#outofdistribution search results

🔥 The kicker? Models trained on random, irrelevant reasoning traces outperformed those trained on logically correct ones — especially on unseen problems. #MachineLearning #OutOfDistribution #Wilders

shivanshpuri35's tweet image. 🔥 The kicker?

Models trained on random, irrelevant reasoning traces outperformed those trained on logically correct ones — especially on unseen problems.

#MachineLearning #OutOfDistribution #Wilders

A fantasy called “#OutofDistribution” by From Narrow To General AI ykulbashian.medium.com/a-fantasy-call…

HWillert's tweet image. A fantasy called “#OutofDistribution” by From Narrow To General AI ykulbashian.medium.com/a-fantasy-call…

New preprint by @GoogleResearch: #Deeplearning models with higher expressive capacity can be more robust to #outofdistribution prediction arxiv.org/abs/2106.15831 @KordingLab @neuro_data @BoWang87

danilobzdok's tweet image. New preprint by @GoogleResearch:

#Deeplearning models with higher expressive capacity can be more robust to #outofdistribution prediction

arxiv.org/abs/2106.15831

@KordingLab @neuro_data @BoWang87

📣 New publication to be presented at @UNSURE_Workshop #MICCAI2024! ⚠️Individual #outofdistribution detection methods have strengths and weaknesses. 💡 Combining complementary methods can mitigate their weaknesses! 🧐 Dive into our research: link.springer.com/chapter/10.100… 🧵(1/10)


📢 New publication to be presented @unsure_workshop #MICCAI2023! ⚠️Mahalanobis distance for #outofdistribution detection has shown mixed performance. 💡 Further research to find best practices required! 🧐 Dive into our research: arxiv.org/abs/2309.01488 #reliableAI 🧵(1/7)


💡 SWOT (Sliding-Window Optimal Transport) performs artifact detection by segmenting multi-layer features into sliding window patches and using optimal transport to align these patches with recognized in-distribution samples. #OptimalTransport #OODDetection #OutofDistribution 3/n

mofuchs1's tweet image. 💡 SWOT (Sliding-Window Optimal Transport) performs artifact detection by segmenting multi-layer features into sliding window patches and using optimal transport to align these patches with recognized in-distribution samples. #OptimalTransport #OODDetection #OutofDistribution
3/n

Cherry-picking gets you pie (nice try, Gary!). Distribution-picking gets you models that can generalize. Progress needs both. Also: chill—we’re all being trained by something. #OODAloops #OutOfDistribution #LLMphilosophy


Exciting news for language model enthusiasts! Discover how your Finetuned Large Language Model can now double as a powerful out-of-distribution detector. Check out the latest blog post here: bit.ly/4aAC9iA #language #AI #outofdistribution #LLM


Quantification of uncertainty #OutOfDistribution (OOD) is a huge challenge and a fervent research field. Fortuna (github.com/awslabs/fortuna) will soon support specific #OOD solutions. ⭐ To check what methods Fortuna already supports, see tinyurl.com/et34ue2u #MachineLearning


🚀 Fortuna starts supporting #OutOfDistribution (#OOD) #UncertaintyQuantification methods! We released #SNGP, a method that properly captures the lack of confidence in the model predictions as we move away from the data. ⭐️ Look, using SNGP is so easy! tinyurl.com/p25292vy

detommaso_g's tweet image. 🚀  Fortuna starts supporting #OutOfDistribution (#OOD) #UncertaintyQuantification methods!

We released #SNGP, a method that properly captures the lack of confidence in the model predictions as we move away from the data. ⭐️

Look, using SNGP is so easy! tinyurl.com/p25292vy
detommaso_g's tweet image. 🚀  Fortuna starts supporting #OutOfDistribution (#OOD) #UncertaintyQuantification methods!

We released #SNGP, a method that properly captures the lack of confidence in the model predictions as we move away from the data. ⭐️

Look, using SNGP is so easy! tinyurl.com/p25292vy

Cherry-picking gets you pie (nice try, Gary!). Distribution-picking gets you models that can generalize. Progress needs both. Also: chill—we’re all being trained by something. #OODAloops #OutOfDistribution #LLMphilosophy


🔥 The kicker? Models trained on random, irrelevant reasoning traces outperformed those trained on logically correct ones — especially on unseen problems. #MachineLearning #OutOfDistribution #Wilders

shivanshpuri35's tweet image. 🔥 The kicker?

Models trained on random, irrelevant reasoning traces outperformed those trained on logically correct ones — especially on unseen problems.

#MachineLearning #OutOfDistribution #Wilders

A fantasy called “#OutofDistribution” by From Narrow To General AI ykulbashian.medium.com/a-fantasy-call…

HWillert's tweet image. A fantasy called “#OutofDistribution” by From Narrow To General AI ykulbashian.medium.com/a-fantasy-call…

📣 New publication to be presented at @UNSURE_Workshop #MICCAI2024! ⚠️Individual #outofdistribution detection methods have strengths and weaknesses. 💡 Combining complementary methods can mitigate their weaknesses! 🧐 Dive into our research: link.springer.com/chapter/10.100… 🧵(1/10)


Exciting news for language model enthusiasts! Discover how your Finetuned Large Language Model can now double as a powerful out-of-distribution detector. Check out the latest blog post here: bit.ly/4aAC9iA #language #AI #outofdistribution #LLM


💡 SWOT (Sliding-Window Optimal Transport) performs artifact detection by segmenting multi-layer features into sliding window patches and using optimal transport to align these patches with recognized in-distribution samples. #OptimalTransport #OODDetection #OutofDistribution 3/n

mofuchs1's tweet image. 💡 SWOT (Sliding-Window Optimal Transport) performs artifact detection by segmenting multi-layer features into sliding window patches and using optimal transport to align these patches with recognized in-distribution samples. #OptimalTransport #OODDetection #OutofDistribution
3/n

📢 New publication to be presented @unsure_workshop #MICCAI2023! ⚠️Mahalanobis distance for #outofdistribution detection has shown mixed performance. 💡 Further research to find best practices required! 🧐 Dive into our research: arxiv.org/abs/2309.01488 #reliableAI 🧵(1/7)


🚀 Fortuna starts supporting #OutOfDistribution (#OOD) #UncertaintyQuantification methods! We released #SNGP, a method that properly captures the lack of confidence in the model predictions as we move away from the data. ⭐️ Look, using SNGP is so easy! tinyurl.com/p25292vy

detommaso_g's tweet image. 🚀  Fortuna starts supporting #OutOfDistribution (#OOD) #UncertaintyQuantification methods!

We released #SNGP, a method that properly captures the lack of confidence in the model predictions as we move away from the data. ⭐️

Look, using SNGP is so easy! tinyurl.com/p25292vy
detommaso_g's tweet image. 🚀  Fortuna starts supporting #OutOfDistribution (#OOD) #UncertaintyQuantification methods!

We released #SNGP, a method that properly captures the lack of confidence in the model predictions as we move away from the data. ⭐️

Look, using SNGP is so easy! tinyurl.com/p25292vy

Quantification of uncertainty #OutOfDistribution (OOD) is a huge challenge and a fervent research field. Fortuna (github.com/awslabs/fortuna) will soon support specific #OOD solutions. ⭐ To check what methods Fortuna already supports, see tinyurl.com/et34ue2u #MachineLearning


No results for "#outofdistribution"

🔥 The kicker? Models trained on random, irrelevant reasoning traces outperformed those trained on logically correct ones — especially on unseen problems. #MachineLearning #OutOfDistribution #Wilders

shivanshpuri35's tweet image. 🔥 The kicker?

Models trained on random, irrelevant reasoning traces outperformed those trained on logically correct ones — especially on unseen problems.

#MachineLearning #OutOfDistribution #Wilders

A fantasy called “#OutofDistribution” by From Narrow To General AI ykulbashian.medium.com/a-fantasy-call…

HWillert's tweet image. A fantasy called “#OutofDistribution” by From Narrow To General AI ykulbashian.medium.com/a-fantasy-call…

New preprint by @GoogleResearch: #Deeplearning models with higher expressive capacity can be more robust to #outofdistribution prediction arxiv.org/abs/2106.15831 @KordingLab @neuro_data @BoWang87

danilobzdok's tweet image. New preprint by @GoogleResearch:

#Deeplearning models with higher expressive capacity can be more robust to #outofdistribution prediction

arxiv.org/abs/2106.15831

@KordingLab @neuro_data @BoWang87

💡 SWOT (Sliding-Window Optimal Transport) performs artifact detection by segmenting multi-layer features into sliding window patches and using optimal transport to align these patches with recognized in-distribution samples. #OptimalTransport #OODDetection #OutofDistribution 3/n

mofuchs1's tweet image. 💡 SWOT (Sliding-Window Optimal Transport) performs artifact detection by segmenting multi-layer features into sliding window patches and using optimal transport to align these patches with recognized in-distribution samples. #OptimalTransport #OODDetection #OutofDistribution
3/n

🚀 Fortuna starts supporting #OutOfDistribution (#OOD) #UncertaintyQuantification methods! We released #SNGP, a method that properly captures the lack of confidence in the model predictions as we move away from the data. ⭐️ Look, using SNGP is so easy! tinyurl.com/p25292vy

detommaso_g's tweet image. 🚀  Fortuna starts supporting #OutOfDistribution (#OOD) #UncertaintyQuantification methods!

We released #SNGP, a method that properly captures the lack of confidence in the model predictions as we move away from the data. ⭐️

Look, using SNGP is so easy! tinyurl.com/p25292vy
detommaso_g's tweet image. 🚀  Fortuna starts supporting #OutOfDistribution (#OOD) #UncertaintyQuantification methods!

We released #SNGP, a method that properly captures the lack of confidence in the model predictions as we move away from the data. ⭐️

Look, using SNGP is so easy! tinyurl.com/p25292vy

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