Bilingual Corpus Mining and Multistage Fine-Tuning for Improving Machine Translation of Lecture Transcripts
π₯ Github: https://github.com/shyyhs/CourseraParallelCorpusMining
π Paper: https://arxiv.org/abs/2311.03696v1
π₯ Datasets: https://paperswithcode.com/dataset/aspec
https://t.iss.one/DataScienceT
π₯ Github: https://github.com/shyyhs/CourseraParallelCorpusMining
π Paper: https://arxiv.org/abs/2311.03696v1
π₯ Datasets: https://paperswithcode.com/dataset/aspec
https://t.iss.one/DataScienceT
π1
Large Language Models (in 2023)
An excellent summary of the research progress and developments in LLMs.
Hyung Won chung, OpenAI (ex.Google and MIT Alumni) made this content publicly available. It's a great way to catch up on some important themes like scaling and optimizing LLMs.
Watch his talk here and Slides shared here.
https://t.iss.one/DataScienceT
An excellent summary of the research progress and developments in LLMs.
Hyung Won chung, OpenAI (ex.Google and MIT Alumni) made this content publicly available. It's a great way to catch up on some important themes like scaling and optimizing LLMs.
Watch his talk here and Slides shared here.
https://t.iss.one/DataScienceT
π3β€1
π Whisper-V3 / Consistency Decoder
Improved decoding for stable diffusion vaes.
- Whisper paper: https://arxiv.org/abs/2212.04356
- Whisper-V3 checkpoint: https://github.com/openai/whisper/discussions/1762
- Consistency Models: https://arxiv.org/abs/2303.01469
- Consistency Decoder release: https://github.com/openai/consistencydecoder
https://t.iss.one/DataScienceT
Improved decoding for stable diffusion vaes.
- Whisper paper: https://arxiv.org/abs/2212.04356
- Whisper-V3 checkpoint: https://github.com/openai/whisper/discussions/1762
- Consistency Models: https://arxiv.org/abs/2303.01469
- Consistency Decoder release: https://github.com/openai/consistencydecoder
https://t.iss.one/DataScienceT
π2
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NVIDIA just made Pandas 150x faster with zero code changes.
All you have to do is:
Their RAPIDS library will automatically know if you're running on GPU or CPU and speed up your processing.
You can try it in this colab notebook
GitHub repo: https://github.com/rapidsai/cudf
https://t.iss.one/DataScienceT
All you have to do is:
%load_ext cudf.pandas
import pandas as pd
Their RAPIDS library will automatically know if you're running on GPU or CPU and speed up your processing.
You can try it in this colab notebook
GitHub repo: https://github.com/rapidsai/cudf
https://t.iss.one/DataScienceT
π7β€2
πͺ Mirror: A Universal Framework for Various Information Extraction Tasks
π₯ Github: https://github.com/Spico197/Mirror
π Paper: https://arxiv.org/abs/2311.05419v1
π Dataset: https://paperswithcode.com/dataset/glue
https://t.iss.one/DataScienceT
π₯ Github: https://github.com/Spico197/Mirror
π Paper: https://arxiv.org/abs/2311.05419v1
π Dataset: https://paperswithcode.com/dataset/glue
https://t.iss.one/DataScienceT
π6β€2
Conic10K
π₯ Github:https://github.com/whynlp/conic10k
π Paper: https://arxiv.org/pdf/2311.05113v1.pdf
π₯ Datasets: https://paperswithcode.com/dataset/gsm8k
β¨ Tasks: https://paperswithcode.com/task/natural-language-understanding
https://t.iss.one/DataScienceT
π₯ Github:https://github.com/whynlp/conic10k
π Paper: https://arxiv.org/pdf/2311.05113v1.pdf
π₯ Datasets: https://paperswithcode.com/dataset/gsm8k
β¨ Tasks: https://paperswithcode.com/task/natural-language-understanding
https://t.iss.one/DataScienceT
π6
β‘οΈ LCM-LoRA: A Universal Stable-Diffusion Acceleration Module
Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.
π₯ Github: https://github.com/luosiallen/latent-consistency-model
π Paper: https://arxiv.org/abs/2311.05556v1
π Project: https://latent-consistency-models.github.io
π€ Demo: https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model
https://t.iss.one/DataScienceT
Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.
pip install diffusers transformers accelerate gradio==3.48.0
π₯ Github: https://github.com/luosiallen/latent-consistency-model
π Paper: https://arxiv.org/abs/2311.05556v1
π Project: https://latent-consistency-models.github.io
π€ Demo: https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model
https://t.iss.one/DataScienceT
π3
Quantized Distillation for Driver Activity Recognition
π₯ Github: https://github.com/calvintanama/qd-driver-activity-reco
π Paper: https://arxiv.org/pdf/2311.05970v1.pdf
π₯ Datasets: https://paperswithcode.com/dataset/drive-act
β¨ Tasks: https://paperswithcode.com/task/activity-recognition
https://t.iss.one/DataScienceT
π₯ Github: https://github.com/calvintanama/qd-driver-activity-reco
π Paper: https://arxiv.org/pdf/2311.05970v1.pdf
π₯ Datasets: https://paperswithcode.com/dataset/drive-act
β¨ Tasks: https://paperswithcode.com/task/activity-recognition
https://t.iss.one/DataScienceT
π1
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Perhaps you have thought about placing ads on it?
To do this, follow three simple steps:
1) Sign up: https://telega.io/c/dataScienceT
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If the topic of your post fits our channel, we will publish it with pleasure.
Perhaps you have thought about placing ads on it?
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3) Create an advertising post
If the topic of your post fits our channel, we will publish it with pleasure.
π4
π Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
Π‘hat & pretrained large audio language model proposed by Alibaba Cloud.
π± Github: https://github.com/qwenlm/qwen-audio
π Demo: https://qwen-audio.github.io/Qwen-Audio/
π Paper: https://arxiv.org/abs/2311.07919v1
β© Dataset: https://paperswithcode.com/dataset/vocalsound
https://t.iss.one/DataScienceT
Π‘hat & pretrained large audio language model proposed by Alibaba Cloud.
π± Github: https://github.com/qwenlm/qwen-audio
π Demo: https://qwen-audio.github.io/Qwen-Audio/
π Paper: https://arxiv.org/abs/2311.07919v1
β© Dataset: https://paperswithcode.com/dataset/vocalsound
https://t.iss.one/DataScienceT
π3
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π’ Introducing Emu Video and Emu Edit, our latest generative AI research milestones
π Meta: https://ai.meta.com/blog/emu-text-to-video-generation-image-editing-research/
βοΈProject page: https://emu-edit.iss.onetademolab.com
πPaper: https://emu-edit.iss.onetademolab.com/assets/emu_edit.pdf
https://t.iss.one/DataScienceT
π Meta: https://ai.meta.com/blog/emu-text-to-video-generation-image-editing-research/
βοΈProject page: https://emu-edit.iss.onetademolab.com
πPaper: https://emu-edit.iss.onetademolab.com/assets/emu_edit.pdf
https://t.iss.one/DataScienceT
π΄Data Science A-Zβ’: Hands-On Exercises & ChatGPT Bonus [2023]π΄
Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!
Price :- 120$ - 20$
Price: 20$
Contact @hussein_sheikho
Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!
Price: 20$
Contact @hussein_sheikho
π2
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π¦ Makani: Massively parallel training of machine-learning based weather and climate models
π±Github: https://github.com/NVIDIA/makani
πBlog: https://developer.nvidia.com/blog/modeling-earths-atmosphere-with-spherical-fourier-neural-operators/
β© Dataset: https://github.com/NVIDIA/makani/tree/main/datasets
π±Github: https://github.com/NVIDIA/makani
πBlog: https://developer.nvidia.com/blog/modeling-earths-atmosphere-with-spherical-fourier-neural-operators/
β© Dataset: https://github.com/NVIDIA/makani/tree/main/datasets
π4β€1
Inherently Interpretable Time Series Classification via Multiple Instance Learning (MILLET)
π₯ Github: https://github.com/jaearly/miltimeseriesclassification
π Paper: https://arxiv.org/pdf/2311.10049v1.pdf
β¨ Tasks: https://paperswithcode.com/task/decision-making
https://t.iss.one/DataScienceT
π₯ Github: https://github.com/jaearly/miltimeseriesclassification
π Paper: https://arxiv.org/pdf/2311.10049v1.pdf
β¨ Tasks: https://paperswithcode.com/task/decision-making
https://t.iss.one/DataScienceT
π4
β SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks
π₯ Github: https://github.com/OpenGVLab/SAM-Med2D
π₯ Colab: https://colab.research.google.com/github/OpenGVLab/SAM-Med2D/blob/main/predictor_example.ipynb
π Paper: https://arxiv.org/abs/2311.11969v1
βοΈ Dataset: https://arxiv.org/abs/2311.11969
π₯ Github: https://github.com/OpenGVLab/SAM-Med2D
π₯ Colab: https://colab.research.google.com/github/OpenGVLab/SAM-Med2D/blob/main/predictor_example.ipynb
π Paper: https://arxiv.org/abs/2311.11969v1
βοΈ Dataset: https://arxiv.org/abs/2311.11969
π7β€2
π¬ ShareGPT4V:Improving Large Multi-Modal Models with Better Captions
π₯ Code: https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V
π¦Ύ Project: https://sharegpt4v.github.io/
β‘οΈ Demo: https://huggingface.co/spaces/Lin-Chen/ShareGPT4V-7B
π Paper: https://arxiv.org/pdf/2311.12793.pdf
π Dataset: https://huggingface.co/datasets/Lin-Chen/ShareGPT4V
π₯ Code: https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V
π¦Ύ Project: https://sharegpt4v.github.io/
β‘οΈ Demo: https://huggingface.co/spaces/Lin-Chen/ShareGPT4V-7B
π Paper: https://arxiv.org/pdf/2311.12793.pdf
π Dataset: https://huggingface.co/datasets/Lin-Chen/ShareGPT4V
π2
minimax: Efficient Baselines for Autocurricula in JAX
π₯ Github: https://github.com/facebookresearch/minimax
π Paper: https://arxiv.org/pdf/2311.12716v1.pdf
β¨ Tasks: https://paperswithcode.com/task/decision-making
https://t.iss.one/DataScienceT
π₯ Github: https://github.com/facebookresearch/minimax
π Paper: https://arxiv.org/pdf/2311.12716v1.pdf
β¨ Tasks: https://paperswithcode.com/task/decision-making
https://t.iss.one/DataScienceT
π3