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The Best of Applied Artificial Intelligence, Machine Learning, Automation, Bots, Chatbots

ErnIe-ViL AAAI 2021

AAAI 2021: Top Research Papers With Business Applications

AI Research Summaries

ErnIe-ViL AAAI 2021

AAAI 2021: Top Research Papers With Business Applications

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2020’s Top AI & Machine Learning Research Papers

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GPT-3 & Beyond: 10 NLP Research Papers You Should Read

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Novel Computer Vision Research Papers From 2020

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The Latest Breakthroughs in Conversational AI Agents

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Best Research Papers From ICML 2020

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open-domain chatbots

State-of-the-art Approaches to Building Open-Domain Conversational Agents

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Key Research Advances in Building Task-Oriented Dialog Agents

dialog datasets

Novel Datasets For Open-Domain & Task-Oriented Dialogs

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New Ways to Evaluate Open-Domain Conversational Agents

Image Synthesis - ImageGAN

Novel AI Approaches For Marketing & Advertising

text generation with AI

AI Approaches For Text Generation In Marketing & Advertising Use Cases

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Novel AI Methods For Video Generation for Marketing & Advertising

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New Ways To Leverage AI Image Synthesis For Marketing & Advertising Content

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text classification

GPT2 For Text Classification Using Hugging Face Transformers

This notebook is used to fine-tune GPT2 model for text classification using Hugging Face transformers library on a custom dataset. Hugging Face is very nice…

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ErnIe-ViL AAAI 2021

AAAI 2021: Top Research Papers With Business Applications

With a record high of 9034 research papers submitted to AAAI 2021 and an acceptance rate of 21%, a total of 1692…

Continue Reading AAAI 2021: Top Research Papers With Business Applications

A Comprehensive Introduction to 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…

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text summarization

Extractive Text Summarization Using Contextual Embeddings

Text Summarization is a process of generating a compact and meaningful synopsis from a huge volume of text. Sources for such text…

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graph neural networks

Graph Neural Networks for Multi-Relational Data

This article describes how to extend the simplest formulation of Graph Neural Networks (GNNs) to encode the structure of multi-relational data, such…

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graph attention networks

Graph Attention Networks Under the Hood

Graph Neural Networks (GNNs) have emerged as the standard toolbox to learn from graph data. GNNs are able to drive improvements for…

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graph transformer

Graph Transformer: A Generalization of Transformers to Graphs

This blog is based on the paper A Generalization of Transformer Networks to Graphs with Xavier Bresson at 2021 AAAI Workshop on Deep Learning on Graphs: Methods…

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autoencoders

Autoencoders: Overview of Research and Applications

Since the early days of machine learning, it has been attempted to learn good representations of data in an unsupervised manner. The…

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probabilistic programming

Variational Methods in Deep Learning

Deep neural networks are a flexible family of models wide applications in AI and other fields. Even though these networks often encompass millions…

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GAN implementation

Step-By-Step Implementation of GANs on Custom Image Data in PyTorch: Part 2

In Part 1 on GANs, we started to build intuition regarding what GANs are, why we need them, and how the entire point behind…

Continue Reading Step-By-Step Implementation of GANs on Custom Image Data in PyTorch: Part 2

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