You are free to share this channel with whomever you wish.
Useful material will be shared in this channel to deepen your knowledge on the topic of deep learning. So that you can stay up to date with all the scientific advances in this field.
Useful material will be shared in this channel to deepen your knowledge on the topic of deep learning. So that you can stay up to date with all the scientific advances in this field.
Obviously this channel was created because I get bored of sharing the same (awesome) links to all my friends.
This link contains a lot of useful lectures. Also on the topic of AutoDiff and Transformers.
https://github.com/glouppe/info8010-deep-learning#agenda
https://github.com/glouppe/info8010-deep-learning#agenda
GitHub
GitHub - glouppe/info8010-deep-learning: Lectures for INFO8010 Deep Learning, ULiège
Lectures for INFO8010 Deep Learning, ULiège. Contribute to glouppe/info8010-deep-learning development by creating an account on GitHub.
Why AI is Harder Than We Think
Reading difficulty: 🟩◼️◼️
This video by Yannic Kilcher explains the paper "Why AI is harder than we think", which suggests that something fundamental is still missing to achieve General AI that is capable of reaching the human method of reasoning. I recommend viewing.
https://www.youtube.com/watch?v=uwfVxckuq50
Reading difficulty: 🟩◼️◼️
This video by Yannic Kilcher explains the paper "Why AI is harder than we think", which suggests that something fundamental is still missing to achieve General AI that is capable of reaching the human method of reasoning. I recommend viewing.
https://www.youtube.com/watch?v=uwfVxckuq50
YouTube
Why AI is Harder Than We Think (Machine Learning Research Paper Explained)
#aiwinter #agi #embodiedcognition
The AI community has gone through regular cycles of AI Springs, where rapid progress gave rise to massive overconfidence, high funding, and overpromise, followed by these promises being unfulfilled, subsequently diving into…
The AI community has gone through regular cycles of AI Springs, where rapid progress gave rise to massive overconfidence, high funding, and overpromise, followed by these promises being unfulfilled, subsequently diving into…
For those of you who are working with Transformers, check out this webinar (26/5/2021)
https://picampus.it/events/efficient-transformers/
https://picampus.it/events/efficient-transformers/
Pi Campus Rome
Efficient Transformers: an AI Webinar with Lukasz Kaiser
Lukasz will introduce a new efficient variant of Transformers and show how they address the main problems that previously limited the use of some Transformers.
If we take attention away from transformers, what's left? An old dear and elegant masked ensemble
Do You Even Need Attention? A Stack of Feed-Forward Layers Does
Surprisingly Well on ImageNet
Reading difficulty: 🟧🟧◼️
https://arxiv.org/pdf/2105.02723.pdf
Do You Even Need Attention? A Stack of Feed-Forward Layers Does
Surprisingly Well on ImageNet
Reading difficulty: 🟧🟧◼️
https://arxiv.org/pdf/2105.02723.pdf
MLP-Mixer: An all-MLP Architecture for Vision
Reading difficulty: 🟧🟧◼️
Very similar to the previous paper there is also MLP-Mixer from Google Research: https://arxiv.org/pdf/2105.01601.pdf
Both papers show that the Vision Transformer can be reduced to just using MLPs while achieving competitive performance.
A very straightforward explanation can be found in this video: https://www.youtube.com/watch?v=7K4Z8RqjWIk
Reading difficulty: 🟧🟧◼️
Very similar to the previous paper there is also MLP-Mixer from Google Research: https://arxiv.org/pdf/2105.01601.pdf
Both papers show that the Vision Transformer can be reduced to just using MLPs while achieving competitive performance.
A very straightforward explanation can be found in this video: https://www.youtube.com/watch?v=7K4Z8RqjWIk
YouTube
MLP-Mixer: An all-MLP Architecture for Vision (Machine Learning Research Paper Explained)
#mixer #google #imagenet
Convolutional Neural Networks have dominated computer vision for nearly 10 years, and that might finally come to an end. First, Vision Transformers (ViT) have shown remarkable performance, and now even simple MLP-based models reach…
Convolutional Neural Networks have dominated computer vision for nearly 10 years, and that might finally come to an end. First, Vision Transformers (ViT) have shown remarkable performance, and now even simple MLP-based models reach…
"Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis"
Reading difficulty: 🟧🟧◼️
📚 https://arxiv.org/pdf/1806.04558.pdf 📚 https://google.github.io/tacotron/publications/speaker_adaptation
Something interesting done some years ago in the TTS (Text To Speech) field. Have you ever asked yourself - "How much time is needed, for a machine, to imitate my voice?" Near 5 seconds!
In this paper (first link) the architectural proposal uses 3 main components:
1) Speaker encoder network: it captures, in an embedding space, the characteristics of several and different speakers. It's the most important part of the entire architecture because its work is to influence the Mel spectrogram generated by the synthesizer.
2) Synthesizer network: trained on pairs of text transcript and target audio to produce a synthesized Mel spectrogram.
3) Vocoder (WaveNet): useful to invert spectrograms into time domain waveforms.
A possible application of this technology? Transferring a voice across languages for more natural speech-to-speech translation.
Reading difficulty: 🟧🟧◼️
📚 https://arxiv.org/pdf/1806.04558.pdf 📚 https://google.github.io/tacotron/publications/speaker_adaptation
Something interesting done some years ago in the TTS (Text To Speech) field. Have you ever asked yourself - "How much time is needed, for a machine, to imitate my voice?" Near 5 seconds!
In this paper (first link) the architectural proposal uses 3 main components:
1) Speaker encoder network: it captures, in an embedding space, the characteristics of several and different speakers. It's the most important part of the entire architecture because its work is to influence the Mel spectrogram generated by the synthesizer.
2) Synthesizer network: trained on pairs of text transcript and target audio to produce a synthesized Mel spectrogram.
3) Vocoder (WaveNet): useful to invert spectrograms into time domain waveforms.
A possible application of this technology? Transferring a voice across languages for more natural speech-to-speech translation.
Dataset trick: COCO (Common Objects in Context)
Reading difficulty: 🟩◼️◼️
Hi everyone, this evening we propose you some practical tricks to work and to enjoy with COCO.
What is COCO? COCO is a large Microsoft image dataset designed for many tasks like:
🔎 Object detection
🔎 Segmentation
🔎 Person keypoints detection
🔎 Stuff segmentation
🔎 Caption generation
Obviously COCO has several annotation types, one for each task to perform, stored using JSON format. It may be difficult to use them and easily switch from one to another, so Python helps us in order to do that and visualize what happens.
pycocotools is a Python API that assists in loading, parsing and visualizing the annotations in COCO. A very simple and practical tutorial shows how to use it https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoDemo.ipynb
The tool is really interesting because due to the popularity of the dataset, the format that COCO uses to store annotations is often the go-to format when creating a new custom object detection dataset or simply find one that follow the same baseline.
We suggest you to open the tutorial and to try to interact with it. It's funny 😆.
COCO homepage -> https://cocodataset.org/#home
COCO annotation format -> https://cocodataset.org/#format-data
COCO paper -> https://arxiv.org/pdf/1405.0312.pdf
COCO download -> https://cocodataset.org/#download
pycocotools -> https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/coco.py
Reading difficulty: 🟩◼️◼️
Hi everyone, this evening we propose you some practical tricks to work and to enjoy with COCO.
What is COCO? COCO is a large Microsoft image dataset designed for many tasks like:
🔎 Object detection
🔎 Segmentation
🔎 Person keypoints detection
🔎 Stuff segmentation
🔎 Caption generation
Obviously COCO has several annotation types, one for each task to perform, stored using JSON format. It may be difficult to use them and easily switch from one to another, so Python helps us in order to do that and visualize what happens.
pycocotools is a Python API that assists in loading, parsing and visualizing the annotations in COCO. A very simple and practical tutorial shows how to use it https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoDemo.ipynb
The tool is really interesting because due to the popularity of the dataset, the format that COCO uses to store annotations is often the go-to format when creating a new custom object detection dataset or simply find one that follow the same baseline.
We suggest you to open the tutorial and to try to interact with it. It's funny 😆.
COCO homepage -> https://cocodataset.org/#home
COCO annotation format -> https://cocodataset.org/#format-data
COCO paper -> https://arxiv.org/pdf/1405.0312.pdf
COCO download -> https://cocodataset.org/#download
pycocotools -> https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/coco.py
GitHub
cocoapi/PythonAPI/pycocoDemo.ipynb at master · cocodataset/cocoapi
COCO API - Dataset @ http://cocodataset.org/ . Contribute to cocodataset/cocoapi development by creating an account on GitHub.