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Isn’t there a way to get the best out of both Convolutional Neural Networks and Vision Transformers? Davide Coccomini sh...
31/01/2022

Isn’t there a way to get the best out of both Convolutional Neural Networks and Vision Transformers? Davide Coccomini shows how these two architectures can be combined to obtain something capable of exploiting the positive sides of both!
https://www.topbots.com/vision-transformers-with-convolutional-neural-networks/?utm_source=facebook&utm_medium=company_post&utm_campaign=vision_transformers_convolutional_neural_networks

Lucky for us, CNNs and VIsion Transformers can be combined in many different ways to exploit the positive sides of both!

Transformer-based language models demonstrate impressive performance in text generation but controlling their output rem...
30/01/2022

Transformer-based language models demonstrate impressive performance in text generation but controlling their output remains challenging. In his recent project, Rohola Zandie addresses the problem of controlled text generation without the massive infrastructure to train it!
https://www.topbots.com/topical-language-generation-with-transformers/?utm_source=facebook&utm_medium=company_post&utm_campaign=topical_language_generation_transformers

We introduce Topical Language Generation (TLG) by combining a pre-trained LM with topic modeling information. We cast the problem using Bayesian probability formulation.

The article by Ravi Charan discusses perplexity, entropy, and the relation between the two. Learn also how perplexity ar...
27/01/2022

The article by Ravi Charan discusses perplexity, entropy, and the relation between the two. Learn also how perplexity arises naturally in natural language processing applications.

https://www.topbots.com/perplexity-and-entropy-in-nlp//?utm_source=facebook&utm_medium=company_post&utm_campaign=perplexity_and_entropy_in_nlp

Perplexity is a common metric to use when evaluating language models. For example, scikit-learn’s implementation of Latent Dirichlet Allocation (a topic-modeling algorithm) includes perplexity as a built-in metric. In this post, I will define perplexity and then discuss entropy, the relation bet...

Even knowing basic probability theory, you may have a hard time understanding and connecting that to modern Bayesian dee...
27/01/2022

Even knowing basic probability theory, you may have a hard time understanding and connecting that to modern Bayesian deep learning research. In this article, Joris Baan aims to bridge that gap and provide a comprehensive introduction.
https://www.topbots.com/comprehensive-introduction-to-bayesian-deep-learning/?utm_source=facebook&utm_medium=company_post&utm_campaign=bayesian_deep_learning

Even knowing basic probability theory, you may find it hard to understand and connect that to modern Bayesian deep learning research. This blogpost bridges this gap and provides a comprehensive introduction.

We all love big language models, but there are some dark sides we should be aware of. Here's an overview of the research...
26/01/2022

We all love big language models, but there are some dark sides we should be aware of. Here's an overview of the research paper showing that language models like BERT and GPT-2 can generate hurtful statements – not only in English but across different languages.
https://www.topbots.com/can-too-much-bert-be-bad-for-you/?utm_source=facebook&utm_medium=company_post&utm_campaign=can_too_much_bert_bad_for_you

Here's a summary of the NAACL 2021 research paper "HONEST: Measuring hurtful sentence completion in language models."

Duncan Wang and his teammates describe their Convolutional Neural Network model to recognize and classify different type...
25/01/2022

Duncan Wang and his teammates describe their Convolutional Neural Network model to recognize and classify different types of waste items! If you’ve ever wondered how AI can help us recycle, this is it.
https://www.topbots.com/how-ai-can-help-us-recycle/?utm_source=facebook&utm_medium=company_post&utm_campaign=how_ai_help_us_recycle

One particular difficulty with recycling is the issue of improper sorting, and AI can help with this. Image classification can be applied to identify and help consumers recognize the material composition and consequent recyclability of their waste items.

In this article, Davide Coccomini suggests how machine learning can help to solve different problems related to climate ...
25/01/2022

In this article, Davide Coccomini suggests how machine learning can help to solve different problems related to climate change. For every topic, he also lists useful datasets which may be a good starting point for your project.
https://www.topbots.com/machine-learning-climate-change-response/?utm_source=facebook&utm_medium=company_post&utm_campaign=machine_learning_experts_responds_climage_change

Here are some ideas and starting points for a machine learning-based response to climate change. The possible innovations are virtually limitless.

Matthew Stewart from Harvard University discusses the existing methods for increasing fairness in AI. In addition to bas...
24/01/2022

Matthew Stewart from Harvard University discusses the existing methods for increasing fairness in AI. In addition to basic techniques such as statistical parity, equalized odds, and equality of opportunity, human-in-the-loop and algorithmic transparency are some of the most promising recent solutions.

https://www.topbots.com/programming-fairness-in-algorithms//?utm_source=facebook&utm_medium=company_post&utm_campaign=programming_fairness_in_algorithms

Being good is easy, what is difficult is being just. ― Victor Hugo We need to defend the interests of those whom we’ve never met and never will. ― Jeffrey D. Sachs Note: This article is intended for a general audience to try and elucidate the complicated nature of unfairness in machine learni...

In this interpretability guide, Matthew Stewart from Harvard University explains how well-chosen visualizations help pra...
20/01/2022

In this interpretability guide, Matthew Stewart from Harvard University explains how well-chosen visualizations help practitioners understand the decision-making process of deep learning models and their learned representations.

https://www.topbots.com/interpretable-machine-learning//?utm_source=facebook&utm_medium=company_post&utm_campaign=interpretable_ml

If you can’t explain it simply, you don’t understand it well enough. — Albert Einstein Disclaimer: This article draws and expands upon material from (1) Christoph Molnar’s excellent book on Interpretable Machine Learning which I definitely recommend to the curious reader, (2) a deep lear...

Graph Neural Networks (GNNs) have emerged as the standard toolbox to learn from graph data. Giuseppe Futia demonstrates ...
20/01/2022

Graph Neural Networks (GNNs) have emerged as the standard toolbox to learn from graph data. Giuseppe Futia demonstrates how the learning capacity of GNNs is improved by the attention mechanism, which assigns different importance to each neighbor’s contribution.
https://www.topbots.com/graph-attention-networks-under-the-hood/?utm_source=facebook&utm_medium=company_post&utm_campaign=graph_attention_networks

Let's see how the learning capacity of Graph Neural Networks is improved by the attention mechanism, which assigns different importance to each neighbor's contribution.

Does explainable AI (XAI) have the potential to reinvent marketing analytics? Anurag Pandey discusses challenges associa...
19/01/2022

Does explainable AI (XAI) have the potential to reinvent marketing analytics? Anurag Pandey discusses challenges associated with marketing analytics today and the potential of explainable AI to address these challenges. He also implements XAI on an openly available marketing dataset.
https://www.topbots.com/explainable-ai-marketing-analytics/?utm_source=facebook&utm_medium=company_post&utm_campaign=explainable_ai_marketing_analytics

We will try to develop an intuition about the explainable AI (XAI), and then, we will implement XAI on an openly available marketing dataset.

This article by Giuseppe Futia describes how to extend the simplest formulation of Graph Neural Networks (GNNs) to encod...
19/01/2022

This article by Giuseppe Futia describes how to extend the simplest formulation of Graph Neural Networks (GNNs) to encode the structure of multi-relational data, such as Knowledge Graphs (KGs).
https://www.topbots.com/graph-neural-networks-multi-relational-data/?utm_source=facebook&utm_medium=company_post&utm_campaign=gnn_multi_relational_data

The article describes how to extend the simplest formulation of GNNs to encode the structure of multi-relational data, such as Knowledge Graphs (KGs).

Facebook AI has recently presented a self-supervised method called DINO, defined as a form of self-distillation with no ...
18/01/2022

Facebook AI has recently presented a self-supervised method called DINO, defined as a form of self-distillation with no labels, and used to train a Vision Transformer. Here's an illustrated guide to DINO prepared by Davide Coccomini.
https://www.topbots.com/dino-vision-transformer/?utm_source=facebook&utm_medium=company_post&utm_campaign=dino_self_distillation

Facebook AI presented a self-supervised method called DINO, defined as a form of self-distillation with no labels, and used to train a Vision Transformer.

The calculation of self-attention brings with it huge computational and memory costs. To address this problem, Google Br...
18/01/2022

The calculation of self-attention brings with it huge computational and memory costs. To address this problem, Google Brain has recently introduced Efficient Transformers. Davide Coccomini explains this method with great illustrations.
https://www.topbots.com/attention-in-transformers/?utm_source=facebook&utm_medium=company_post&utm_campaign=attention_in_transformers

The success of the transformers is related to their extreme effectiveness and ability to solve non-trivial problems in a superior way compared to previous architectures in NLP and computer vision.

When we do online experiments or A/B testing, we need to ensure our test has high statistical power so that we can find ...
13/01/2022

When we do online experiments or A/B testing, we need to ensure our test has high statistical power so that we can find the experimental effect if it does exist. In this article, Sophia Yang will walk you through several popular variance reduction methods and demonstrate some simple examples in Python.
https://www.topbots.com/online-experiments-variance-reduction/?utm_source=facebook&utm_medium=company_post&utm_campaign=online_experiments_tricks_variance_reduction

In this article, I will walk through some of the popular variance reduction methods and demonstrate some simple examples in Python.

The article introduces an approach known as probabilistic programming. This method allows us to largely automatize the p...
13/01/2022

The article introduces an approach known as probabilistic programming. This method allows us to largely automatize the process of statistical inference in the models, making it easy to use without having to know all the tricks and intricacies of Bayesian inference in large models.
https://www.topbots.com/variational-methods-in-deep-learning/?utm_source=facebook&utm_medium=company_post&utm_campaign=variational_methods

Probabilistic programming allows us to largely automatize the process of statistical inference in the models, without having to know all the tricks of Bayesian inference in large models.

Interested to learn more about graph ML? In this article, Sid Arcidiacono is breezing through the very basic concepts of...
12/01/2022

Interested to learn more about graph ML? In this article, Sid Arcidiacono is breezing through the very basic concepts of convolutional neural networks to explain graph convolutional nets.
https://www.topbots.com/graph-convolutional-networks-explained/?utm_source=facebook&utm_medium=company_post&utm_campaign=graph_convolutional_networks_explained

In my last article on graph theory, I briefly introduced my latest topic of interest: Graph Convolutional Networks. In this article, we’re going to break this topic down, step by step.

Graphs demonstrate comprehensive relationships between pieces of data, and we can use them to perform in-depth analysis ...
11/01/2022

Graphs demonstrate comprehensive relationships between pieces of data, and we can use them to perform in-depth analysis and make predictions based on these relationships. Read this article to understand the math behind graph convolutional networks.
https://www.topbots.com/math-behind-graph-convolutional-networks/?utm_source=facebook&utm_medium=company_post&utm_campaign=graph_convolutional_networks_work

The goal of the article is to introduce the math behind graph convolutional networks. I'll try to break down the concepts so that anyone can understand.

Autoencoders are neural network models designed to learn complex non-linear relationships between data points. In this p...
10/01/2022

Autoencoders are neural network models designed to learn complex non-linear relationships between data points. In this post, the author gives an overview of the various types of autoencoders developed over the years and their applications.
https://www.topbots.com/autoencoders-research-and-applications/?utm_source=facebook&utm_medium=company_post&utm_campaign=autoencoders_overview

Autoencoders are neural network models designed to learn complex non-linear relationships between data points. Here's an overview of the various types of autoencoders and their applications.

Can we make GANs using Vision Transformers? In this article, Davide Coccomini investigates the creative side of Vision T...
09/01/2022

Can we make GANs using Vision Transformers? In this article, Davide Coccomini investigates the creative side of Vision Transformers with two different model architectures: TransGAN and Paint Transformer.
https://www.topbots.com/the-creative-side-of-vision-transformers/?utm_source=facebook&utm_medium=company_post&utm_campaign=creative_transformers

Vision Transformers-based architectures are today one of the most promising approaches in computer vision. But if they are so great, wouldn’t it be worth using Vision Transformers to make GANs?

What if you need to analyze an SQL database consisting of 500+ tables? Check out this article by Thomas Baumgartner to s...
06/01/2022

What if you need to analyze an SQL database consisting of 500+ tables? Check out this article by Thomas Baumgartner to see how to build a tool in which you can apply various filters and then view the filtered tables and their relations in an easy-to-grasp visual representation.
https://www.topbots.com/visualize-databases-as-network-graphs/?utm_source=facebook&utm_medium=company_post&utm_campaign=visualize_databases_network_graphs_in_python

Here's how to build a Dash web application to visualize your database and explore its structure interactively.

Will AI take "human" out of HR? Not likely. However, it effectively eliminates repetitiveness from the recruiting proces...
05/01/2022

Will AI take "human" out of HR? Not likely. However, it effectively eliminates repetitiveness from the recruiting process. In this article, we feature companies with proven AI and ML expertise that are transforming HR activities with state-of-the-art AI-driven solutions.

https://www.topbots.com/ai-companies-hr-recruiting//?utm_source=facebook&utm_medium=company_post&utm_campaign=ai_companies_revolutionizing_recruiting

AI won’t take the “human” out of HR, but applying novel machine learning capabilities can eliminate repetitive and monotonous activities in recruiting.

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