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Lecture 10.5 — Dropout — [ Deep Learning | Geoffrey Hinton | Toronto ]

September 25, 2017

🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (https://twitter.com/mtnayeem) on Twitter 🐦 for real-time updates, news, and discussions in the field.

Check out the following interesting papers. Happy learning!

Paper Title: "On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction" Paper: https://aclanthology.org/2023.findings-eacl.125/ Dataset: https://huggingface.co/datasets/tafseer-nayeem/review_helpfulness_prediction

Paper Title: "Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion" Paper: https://aclanthology.org/C18-1102/

Paper Title: "Extract with Order for Coherent Multi-Document Summarization" Paper: https://aclanthology.org/W17-2407.pdf

Paper Title: "Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation" Paper: https://dl.acm.org/doi/abs/10.1145/3132847.3133106

Paper Title: "Neural Diverse Abstractive Sentence Compression Generation" Paper: https://link.springer.com/chapter/10.1007/978-3-030-15719-7_14

Recommended Further Browsing

Lecture 2.5 — What perceptrons cant do — [ Deep Learning | Geoffrey Hinton | UofT ]

Lecture 2.5 — What perceptrons cant do — [ Deep Learning | Geoffrey Hinton | UofT ]

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Lecture 14.5 — RBMs are infinite sigmoid belief nets — [ Deep Learning | Geoffrey Hinton | UofT ]

Lecture 14.5 — RBMs are infinite sigmoid belief nets — [ Deep Learning | Geoffrey Hinton | UofT ]

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Lecture 10.1 — Why it helps to combine models — [ Deep Learning | Geoffrey Hinton | UofT ]

Lecture 10.1 — Why it helps to combine models — [ Deep Learning | Geoffrey Hinton | UofT ]

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Lecture 11.4 — Using stochastic units to improve search — [ Deep Learning | Geoffrey Hinton | UofT ]

Lecture 11.4 — Using stochastic units to improve search — [ Deep Learning | Geoffrey Hinton | UofT ]

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