Bytes Are All You Need: Transformers Operating Directly On File Bytes paper page: https://
huggingface.co/papers/2306.00
238
… ByteFormer, achieves an ImageNet Top-1 classification accuracy of 77.33% when training and testing directly on TIFF file bytes using a transformer backbone with
MACHINE LEARNING
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ByteFormer: Transformers Processing Raw File Bytes for Image Classification
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Brainformers: Trading Simplicity for Efficiency in Transformers
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Brainformers: Trading Simplicity for Efficiency paper page: https://
huggingface.co/papers/2306.00
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… develop a complex block, named Brainformer, that consists of a diverse sets of layers such as sparsely gated feed-forward layers, dense feed-forward layers, attention layers, and various forms -
Understanding Transformer Internal Mechanisms Through Memory Analysis
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Birth of a Transformer: A Memory Viewpoint paper page: https://
huggingface.co/papers/2306.00
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… Large language models based on transformers have achieved great empirical successes. However, as they are deployed more widely, there is a growing need to better understand their internal mechanisms -
Analyzing Attention Glitches in Transformer Language Models
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Exposing Attention Glitches with Flip-Flop Language Modeling abs: https://
arxiv.org/abs/2306.00946 identifies and analyzes the phenomenon of attention glitches, in which the Transformer architecture's inductive biases intermittently fail to capture robust reasoning. To isolate the -
Hiera: Hierarchical Vision Transformer Simplified Architecture
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Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles abs: https://
arxiv.org/abs/2306.00989 Modern hierarchical vision transformers have added several vision-specific components in the pursuit of supervised classification performance. While these components lead to -
Understanding Concept Representations in Text-to-Image Diffusion Models
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The Hidden Language of Diffusion Models
— AK (@_akhaliq) 2 juin 2023
paper page: https://t.co/biwX1KX7hG
tackle the challenge of understanding concept representations in text-to-image models by decomposing an input text prompt into a small set of interpretable elements. This is achieved by learning a… pic.twitter.com/mF0QzNxAjoThe Hidden Language of Diffusion Models paper page: https://
huggingface.co/papers/2306.00
966
… tackle the challenge of understanding concept representations in text-to-image models by decomposing an input text prompt into a small set of interpretable elements. This is achieved by learning a -
ReviewerGPT: Using Large Language Models for Scientific Paper Review
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ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing Given the rapid ascent of large language models (LLMs), we study the question: (How) can large language models help in reviewing of scientific papers or proposals? We first conduct some pilot
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StyleDrop: Text-to-Image Generation in Any Style
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StyleDrop: Text-to-Image Generation in Any Style
— AK (@_akhaliq) 2 juin 2023
introduce StyleDrop, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided… pic.twitter.com/ATlsSA5RWsStyleDrop: Text-to-Image Generation in Any Style introduce StyleDrop, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided
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Karpathy’s GPT State Talk: Training and Prompting Strategies
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Excellent talk by @karpathy on the State of GPT, breaking down everything from the training pipeline to the most effective prompting strategies for LLMs. https://
youtube.com/watch?v=bZQun8
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AI As An Essential Tool For Modern Marketers
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AI As An Essential Tool For Modern Marketers
#AI #AIio #BigData #ML #NLU #Futureofwork @gp_pulipaka @stratorob @PetiotEric @EvanKirstel @Fgraillot @HaroldSinnott @HeinzVHoenen @helene_wpli http://
ow.ly/EcgB30svtqb