BigData
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Data Science : Big Data : Machine Learning : Deep Learning. По всем вопросам @evgenycarter
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☄️ Uncertainty-Aware Machine Translation Evaluation

Code: https://github.com/Unbabel/COMET

Paper: https://arxiv.org/abs/2109.06352v1

Documentation: https://unbabel.github.io/COMET/html/index.html
👉 @bigdata_1
Two4Two: Evaluating Interpretable Machine Learning -- A Synthetic Dataset For Controlled Experiments

Github: https://github.com/mschuessler/two4two

Paper: https://arxiv.org/abs/2105.02825v1
👉 @bigdata_1
🌳 WildWood: a new Random Forest algorithm

Github: https://github.com/pyensemble/wildwood

Paper: https://arxiv.org/abs/2109.08010v1

Documentation: https://wildwood.readthedocs.io/
👉 @bigdata_1
Chimera: Learning Shared Semantic Space for Speech-to-Text Translation (Nightly Version)

Github: https://github.com/Glaciohound/Chimera-SLT

Paper: https://arxiv.org/abs/2105.03095

👉 @bigdata_1
Diffusion Models Beat GANs on Image Synthesis

Github: https://github.com/openai/guided-diffusion

Paper: https://arxiv.org/abs/2105.05233v1

👉 @bigdata_1
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The Machine & Deep Learning Compendium Open Book

https://book.mlcompendium.com/

GitBook: https://github.com/orico/www.mlcompendium.com

👉 @bigdata_1
Self-Supervised Learning with Swin Transformers

Github: https://github.com/SwinTransformer/Transformer-SSL

Paper: https://arxiv.org/abs/2105.04553v2

👉 @bigdata_1
Dynamic Slimmable Network (DS-Net)

Github: https://github.com/M3DV/AlignShift

Paper: https://arxiv.org/abs/2109.10060v1

👉 @bigdata_1
A Large-Scale Benchmark for Food Image Segmentation

Github: https://github.com/XiongweiWu/FoodSeg-Benchmark

Paper: https://arxiv.org/abs/2105.05409v1

👉 @bigdata_1
Language Models are Few-shot Multilingual Learners

Github: https://github.com/gentaiscool/few-shot-lm

Paper: https://arxiv.org/abs/2109.07684v1

👉 @bigdata_1
EL-Attention: Memory Efficient Lossless Attention for Generation

Github: https://github.com/microsoft/fastseq

Paper: https://arxiv.org/abs/2105.04779v1

👉 @bigdata_1
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CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

CompilerGym is a library of easy to use and performant reinforcement learning environments for compiler tasks.

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

Documents: https://facebookresearch.github.io/CompilerGym/

Paper: https://arxiv.org/abs/2109.08267v1

👉 @bigdata_1
SAFIN: Arbitrary Style Transfer With Self-Attentive Factorized Instance Normalization

Github: https://github.com/Aaditya-Singh/SAFIN

Paper: https://arxiv.org/abs/2105.06129v1

👉 @bigdata_1
Don’t be Contradicted with Anything!CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

Github: https://github.com/yizhen20133868/ci-tod

Paper: https://arxiv.org/abs/2109.11292v1

Dataset: https://paperswithcode.com/dataset/kvret-1

👉 @bigdata_1