Also a lot is it due to 512×512 limit of SD1.5, with SD2 u can do 786×786 so it gets better, but we're all upscaling so real details we will get in a few months when we can do 1024×1024 and 2048×2048 etc after, it will exponentially get better
RESEARCH
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Ludwig v0.6 Simplifies ML Model Operationalization with Inference
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Operationalizing state-of-the-art #ML #models just got easier! With @ludwig_ai v0.6, you get an end-to-end #inference pipeline out of the box. Check out the blog to learn more. Sample code and #data included. https://
pbase.ai/3Hrdstf #machinelearning #deeplearning #pytorch -
Large Language Models: Confidence and Misinformation Risk Reduction
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3/Building large language models that can accurately decide when to be confident and when not to will reduce their risk of misinformation and build trust.
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Large Language Models’ Overconfidence Problem and Misinformation Risk
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1/Large language models like Galactica and ChatGPT can spout nonsense in a confident, authoritative tone. This overconfidence – which reflects the data they’re trained on – makes them more likely to mislead.
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Robotics Transformer 1: Multi-Task Robot Learning Model
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Introducing the Robotics Transformer 1, a multi-task model that tokenizes robot inputs and outputs actions to enable efficient inference at runtime. Learn how it improves zero-shot generalization to new tasks, environments and objects → https://t.co/hnsKvCJjmP pic.twitter.com/g9QFXzjs9T
— Google AI (@GoogleAI) 13 décembre 2022Introducing the Robotics Transformer 1, a multi-task model that tokenizes robot inputs and outputs actions to enable efficient inference at runtime. Learn how it improves zero-shot generalization to new tasks, environments and objects → https://
goo.gle/3Yxomnt -
Self-ranking method improves academic paper evaluation system
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This is an obvious critique, which the paper deals with. It makes you rank your own papers against each other. This turns out to make things work out. Check out the paper!
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Key AI Benchmarks for Language Model Evaluation
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Benchmarks:
– MMLU (massively multitask language understanding): https://
arxiv.org/abs/2009.03300
– BBH (Big-Bench Hard): https://
arxiv.org/abs/2210.09261
– TyDiQA (typographically diverse QA): https://
arxiv.org/abs/2003.05002
– MGSM (multilingual grade school math): https://
arxiv.org/abs/2210.03057 -
Code-Davinci-2 vs Text-Davinci-3: Instruction Tuning and PPO Performance
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– code-davinci-2 > text-davinci-3 means that their instruction finetuning overall hurts performance on academic benchmarks
– text-davinci-3 > text-davinci-2 means that PPO improves performance -
Text-davinci-003 Instruction Following vs Academic Benchmark Performance
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@OpenAI
's text-davinci-003 follows instructions better. Is it also better on academic benchmarks? Summary:
– text-davinci-3 beats text-davinci-2, but is not as good as code-davinci-2
– it is behind @GoogleAI
's PaLM and Flan-U-PaLM Full results: https://
arxiv.org/abs/2210.11416 App D -
Neural Networks Introduction: 7-Minute Quick Start Guide
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Get started with Neural Networks in 7 minutes!
— Satya Mallick (@LearnOpenCV) 13 décembre 2022
Watch the full video here: https://t.co/wDHSvFKuLJ #learnopencv #opencv #computervision #artificialintelligence #neuralnetworks #python #ai #deeplearning #machinelearning pic.twitter.com/6dbnXlXZYtGet started with Neural Networks in 7 minutes!
Watch the full video here: https://
youtube.com/watch?v=gsgt_u
XvjmM
… #learnopencv #opencv #computervision #artificialintelligence #neuralnetworks #python #ai #deeplearning #machinelearning