RTAB-Map

Real-Time Appearance-Based Mapping

http://introlab.github.io/rtabmap/

Github: https://github.com/introlab/rtabmap

Paper: https://arxiv.org/abs/2103.03827v1

@ai_machinelearning_big_data

Precise Multi-Neuron Abstractions for Neural Network Certification

Github : https://github.com/eth-sri/eran

Paper: https://arxiv.org/abs/2103.03638v1

@ai_machinelearning_big_data

👔 Virtual Try-on via Distilling Appearance Flows, CVPR 2021

Github: https://github.com/geyuying/PF-AFN

Paper: https://arxiv.org/abs/2103.04559

@ai_machinelearning_big_data

Train and serve a TensorFlow model with TensorFlow Serving

https://www.tensorflow.org/tfx/tutorials/serving/rest_simple

Code: https://github.com/tensorflow/tfx/blob/master/docs/tutorials/serving/rest_simple.ipynb

Dataset: https://github.com/zalandoresearch/fashion-mnist

@ai_machinelearning_big_data

Involution: Inverting the Inherence of Convolution for Visual Recognition

Github: https://github.com/d-li14/involution

Paper: https://arxiv.org/abs/2103.06255

OpenMMLab: https://openmmlab.com/

@ai_machinelearning_big_data

Strawberry Fields is a full-stack Python library for designing, simulating, and optimizing continuous-variable quantum optical circuits.

Github: https://github.com/XanaduAI/strawberryfields

Paper: https://arxiv.org/pdf/2103.05530v1.pdf

@ai_machinelearning_big_data

MagFace: A Universal Representation for Face Recognition and Quality Assessment

Github: https://github.com/IrvingMeng/MagFace

Paper: https://arxiv.org/abs/2103.06627

Code example: https://github.com/IrvingMeng/MagFace/blob/main/inference/examples.ipynb

@ai_machinelearning_big_data

Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion

Github: https://github.com/hkchengrex/MiVOS

Paper: https://arxiv.org/pdf/2103.07941v1.pdf

@ai_machinelearning_big_data

Open Avatarify Photorealistic avatars for video-conferencing apps. Democratized.

Github: https://github.com/alievk/avatarify-python

Demo: https://www.youtube.com/watch?v=Q7LFDT-FRzs&feature=youtu.be&ab_channel=AliAliev

@ai_machinelearning_big_data

Probabilistic two-stage detection

Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network.

Github: https://github.com/xingyizhou/CenterNet2

Paper: https://arxiv.org/abs/2103.07461v1


@ai_machinelearning_big_data

🔷 PyTorch Geometric Temporal is a temporal (dynamic) extension library for PyTorch Geometric.

Github: https://github.com/benedekrozemberczki/pytorch_geometric_temporal

Paper: https://dl.acm.org/doi/10.1145/3308560.3316581

Dataset: https://pytorch-geometric-temporal.readthedocs.io/en/latest/notes/introduction.html#discrete-time-datasets

@ai_machinelearning_big_data

Large Scale Image Completion via Co-Modulated Generative Adversarial Networks, ICLR 2021 (Spotlight)

Gitub: https://github.com/zsyzzsoft/co-mod-gan

Paper: https://openreview.net/pdf?id=sSjqmfsk95O

Demo: http://comodgan.ml/

@ai_machinelearning_big_data

Contrastive Learning of Musical Representations

Github: https://github.com/spijkervet/CLMR

Paper: https://arxiv.org/abs/2103.09410

@ai_machinelearning_big_data

🔬 The Best ML Frameworks & Extensions for Scikit-learn

https://neptune.ai/blog/the-best-ml-framework-extensions-for-scikit-learn

@ai_machinelearning_big_data

The last article in uplift modeling tutorials by MTS data scientists: “Metrics in uplift modeling”📈

Uplift models are used to promote products and create advertising segments 🤑

📗 Habr: https://habr.com/ru/company/ru_mts/blog/538934/
💻 Code: https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/uplift_metrics_tutorial.ipynb

The GENRE (Generarive ENtity REtrieval) system as presented in Autoregressive Entity Retrieval implemented in pytorch.

Github: https://github.com/facebookresearch/GENRE

Paper: https://arxiv.org/abs/2103.12528v1

Dataset: https://github.com/facebookresearch/GENRE/blob/main/scripts/download_all_datasets.sh

@ai_machinelearning_big_data

Каждый день мы загружаем и передаем все больше контента. Нагрузка на базовые станции операторов растет, но вместе с ней растет и качество связи. Как это получается? И какую роль в этом процессе играет big data?

Ответы найдете в видео «Как нейросети помогают оставаться на связи», где Тимур и Вика, датасайентисты из МегаФона, рассказывают, почему модернизация базовых станций – это задача из области data science, как работает модель, на основании рекомендаций которой принимаются решения о необходимости «прокачки» той или иной вышки, и остается ли в этом процессе место для человека.

Спойлер: место для человека остается. Например, в МегаФоне в команду по работе с большими данными ищут Team Lead, Senior data scientist, инженера SQL и других специалистов.

Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization

Github: https://github.com/czczup/URST

Paper: https://arxiv.org/abs/2103.11784

Dataset: https://cocodataset.org/#download

@ai_machinelearning_big_data

🤗 ⚡️Multilingual CLIP with Huggingface + PyTorch Lightning

https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html#Data

CLIP: Connecting Text and Images: https://openai.com/blog/clip/


@ai_machinelearning_big_data