both psychedelic research and UFO investigations is that we're systematically undersampling the true extent of anomalous phenomena. When pilots, meditators, or psychedelic researchers encounter something profoundly strange,
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The Myth of Class Imbalance Correction: Stop 🛑 Ruining Your Models . For years, ML practitioners have applied SMOTE, oversampling, and undersampling to “fix” class imbalance—believing it improves model performance.
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#LSPPDay54Today I learned that class imbalance isn’t the real reason ML models perform poorly—it's more about wrong loss functions, metrics, and thresholds. Fixing those is better than just oversampling or undersampling. #learningwithleapfrog #60DaysOfLearning2025.@lftechnology
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Not sure which undersampling method to choose for your imbalanced data?. This table breaks down the logic behind different techniques: 👇 . Ready to start right away? Check out the open-source library imbalanced learn: . Share your thought below 👇
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The authors tackle a key error in RANSAC, showing traditional sampling leads to undersampling in complex scenarios. Their simple solution for exact probability computation could revolutionize robust estimation in computer vision.
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Today was all about fixing imbalanced datasets!. 🧠 Learned & implemented:.⚖️ Oversampling & Undersampling.✨ SMOTE & ADASYN – game changers for minority class boosting!. Model performance = instantly better 📈.#MachineLearning #AI #100DaysOfCode #BuildInPublic #SMOTE #ADASYN #DS.
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Well I got the model name wrong, no wonder there are some weird 3D render. However I can't say how much it learned now, it's flux lora. It seems to have solved some of the blurring problems caused by undersampling.
Had a power outage yesterday, which caused me to not finish training my H100. With just 10K datasets, flux lora can't fully cover multiple concepts or styles either, which is why don't waste more time on flux lora.
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The Dwork Lab at the @CUMedicalSchool is studying how the undersampling direction and coil setup impact image quality with MRI. They are devising a method to predict the best undersampling strategy before full data collection. Dive into their findings ➡️
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🔥 Read our Paper.📚 An Undersampling Method Approaching the Ideal Classification Boundary for #Imbalance Problems.🔗 👨🔬 by Wensheng Zhou,Chen Liu,Peng Yuan andLei Jiang. 🏫 National Key Laboratory of Offshore Oil and Gas Exploitation.#classification
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ASCAT and microwave imagery have now confirmed that #91S is a tropical storm likely with ~40kt winds (when accounting for the scan being old and undersampling) .A few more hours likely will be between now and inner core formation, but intensification likely will be extremely fast
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"We argue that the undersampling of population uncertainties limits the range of future emission trajectories and has implications for climate transition scenarios". New article in our Special Collection on IPCC in @ClimateActionSN .
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Radar images show that Tropical Storm #Prapiroon has a mostly closed eyewall despite ASCAT only supporting 40-45 kts with undersampling.
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In Machine Learning, there could be the problem that the underlying dataset for a classification task has imbalanced classes Undersampling and Oversampling are techniques in Machine Learning for solving this problem by balancing the imbalanced dataset.
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