Machine learning books and papers

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Sentiment analysis (SA) is a computational analysis of ideas, feelings and opinions, and uses natural language processing techniques, computational techniques and text analyses to extract polarity (positive, negative or neutral) from non-structured documents or textual comments. the purpose of the multi-domain SA is that the classifier training is based on a set of labelled data in a way that reduces the need for large amounts of data on specific domains and to address the challenges of data scarcity in them with the help of existing data on other domains. The purpose of this paper is to present a new method for analysing the Persian multi-domain SA using DL approaches. The proposed Bi-GRUCapsule approach uses the combination of two networks Bi-GRU and CapsuleNet to solve the multi-domain SA problem that Bi-GRU has the role of extracting features for CapsuleNet. the proposed approach was evaluated using the Digikala dataset and has received acceptable accuracy compared to the existing approaches.

📑 Extreme Zero-Shot Learning for Extreme Text Classification Github: https://github.com/amzn/pecos Paper: https://arxiv.org/abs/2112.08652v1 @Machine_learn

📑 Extreme Zero-Shot Learning for Extreme Text Classification Github: https://github.com/amzn/pecos Paper: https://arxiv.org/abs/2112.08652v1 @Machine_learn

Training Machine Learning Models More Efficiently with Dataset Distillation http://ai.googleblog.com/2021/12/training-machine-learning-models-more.html @Machine_learn

#تخفیف_تاامشب 50%

#تخفیف_تاامشب 50%

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🕷 Bayesian Active Learning (BaaL) Github: https://github.com/ElementAI/baal Documentation: https://baal.readthedocs.io. Paper: https://arxiv.org/abs/2112.06586v1 Blog: https://www.elementai.com/news/2019/element-ai-makes-its-bayesian-active-learning-library-open-source @Machine_learn

🎓 GAN-Supervised Dense Visual Alignment Github: https://github.com/wpeebles/gangealing Project: https://www.wpeebles.com/gangealing Paper: https://arxiv.org/abs/2112.04894v1 Dataset: https://paperswithcode.com/dataset/celeba @Machine_learn

Movie Name Generation Using GPT-2 https://www.nbshare.io/notebook/976197999/Movie-Name-Generation-Using-GPT-2/ @Machine_learn

General and Scalable Parallelization for Neural Networks http://ai.googleblog.com/2021/12/general-and-scalable-parallelization.html @Machine_learn

Improving Vision Transformer Efficiency and Accuracy by Learning to Tokenize http://ai.googleblog.com/2021/12/improving-vision-transformer-efficiency.html @Machine_learn

A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents Github: https://github.com/extreme-classification/deepxml Paper: https://arxiv.org/abs/2111.06685v1 Dataset: https://paperswithcode.com/dataset/extreme-classification @Machine_learn

🔹 LUMINOUS: Indoor Scene Generation for Embodied AI Challenges Github: https://github.com/amazon-research/indoor-scene-generation-eai Paper: https://arxiv.org/abs/2111.05527v1 Dataset: https://paperswithcode.com/dataset/ai2-thor @Machine_learn

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review Github: https://github.com/Ildaron/Laser_control Paper: https://www.mdpi.com/2072-4292/13/21/4486 @Machine_learn