#modelselection — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #modelselection, aggregated by home.social.
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via #AIFoundry : How to run evals for the model router
https://ift.tt/XAF1Ivt
#ModelRouter #Foundry #Evals #Evaluations #LLM #AIModelRouting #PromptEngineering #ModelSelection #Latency #Cost #Quality #Benchmarking #OpenSource #GitHub #EvalRepo #Azure #AzureOpenAI #Claude #Fou… -
via #AIFoundry : How to run evals for the model router
https://ift.tt/XAF1Ivt
#ModelRouter #Foundry #Evals #Evaluations #LLM #AIModelRouting #PromptEngineering #ModelSelection #Latency #Cost #Quality #Benchmarking #OpenSource #GitHub #EvalRepo #Azure #AzureOpenAI #Claude #Fou… -
via #AIFoundry : How to run evals for the model router
https://ift.tt/XAF1Ivt
#ModelRouter #Foundry #Evals #Evaluations #LLM #AIModelRouting #PromptEngineering #ModelSelection #Latency #Cost #Quality #Benchmarking #OpenSource #GitHub #EvalRepo #Azure #AzureOpenAI #Claude #Fou… -
via #AIFoundry : How to run evals for the model router
https://ift.tt/XAF1Ivt
#ModelRouter #Foundry #Evals #Evaluations #LLM #AIModelRouting #PromptEngineering #ModelSelection #Latency #Cost #Quality #Benchmarking #OpenSource #GitHub #EvalRepo #Azure #AzureOpenAI #Claude #Fou… -
via #AIFoundry : How to run evals for the model router
https://ift.tt/XAF1Ivt
#ModelRouter #Foundry #Evals #Evaluations #LLM #AIModelRouting #PromptEngineering #ModelSelection #Latency #Cost #Quality #Benchmarking #OpenSource #GitHub #EvalRepo #Azure #AzureOpenAI #Claude #Fou… -
Why does AI orchestration succeed? Not the size of the LLM, but hitting ~90 % router accuracy. Learn how precise routing, semantic cues, and smart decision logic let specialist models shine in production. A deep dive into model selection and router design that could reshape your AI pipeline. #AIRouterAccuracy #LLMRouting #ModelSelection #SemanticRouting
🔗 https://aidailypost.com/news/ai-orchestration-success-hinges-90-router-accuracy-not-model-size
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Why does AI orchestration succeed? Not the size of the LLM, but hitting ~90 % router accuracy. Learn how precise routing, semantic cues, and smart decision logic let specialist models shine in production. A deep dive into model selection and router design that could reshape your AI pipeline. #AIRouterAccuracy #LLMRouting #ModelSelection #SemanticRouting
🔗 https://aidailypost.com/news/ai-orchestration-success-hinges-90-router-accuracy-not-model-size
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Diving deep into the world of model selection! Discover how to choose your favorite and most effective model for optimal results and make data-driven decisions. #ModelSelection #MachineLearning #DataScience #AI #Analytics
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Not So Prompt: Prompt Optimization as Model Selection
https://www.gojiberries.io/not-so-prompt-prompt-optimization-as-model-selection/
#HackerNews #PromptOptimization #ModelSelection #AIResearch #GojiBerries #HackerNews
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Not So Prompt: Prompt Optimization as Model Selection
https://www.gojiberries.io/not-so-prompt-prompt-optimization-as-model-selection/
#HackerNews #PromptOptimization #ModelSelection #AIResearch #GojiBerries #HackerNews
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Not So Prompt: Prompt Optimization as Model Selection
https://www.gojiberries.io/not-so-prompt-prompt-optimization-as-model-selection/
#HackerNews #PromptOptimization #ModelSelection #AIResearch #GojiBerries #HackerNews
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Not So Prompt: Prompt Optimization as Model Selection
https://www.gojiberries.io/not-so-prompt-prompt-optimization-as-model-selection/
#HackerNews #PromptOptimization #ModelSelection #AIResearch #GojiBerries #HackerNews
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Not So Prompt: Prompt Optimization as Model Selection
https://www.gojiberries.io/not-so-prompt-prompt-optimization-as-model-selection/
#HackerNews #PromptOptimization #ModelSelection #AIResearch #GojiBerries #HackerNews
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Can anyone help with understanding how to best do #modelselection in the context of #neuralnetworks ? I'm trying to understand how to reduce #bias due to the selection of a particular test set.
More details here
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Can anyone help with understanding how to best do #modelselection in the context of #neuralnetworks ? I'm trying to understand how to reduce #bias due to the selection of a particular test set.
More details here
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Can anyone help with understanding how to best do #modelselection in the context of #neuralnetworks ? I'm trying to understand how to reduce #bias due to the selection of a particular test set.
More details here
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Can anyone help with understanding how to best do #modelselection in the context of #neuralnetworks ? I'm trying to understand how to reduce #bias due to the selection of a particular test set.
More details here
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from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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7/10) This finding led to our #proposal: Can we use α for #modelSelection in an #SSL pipeline?
Two key +s of α:
1. α doesn’t require labels
2. α is quick to #compute (compared to training a readout)
We study hyperparam selection in #BarlowTwins (Zbontar et al.) as a case study!
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7/10) This finding led to our #proposal: Can we use α for #modelSelection in an #SSL pipeline?
Two key +s of α:
1. α doesn’t require labels
2. α is quick to #compute (compared to training a readout)
We study hyperparam selection in #BarlowTwins (Zbontar et al.) as a case study!
-
7/10) This finding led to our #proposal: Can we use α for #modelSelection in an #SSL pipeline?
Two key +s of α:
1. α doesn’t require labels
2. α is quick to #compute (compared to training a readout)
We study hyperparam selection in #BarlowTwins (Zbontar et al.) as a case study!
-
7/10) This finding led to our #proposal: Can we use α for #modelSelection in an #SSL pipeline?
Two key +s of α:
1. α doesn’t require labels
2. α is quick to #compute (compared to training a readout)
We study hyperparam selection in #BarlowTwins (Zbontar et al.) as a case study!
-
7/10) This finding led to our #proposal: Can we use α for #modelSelection in an #SSL pipeline?
Two key +s of α:
1. α doesn’t require labels
2. α is quick to #compute (compared to training a readout)
We study hyperparam selection in #BarlowTwins (Zbontar et al.) as a case study!