🦜 Hi!

We are the first Telegram Data Science channel.

Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.


Ultimate Posts

* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda


Open Data Science

ODS.ai is an international community of people anyhow related to Data Science.

Website: https://ods.ai



Hashtags

Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.

#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
#OpenAI #microsoft #Github #DeepMind #Yandex #Google #Facebook #huggingface — hashtags related to certain companies
#productionml #sota #recommendation #embeddings #selfdriving #dataset #opensource #analytics #statistics #attention #machine #translation #visualization


Chats

- Data Science Chat https://t.me/datascience_chat
- ODS Slack through invite form at website

ODS resources

* Main website: https://ods.ai
* ODS Community Telegram Channel (in Russian): @ods_ru
* ML trainings Telegram Channel: @mltrainings
* ODS Community Twitter: https://twitter.com/ods_ai

Feedback and Contacts

You are welcome to reach administration through telegram bot: @opendatasciencebot

​​Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

While vision transformers demostrate high performance, they can't be deployed as efficiently as CNNs in realistic industrial deployment scenarios, e. g. TensorRT or CoreML.

The authors propose Next-ViT, which has a higher latency/accuracy trade-off than existing CNN and ViT models. They develop two new architecture blocks and a new paradigm to stack them. As a result, On TensorRT, Next-ViT surpasses ResNet by 5.4 mAP (from 40.4 to 45.8) on COCO detection and 8.2% mIoU (from 38.8% to 47.0%) on ADE20K segmentation. Also, it achieves comparable performance with CSWin, while the inference speed is accelerated by
3.6×. On CoreML, Next-ViT surpasses EfficientFormer by 4.6 mAP (from 42.6 to 47.2) on COCO detection and 3.5% mIoU (from 45.2% to 48.7%) on ADE20K segmentation under similar latency.

Paper: https://arxiv.org/abs/2207.05501

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-next-vit

#deeplearning #cv #transformer #computervision

​​Dual PatchNorm

The authors propose a new method, Dual PatchNorm, for Vision Transformers which involves adding two Layer Normalization layers before and after the patch embedding layer. Experiments across three datasets show that this method improves the performance of well-tuned ViT models, and qualitative experiments support this.

Paper: https://arxiv.org/abs/2302.01327

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-dual-patch-norm

#deeplearning #cv #transformer

​​Scaling Vision Transformers to 22 Billion Parameters

Google Research authors present a recipe for training a highly efficient and stable Vision Transformer (ViT-22B) with 22B parameters, the largest dense ViT model to date. Experiments reveal that as the model's scale increases, its performance on downstream tasks improves. Additionally, ViT-22B shows an improved tradeoff between fairness and performance, state-of-the-art alignment with human visual perception in terms of shape/texture bias, and improved robustness. The authors suggest that ViT-22B demonstrates the potential for achieving “LLM-like” scaling in vision models and takes important steps toward that goal.

Paper: https://arxiv.org/abs/2302.05442

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-vit-22

#deeplearning #cv #transformer #sota

​​LLaMA: Open and Efficient Foundation Language Models

LLaMA is a set of large language models, ranging from 7B to 65B parameters, that have been trained on publicly available datasets containing trillions of tokens. The LLaMA-13B model performs better than GPT-3 (175B) on most benchmarks, and the LLaMA-65B model is competitive with other state-of-the-art models, such as Chinchilla70B and PaLM-540B. This suggests that it is possible to achieve excellent performance in language modeling without relying on proprietary or inaccessible datasets.

Paper: https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/

Code: https://github.com/facebookresearch/llama

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-llama

#deeplearning #nlp #transformer #sota #languagemodel

​​In-Context Instruction Learning

The authors introduce a novel approach called In-Context Instruction Learning (ICIL), which greatly enhances zero-shot task generalization performance for both pretrained and instruction-fine-tuned models. ICIL employs a single fixed prompt to evaluate all tasks, which is a concatenation of cross-task demonstrations. The authors demonstrate that even the most powerful instruction-fine-tuned baseline (text-davinci-003) benefits from ICIL by 9.3%, indicating that the effect of ICIL is complementary to instruction-based fine-tuning.

Paper: https://arxiv.org/abs/2302.14691

Code: https://github.com/seonghyeonye/ICIL

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-icil

#deeplearning #nlp #transformer #sota #languagemodel

​​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

​​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

​​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

​​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

​​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