PaLM-E: An Embodied Multimodal Language Model
In this paper, the authors introduce the concept of "embodied language models," which integrate real-world sensory information with language processing. This integration enables the models to perform tasks related to robotics and perception seamlessly.
To achieve this, the models are trained end-to-end using a large language model and multiple sensory inputs, including visual and textual information. These models can tackle complex tasks such as sequential robotic manipulation planning, visual question answering, and captioning. The results of evaluations demonstrate the effectiveness of this approach, including positive transfer across different domains.
The flagship model, PaLM-E-562B, is the crown jewel of this research. It excels in robotics tasks and delivers state-of-the-art performance on OK-VQA. Despite its specialization in robotics, this model maintains its generalist language capabilities.
Paper: https://arxiv.org/abs/2303.03378
Project link: https://palm-e.github.io/
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-palme
#deeplearning #nlp #transformer #sota #languagemodel #robotics
In this paper, the authors introduce the concept of "embodied language models," which integrate real-world sensory information with language processing. This integration enables the models to perform tasks related to robotics and perception seamlessly.
To achieve this, the models are trained end-to-end using a large language model and multiple sensory inputs, including visual and textual information. These models can tackle complex tasks such as sequential robotic manipulation planning, visual question answering, and captioning. The results of evaluations demonstrate the effectiveness of this approach, including positive transfer across different domains.
The flagship model, PaLM-E-562B, is the crown jewel of this research. It excels in robotics tasks and delivers state-of-the-art performance on OK-VQA. Despite its specialization in robotics, this model maintains its generalist language capabilities.
Paper: https://arxiv.org/abs/2303.03378
Project link: https://palm-e.github.io/
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-palme
#deeplearning #nlp #transformer #sota #languagemodel #robotics
👍18❤3
Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
ChatGPT is a language interface with distinctive conversational competency and reasoning capabilities across many domains. However, it is currently unable to process or generate images from the visual world. To address this limitation, the authors propose a system called Visual ChatGPT that incorporates different Visual Foundation Models to enable users to interact with ChatGPT using both language and images. The system is capable of handling complex visual questions or instructions that require multiple AI models and steps. Additionally, it allows for feedback and corrections.
Rather than creating a new multimodal ChatGPT from scratch, the authors propose building Visual ChatGPT by incorporating various (22) Visual Foundation Models (VFMs) directly into ChatGPT. To facilitate the integration of these VFMs, the authors introduce a Prompt Manager that supports several functions. These include specifying the input-output formats of each VFM, converting visual information to language format, and managing the histories, priorities, and conflicts of different VFMs. With the Prompt Manager's help, ChatGPT can use these VFMs iteratively and receive their feedback until it satisfies the users' requirements or reaches the end condition.
Paper: https://arxiv.org/abs/2303.04671
Code link: https://github.com/microsoft/visual-chatgpt
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-palme
#deeplearning #nlp #transformer #sota #languagemodel #visual
ChatGPT is a language interface with distinctive conversational competency and reasoning capabilities across many domains. However, it is currently unable to process or generate images from the visual world. To address this limitation, the authors propose a system called Visual ChatGPT that incorporates different Visual Foundation Models to enable users to interact with ChatGPT using both language and images. The system is capable of handling complex visual questions or instructions that require multiple AI models and steps. Additionally, it allows for feedback and corrections.
Rather than creating a new multimodal ChatGPT from scratch, the authors propose building Visual ChatGPT by incorporating various (22) Visual Foundation Models (VFMs) directly into ChatGPT. To facilitate the integration of these VFMs, the authors introduce a Prompt Manager that supports several functions. These include specifying the input-output formats of each VFM, converting visual information to language format, and managing the histories, priorities, and conflicts of different VFMs. With the Prompt Manager's help, ChatGPT can use these VFMs iteratively and receive their feedback until it satisfies the users' requirements or reaches the end condition.
Paper: https://arxiv.org/abs/2303.04671
Code link: https://github.com/microsoft/visual-chatgpt
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-palme
#deeplearning #nlp #transformer #sota #languagemodel #visual
👍31😁1
BloombergGPT: A Large Language Model for Finance
The realm of financial technology involves a wide range of NLP applications, such as sentiment analysis, named entity recognition, and question answering. Although Large Language Models (LLMs) have demonstrated effectiveness in various tasks, no LLM specialized for the financial domain has been reported so far. This work introduces BloombergGPT, a 50-billion-parameter language model trained on an extensive range of financial data. The researchers have created a massive 363-billion-token dataset using Bloomberg's data sources, supplemented with 345 billion tokens from general-purpose datasets, potentially creating the largest domain-specific dataset to date.
BloombergGPT has been validated on standard LLM benchmarks, open financial benchmarks, and a suite of internal benchmarks that accurately reflect its intended usage. The mixed dataset training results in a model that significantly outperforms existing models on financial tasks without sacrificing performance on general LLM benchmarks. The paper also discusses modeling choices, training processes, and evaluation methodology. As a next step, the researchers plan to release training logs (Chronicles) detailing their experience in training BloombergGPT.
Paper: https://arxiv.org/abs/2303.17564
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-bloomberggpt
#deeplearning #nlp #transformer #sota #languagemodel #finance
The realm of financial technology involves a wide range of NLP applications, such as sentiment analysis, named entity recognition, and question answering. Although Large Language Models (LLMs) have demonstrated effectiveness in various tasks, no LLM specialized for the financial domain has been reported so far. This work introduces BloombergGPT, a 50-billion-parameter language model trained on an extensive range of financial data. The researchers have created a massive 363-billion-token dataset using Bloomberg's data sources, supplemented with 345 billion tokens from general-purpose datasets, potentially creating the largest domain-specific dataset to date.
BloombergGPT has been validated on standard LLM benchmarks, open financial benchmarks, and a suite of internal benchmarks that accurately reflect its intended usage. The mixed dataset training results in a model that significantly outperforms existing models on financial tasks without sacrificing performance on general LLM benchmarks. The paper also discusses modeling choices, training processes, and evaluation methodology. As a next step, the researchers plan to release training logs (Chronicles) detailing their experience in training BloombergGPT.
Paper: https://arxiv.org/abs/2303.17564
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-bloomberggpt
#deeplearning #nlp #transformer #sota #languagemodel #finance
🤔16👍7🔥4❤2🥰1
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
In the rapidly evolving landscape of artificial intelligence, a groundbreaking approach to supervised classification performance has been born. Modern hierarchical vision transformers have been known to incorporate various vision-specific components, aiming to enhance accuracies and produce desirable FLOP counts. However, these augmentations have led to slower processing times compared to their vanilla ViT counterparts. In this exciting research, we challenge the necessity of such additional complexities.
Enter Hiera, an innovative and significantly simplified hierarchical vision transformer that champions efficiency without compromising accuracy. By deploying a potent visual pretext task, MAE, we're able to eliminate the bells-and-whistles from a state-of-the-art multi-stage vision transformer. The result? A lean, mean machine learning model that not only outperforms its predecessors in terms of accuracy but also achieves superior speed, both during inference and training. Tested across a diverse array of image and video recognition tasks, Hiera stands as a beacon of progress in the field of computer vision.
Paper link: https://arxiv.org/abs/2306.00989
Code link: https://github.com/facebookresearch/hiera
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-hiera
#deeplearning #cv #transformer #sota
In the rapidly evolving landscape of artificial intelligence, a groundbreaking approach to supervised classification performance has been born. Modern hierarchical vision transformers have been known to incorporate various vision-specific components, aiming to enhance accuracies and produce desirable FLOP counts. However, these augmentations have led to slower processing times compared to their vanilla ViT counterparts. In this exciting research, we challenge the necessity of such additional complexities.
Enter Hiera, an innovative and significantly simplified hierarchical vision transformer that champions efficiency without compromising accuracy. By deploying a potent visual pretext task, MAE, we're able to eliminate the bells-and-whistles from a state-of-the-art multi-stage vision transformer. The result? A lean, mean machine learning model that not only outperforms its predecessors in terms of accuracy but also achieves superior speed, both during inference and training. Tested across a diverse array of image and video recognition tasks, Hiera stands as a beacon of progress in the field of computer vision.
Paper link: https://arxiv.org/abs/2306.00989
Code link: https://github.com/facebookresearch/hiera
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-hiera
#deeplearning #cv #transformer #sota
👍11❤3👀3🔥2🥰1
Meta-Transformer: A Unified Framework for Multimodal Learning
The landscape of multimodal learning is about to witness a remarkable transformation with the introduction of Meta-Transformer, a state-of-the-art framework that's poised to overcome long-standing challenges in the field. The beauty of Meta-Transformer lies in its unique ability to process and understand information from a diverse range of modalities - from natural language, 2D images, 3D point clouds, to audio, video, time series, and tabular data. This ability stems from its innovative design that leverages a frozen encoder to map raw input data from these diverse modalities into a shared token space, eliminating the need for paired multimodal training data.
More than just a theoretical achievement, the Meta-Transformer has proven its practical application across various benchmarks, handling an impressive range of tasks from fundamental perception such as text, image, and audio processing, to more complex applications like X-Ray, infrared, and hyperspectral data interpretation, as well as data mining tasks involving graph, tabular, and time-series data.
Code link: https://github.com/invictus717/MetaTransformer
Paper link: https://arxiv.org/abs/2307.10802
A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-meta-transformer
#deeplearning #nlp #transformer #cv
The landscape of multimodal learning is about to witness a remarkable transformation with the introduction of Meta-Transformer, a state-of-the-art framework that's poised to overcome long-standing challenges in the field. The beauty of Meta-Transformer lies in its unique ability to process and understand information from a diverse range of modalities - from natural language, 2D images, 3D point clouds, to audio, video, time series, and tabular data. This ability stems from its innovative design that leverages a frozen encoder to map raw input data from these diverse modalities into a shared token space, eliminating the need for paired multimodal training data.
More than just a theoretical achievement, the Meta-Transformer has proven its practical application across various benchmarks, handling an impressive range of tasks from fundamental perception such as text, image, and audio processing, to more complex applications like X-Ray, infrared, and hyperspectral data interpretation, as well as data mining tasks involving graph, tabular, and time-series data.
Code link: https://github.com/invictus717/MetaTransformer
Paper link: https://arxiv.org/abs/2307.10802
A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-meta-transformer
#deeplearning #nlp #transformer #cv
👍8👨💻6🔥3❤2