AI Experts in 2021
Reading difficulty: 🟩◼️◼️
This roadmap is very useful to understand if we have all the knowledge to become AI experts.
It is interesting to check if we are forgetting something important.
Reading difficulty: 🟩◼️◼️
This roadmap is very useful to understand if we have all the knowledge to become AI experts.
It is interesting to check if we are forgetting something important.
AI Roadmap
Follow these roadmaps to become an Artificial Intelligence expert.
Classifying emotions using audio recordings
Reading difficulty: 🟧🟧◼️
Today we propose an article, precisely a tutorial, that explains how to classify emotions using Python and CNN. Hope you enjoy it!
https://towardsdatascience.com/classifying-emotions-using-audio-recordings-and-python-434e748a95eb
Reading difficulty: 🟧🟧◼️
Today we propose an article, precisely a tutorial, that explains how to classify emotions using Python and CNN. Hope you enjoy it!
https://towardsdatascience.com/classifying-emotions-using-audio-recordings-and-python-434e748a95eb
Medium
Classifying emotions using audio recordings and Python
The theory of Sound and Acoustics could be used for cutting edge Emotion-Recognition technologies. here’s how we can apply it using Python
DDPM - Diffusion Models Beat GANs on Image Synthesis (NEW!)
Reading difficulty: 🟧🟧◼️
GANs have dominated the image generation space for the main part of the last decade.
This paper shows for the first time, how a non-GAN model, a DDPM, can be improved to overtake GANs at standard evaluation metrics for image generation. The produced samples look amazing and other than GANs, the new model has a formal probabilistic foundation. Is there a future for GANs or are Diffusion Models going to overtake them for good?
If you are looking for someone to help you read this paper I recommend Yannic's video, as always.
We suggest you first to read about Diffision Models (also explained in the Yannic's video).
Then the actual paper 📚: https://arxiv.org/pdf/2105.05233
And obviously keep an eye on the code to understand the details 💻: https://github.com/openai/guided-diffusion
Reading difficulty: 🟧🟧◼️
GANs have dominated the image generation space for the main part of the last decade.
This paper shows for the first time, how a non-GAN model, a DDPM, can be improved to overtake GANs at standard evaluation metrics for image generation. The produced samples look amazing and other than GANs, the new model has a formal probabilistic foundation. Is there a future for GANs or are Diffusion Models going to overtake them for good?
If you are looking for someone to help you read this paper I recommend Yannic's video, as always.
We suggest you first to read about Diffision Models (also explained in the Yannic's video).
Then the actual paper 📚: https://arxiv.org/pdf/2105.05233
And obviously keep an eye on the code to understand the details 💻: https://github.com/openai/guided-diffusion
Siamese Neural Networks: an overview
Reading difficulty: 🟧🟧◼️
A general consideration of what Siamese networks are and how useful they might be in dynamic contexts that require what is known as One Shot Learning
https://www.mygreatlearning.com/blog/siamese-networks/
Here you can see a practical example applied to the well-known Omniglot Dataset for the use of Siamese Neural Networks, using Keras as a framework.
https://towardsdatascience.com/one-shot-learning-with-siamese-networks-using-keras-17f34e75bb3d
Reading difficulty: 🟧🟧◼️
A general consideration of what Siamese networks are and how useful they might be in dynamic contexts that require what is known as One Shot Learning
https://www.mygreatlearning.com/blog/siamese-networks/
Here you can see a practical example applied to the well-known Omniglot Dataset for the use of Siamese Neural Networks, using Keras as a framework.
https://towardsdatascience.com/one-shot-learning-with-siamese-networks-using-keras-17f34e75bb3d
Great Learning Blog: Free Resources what Matters to shape your Career!
Similarity Learning with Siamese Networks
Siamese network is an artificial neural network that is used to find out how similar two objects are when comapring them with each other.
gMLP: Pay Attention to MLPs
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🔍 We've been discussing Transformers a lot on this channel, and the latest publications are pointing out more and more that, what's important in Transformers, are more MLPs and not Attention modules.
Here is another study from Google Research that shows that the attention module can be removed from the Transformers architecture.
The proposed architecture is called gMLP, based only on MLPs with gating (that's what the "g" stands for), and it shows that it can perform just like Transformers in language and vision applications.
⚡️ With 66% fewer parameters, a gMLP model is 3% more accurate than MLP-Mixer.
If you'd like to first understand how transformers work here's a video for you: https://www.youtube.com/watch?v=OyFJWRnt_AY
📚 Paper: https://arxiv.org/pdf/2105.08050.pdf
Reading difficulty: 🟧🟧◼️
🔍 We've been discussing Transformers a lot on this channel, and the latest publications are pointing out more and more that, what's important in Transformers, are more MLPs and not Attention modules.
Here is another study from Google Research that shows that the attention module can be removed from the Transformers architecture.
The proposed architecture is called gMLP, based only on MLPs with gating (that's what the "g" stands for), and it shows that it can perform just like Transformers in language and vision applications.
⚡️ With 66% fewer parameters, a gMLP model is 3% more accurate than MLP-Mixer.
If you'd like to first understand how transformers work here's a video for you: https://www.youtube.com/watch?v=OyFJWRnt_AY
📚 Paper: https://arxiv.org/pdf/2105.08050.pdf
CodeNet: IBM releases 14M sample coding dataset for "AI for code"
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IBM Research released Project CodeNet, a dataset of 14 million code samples to train machine learning models for programming tasks.
⚡️ Key highlights:
- Largest coding dataset gathered yet (4,000 problems, 14 million code samples, 50+ languages)
- The dataset has been annotated (problem description, memory/time limit, language, success, errors, etc.)
Possible uses:
- Translation from one programming language to another
- Code recommendation/completion
- Code optimization
💻 GitHub: https://github.com/IBM/Project_CodeNet
🔍 IBM blog: https://research.ibm.com/blog/codenet-ai-for-code
@deeplearning_material
Reading difficulty: 🟩◼️◼️
IBM Research released Project CodeNet, a dataset of 14 million code samples to train machine learning models for programming tasks.
⚡️ Key highlights:
- Largest coding dataset gathered yet (4,000 problems, 14 million code samples, 50+ languages)
- The dataset has been annotated (problem description, memory/time limit, language, success, errors, etc.)
Possible uses:
- Translation from one programming language to another
- Code recommendation/completion
- Code optimization
💻 GitHub: https://github.com/IBM/Project_CodeNet
🔍 IBM blog: https://research.ibm.com/blog/codenet-ai-for-code
@deeplearning_material
AAAI 2020 Keynotes
Reading difficulty: 🟩◼️◼️
The AAAI Conference (Association for the Advancement of Artificial Intelligence) is an important forum to talk about Artificial Intelligence and Deep Learning.
In February 2020 some big bosses of these fields met to discuss about problems, challenges and opportunities of Deep Learning for the next years.
💣 Yan LeCun presented three challenges of Deep Learning:
1. Learning with fewer labeled samples
2. Learning to reason
3. Learning to plan complex action sequences
📚 Geoff Hinton deals with Convolutional Neural Networks:
1. CNNs not good at dealing with rotation or scaling
2. CNNs do not understand images in terms of objects and their parts
3. CNNs are brittle against adversarial examples
🔎 Yoshua Bengio talks about:
1. Should generalize faster from fewer examples
2. Learn better models from the world, like common sense
3. Get better at "System 2" thinking (slower, methodological thinking as opposed to fast recognition)
Remarkable is the participation of Daniel Kahneman; that I suggest you to follow. It's a very interesting talk. I give you the link of the presentation, enjoy it 🤩.
https://aaai.org/Conferences/AAAI-20/livestreamed-talks/
Reading difficulty: 🟩◼️◼️
The AAAI Conference (Association for the Advancement of Artificial Intelligence) is an important forum to talk about Artificial Intelligence and Deep Learning.
In February 2020 some big bosses of these fields met to discuss about problems, challenges and opportunities of Deep Learning for the next years.
💣 Yan LeCun presented three challenges of Deep Learning:
1. Learning with fewer labeled samples
2. Learning to reason
3. Learning to plan complex action sequences
📚 Geoff Hinton deals with Convolutional Neural Networks:
1. CNNs not good at dealing with rotation or scaling
2. CNNs do not understand images in terms of objects and their parts
3. CNNs are brittle against adversarial examples
🔎 Yoshua Bengio talks about:
1. Should generalize faster from fewer examples
2. Learn better models from the world, like common sense
3. Get better at "System 2" thinking (slower, methodological thinking as opposed to fast recognition)
Remarkable is the participation of Daniel Kahneman; that I suggest you to follow. It's a very interesting talk. I give you the link of the presentation, enjoy it 🤩.
https://aaai.org/Conferences/AAAI-20/livestreamed-talks/
What type of content would you like most?
Final Results
50%
Last papers and publications
68%
Blog and tutorial links
45%
News (AI and Deep Learning)
SETR: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Reading difficulty: 🟧🟧◼️
By reformulating the semantic segmentation problem from a sequence-to-sequence learning perspective, SETR proposes to replace the stacked convolution layers-based encoder with a pure transformer to encode an image as a sequence of patches. This transformer-based encoder can be combined with a simple decoder to provide a powerful segmentation model.
📚 Paper (to appear at CVPR 2021): https://arxiv.org/pdf/2012.15840.pdf
🔍 Web Site: https://fudan-zvg.github.io/SETR
💻 GitHub: https://github.com/fudan-zvg/SETR
Reading difficulty: 🟧🟧◼️
By reformulating the semantic segmentation problem from a sequence-to-sequence learning perspective, SETR proposes to replace the stacked convolution layers-based encoder with a pure transformer to encode an image as a sequence of patches. This transformer-based encoder can be combined with a simple decoder to provide a powerful segmentation model.
📚 Paper (to appear at CVPR 2021): https://arxiv.org/pdf/2012.15840.pdf
🔍 Web Site: https://fudan-zvg.github.io/SETR
💻 GitHub: https://github.com/fudan-zvg/SETR
Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis
Reading difficulty 🟧🟧◼️
The world of XAI is quickly making some steps forward and getting some good results, especially when it comes to visualize the decision of a CNN for classification!
The first good idea to "explain" a CNN for classification is to find a good and interpretable method (named Attribution Method) to give each input pixel its relevance w.r.t the final output (a sort of saliency map of the CNN).
The most common choices to build Attribution Methods are now concerning three approaches:
- Gradient based approach
- Perturbation based approach
- CAM based approach (often combined with one of the previous)
But each of them is giving very different results in terms of conceptual meaning and correctness of the method. Some questions are definitely still open points and need to find an answer.
My collaborators and me have tried to find a more stable and unbiased benchmark to evaluate firstly the CAM based methods and then the others, with the aim of finding a good abstraction of what should be the Ideal Attribution Method, and which properties it should have.
The importance of XAI is getting more and more relevance as the AI breaks through everyone's life!
If you'd like to know more details this paper will appear at CVPR 2021 (RCV workshop). I'll be presenting it personally and I'll be more than glad to talk about it!
📚Paper (to appear at CVPR 2021, RCV workshop): https://arxiv.org/abs/2104.10252
Reading difficulty 🟧🟧◼️
The world of XAI is quickly making some steps forward and getting some good results, especially when it comes to visualize the decision of a CNN for classification!
The first good idea to "explain" a CNN for classification is to find a good and interpretable method (named Attribution Method) to give each input pixel its relevance w.r.t the final output (a sort of saliency map of the CNN).
The most common choices to build Attribution Methods are now concerning three approaches:
- Gradient based approach
- Perturbation based approach
- CAM based approach (often combined with one of the previous)
But each of them is giving very different results in terms of conceptual meaning and correctness of the method. Some questions are definitely still open points and need to find an answer.
My collaborators and me have tried to find a more stable and unbiased benchmark to evaluate firstly the CAM based methods and then the others, with the aim of finding a good abstraction of what should be the Ideal Attribution Method, and which properties it should have.
The importance of XAI is getting more and more relevance as the AI breaks through everyone's life!
If you'd like to know more details this paper will appear at CVPR 2021 (RCV workshop). I'll be presenting it personally and I'll be more than glad to talk about it!
📚Paper (to appear at CVPR 2021, RCV workshop): https://arxiv.org/abs/2104.10252
Is it difficult for you to find the right scientific papers? Not anymore
Reading difficulty: 🟩◼️◼️
Connected Papers is a fantastic tool published one year ago that aims at helping researchers visually find and explore academic papers.
📖 Input: a paper that you like
🔗 Output: a full interactive map of similar papers to explore
So, just open the following link and use it! ⚡️
Tool link: https://www.connectedpapers.com/
Reading difficulty: 🟩◼️◼️
Connected Papers is a fantastic tool published one year ago that aims at helping researchers visually find and explore academic papers.
📖 Input: a paper that you like
🔗 Output: a full interactive map of similar papers to explore
So, just open the following link and use it! ⚡️
Tool link: https://www.connectedpapers.com/
Connectedpapers
Connected Papers | Find and explore academic papers
A unique, visual tool to help researchers and applied scientists find and explore papers relevant to their field of work.