​​CREME – python library for online ML

All the tools in the library can be updated with a single observation at a time, and can therefore be used to learn from streaming data.

The model learns from one observation at a time, and can therefore be updated on the fly. This allows to learn from massive datasets that don't fit in main memory. Online machine learning also integrates nicely in cases where new data is constantly arriving. It shines in many use cases, such as time series forecasting, spam filtering, recommender systems, CTR prediction, and IoT applications. If you're bored with retraining models and want to instead build dynamic models, then online machine learning might be what you're looking for.

Here are some benefits of using creme (and online machine learning in general):
• incremental – models can update themselves in real-time
• adaptive – models can adapt to concept drift
• production-ready – working with data streams makes it simple to replicate production scenarios during model development
• efficient – models don't have to be retrained and require little compute power, which lowers their carbon footprint


api reference: https://creme-ml.github.io/content/api.html
github: https://github.com/creme-ml/creme

#ml #online #learning

​​the latest news from :hugging_face_mask:

[0] Helsinki-NLP

With v2.9.1 released 1,008 machine translation models, covering of 140 different languages trained with marian-nmt

link to models: https://huggingface.co/models?search=Helsinki-NLP%2Fopus-mt


[1] updated colab notebook with the new Trainer

colab: https://t.co/nGQxwqwwZu?amp=1


[2] NLP – library to easily share & load data/metrics already providing access to 99+ datasets!

features
– get them all: built-in interoperability with pytorch, tensorflow, pandas, numpy
– simple transparent pythonic API
– strive on large datasets: nlp frees you from RAM memory limits
– smart cache: process once reuse forever
– add your dataset

colab: https://t.co/37pfogRWIZ?amp=1
github: https://github.com/huggingface/nlp


#nlp #huggingface #helsinki #marian #trainer # #data #metrics

🔥Nvidia product release livestream


https://youtu.be/nurL3N1Etuc

​​Transformer Reasoning Network for Image-Text Matching and Retrieval

A new approach for image-text matching using Faster-RCNN Bottom-Up and BERT.

Usually, downstream applications use the ResNet or one of its variants as the backbone CNN. Its simple and modular design can be easily adapted to various tasks. However, since ResNet models are originally designed for image classification, they may not be suitable for various downstream applications because of the limited receptive-field size and lack of cross-channel interaction.

Authors suggest an architecture, where images and texts are processed at first, and then their representations are combined.

Main contributions of the paper:
- TERN Architecture
- NDCG metric in addition to Recall@K
- show SOTA result on the benchmark


Paper: https://arxiv.org/abs/2004.09144
Code: https://github.com/mesnico/TERN

#computervision #deeplearning #bert #imagetextmatching

​​Brilliant article on different float types used in DL

FP64, FP32, FP16, BFLOAT16, TF32, and other members of the ZOO by Grigory Sapunov

Link: https://medium.com/@moocaholic/fp64-fp32-fp16-bfloat16-tf32-and-other-members-of-the-zoo-a1ca7897d407

#dl #engineering #cs #floatingpoint

S2IGAN — Speech-to-Image Generation via Adversarial Learning

Authors present a framework that translates speech to images bypassing text information, thus allowing unwritten languages to potentially benefit from this technology.

ArXiV: https://arxiv.org/abs/2005.06968
Project: https://xinshengwang.github.io/project/s2igan/

#DL #audiolearning #speechrecognition

​​Blackcellmagic extension for jupyter

There are people who like dark themes and are fond of them, but this extension helps to format the code.

Extension: https://github.com/csurfer/blackcellmagic
Black formatter: https://github.com/psf/black

#codestyle #python #jupyter

Dear subscribers we remind you that our channel remains focused on DS-related news and updates, so you are welcome to read and submit any DS-related news to our separate repo: https://github.com/open-data-science/ultimate_posts/tree/master/COVID_2019

PRs are mostly welcome.

​​CURL: Contrastive Unsupervised Representations for Reinforcement Learning

This paper introduces a new method that significantly improves the sample efficiency of RL algorithms when learning from raw pixel data.

CURL architecture consists of three models: Query Encoder, Key Encoder, and RL agent. Query Encoder outputs embedding which used in RL agent as state representation. Contrastive loss computed from outputs of Query Encoder and Key Encoder. An important thing is that Query Encoder learns to minimize both RL and contrastive losses which allow all models to be trained jointly.

The method was tested on Atari and DeepMind Control tasks with limited interaction steps. It showed SOTA results for most of these tasks.

Paper: https://arxiv.org/abs/2004.04136.pdf
Code: https://github.com/MishaLaskin/curl


#rl #agent #reinforcement #learning

​​Mypy stubs, i.e., type information, for numpy, pandas and matplotlib for your #ds #python projects.

Lots of functions are already typed, but a lot is still missing (numpy and pandas are huge libraries).

https://github.com/predictive-analytics-lab/data-science-types

Remote online PyDoma meetup for russian-speaking community.

#PyData — worldwide events about anything related to storage, processing or visualization data with #python.

Link: https://pydata.timepad.ru/event/1318959/

P.S. We are happy to promote any national online activities, which are valuable and open to public.

​​Automatic product tagging on photoes on Facebook Pages

#FacebookAI released an improvement aiming at enhancing shopping platform.

Post: https://ai.facebook.com/blog/powered-by-ai-advancing-product-understanding-and-building-new-shopping-experiences
Paper: https://scontent-arn2-1.xx.fbcdn.net/v/t39.8562-6/99353320_565175057533429_3886205100842024960_n.pdf

#GrokNet #DL #segmentation #PyTorch

​​Learning to Simulate Dynamic Environments with GameGAN

#Nvidia designed a GAN that able to recreate games without any game engine. To train it, authors of the model use experience collected by reinforcement learning and other techniques.

GameGAN successfully reconstructed all mechanics of #Pacman game. Moreover, the trained model can generate new mazes that have never appeared in the original game. It can even replace background (static objects) and foreground (dynamic objects) with different images!

As the authors say, applying reinforcement learning algorithms to real world tasks requires accurate simulation of that task. Currently designing such simulations is expensive and time-consuming. Using neural networks instead of hand-written simulations may help to solve these problems.

Paper: https://cdn.arstechnica.net/wp-content/uploads/2020/05/Nvidia_GameGAN_Research.pdf
Blog: https://blogs.nvidia.com/blog/2020/05/22/gamegan-research-pacman-anniversary/
Github Page: https://nv-tlabs.github.io/gameGAN/

#GAN #RL

​​SpERT: Span-based Joint Entity and Relation Extraction with Transformer Pre-training

Authors introduce SpERT, an attention model for span-based joint entity and relation extraction.

This work investigates the use of Transformer networks for relation extraction: given a pre-defined set of target relations and a sentence such as “Leonardo DiCaprio starred in Christopher Nolan’s thriller Inception”, the goal is to extract triplets such as (“Leonardo DiCaprio”, Plays-In, “Inception”) or (“Inception”, Director, “Christopher Nolan”).

The main contributions of the paper are:
– a novel approach towards span-based joint entity and relation extraction
– ablation study showing that negative samples from the same sentence yield efficient training, a localized context representation is beneficial, finetuning a pre-trained model yields a strong performance increase over training from scratch.

This approach improves the SOTA score on CoNLL04 dataset by 2.6% (micro) F1.


Paper: https://arxiv.org/abs/1909.07755
Code: https://github.com/markus-eberts/spert

#nlp #deeplearning #transformer #bert #ner #relationextraction

​​Movement Pruning: Adaptive Sparsity by Fine-Tuning
Victor Sanh, Thomas Wolf, Alexander M. Rush
Hugging Face, Cornell University

The authors consider the case of pruning of pretrained models for task-specific fine-tuning and compare zeroth- and first-order pruning methods. They show that a simple method for weight pruning based on straight-through gradients is effective for this task and that it adapts using a first-order importance score.

They apply this movement pruning to a transformer-based architecture and empirically show that their method consistently yields strong improvements over existing methods in high-sparsity regimes. The analysis demonstrates how this approach adapts to the fine-tuning regime in a way that magnitude pruning cannot.
In future work, it would also be interesting to leverage group-sparsity inducing penalties to remove entire columns or filters. In this setup, they would associate a score to a group of weights (a column or a row for instance). In the transformer architecture, it would give a systematic way to perform feature selection and remove entire columns of the embedding matrix.


paper: https://arxiv.org/abs/2005.07683

#nlp #pruning #sparsity #transfer #learning

​​First Order Motion Model for Image Animation hooked up to a live camera

You can animate any face with your own mimic from camera.

Github: https://github.com/anandpawara/Real_Time_Image_Animation
Original work: https://github.com/AliaksandrSiarohin/first-order-model

#DL #deepfake #DIY

Img credit [at]aldrwinter in Twitter.

Absolutely brilliant

Tail risk of contagious diseases

Fresh N. Taleb’s and Pasquale Cirillo’s article on risks of fat tail distribution.

Article: https://www.nature.com/articles/s41567-020-0921-x

#statistics #fattail

​​End-to-End Object Detection with Transformers

Authors present a new method that views object detection as a direct set prediction problem.

This approach simplifies the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task.

The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture

DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily generalized to produce panoptic segmentation in a unified manner


Paper: https://arxiv.org/abs/2005.12872
Code: https://github.com/facebookresearch/detr

#deeplearning #objectdetection #transformer #coco

​​GPT-3: Language Models are Few-Shot Learners

#openAI train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting
Their model applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model.

Achieves strong performance on many NLP datasets, including translation, q&a, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.

Also, they find that GPT-3 can generate samples of news articles in which human evaluators have difficulty distinguishing from articles written by humans.

175 billion parameters! And on some tasks, it is not performed
It is all you need to know about


paper: https://arxiv.org/abs/2005.14165.pdf

#nlp #gpt #gpt3 #language #model