{"id":17632452,"url":"https://github.com/shashi456/deep-learning-research","last_synced_at":"2025-03-30T03:23:49.654Z","repository":{"id":133649510,"uuid":"138471879","full_name":"Shashi456/Deep-Learning-Research","owner":"Shashi456","description":"Summaries of papers on Deep Learning, Natural Language Processing, Computer vision ","archived":false,"fork":false,"pushed_at":"2023-09-02T07:09:02.000Z","size":2155,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-02-05T05:41:43.906Z","etag":null,"topics":["artificial-intelligence","computer-vision","deep-learning","deep-neural-networks","hacktoberfest","language-modeling","machine-learning","natural-language-processing","neural-networks","nlp","research","text-summarization"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Shashi456.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-06-24T10:11:54.000Z","updated_at":"2023-09-02T07:09:40.000Z","dependencies_parsed_at":null,"dependency_job_id":"76aa0bee-ed6c-48d7-aa53-a87c7a9b5b1c","html_url":"https://github.com/Shashi456/Deep-Learning-Research","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shashi456%2FDeep-Learning-Research","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shashi456%2FDeep-Learning-Research/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shashi456%2FDeep-Learning-Research/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shashi456%2FDeep-Learning-Research/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Shashi456","download_url":"https://codeload.github.com/Shashi456/Deep-Learning-Research/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246271284,"owners_count":20750557,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","computer-vision","deep-learning","deep-neural-networks","hacktoberfest","language-modeling","machine-learning","natural-language-processing","neural-networks","nlp","research","text-summarization"],"created_at":"2024-10-23T01:07:57.254Z","updated_at":"2025-03-30T03:23:49.629Z","avatar_url":"https://github.com/Shashi456.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Research\n\nJust a list of papers i read everyday and notes to keep a track of them. I used to read a variety of papers pre 2023 and you can look at them in the [Pre-2023 section](#pre-2023).\n\n\n# 2nd September 2023\n\n \n\n\n## \u003ca id=\"pre-2023\"\u003e\u003c/a\u003ePre 2023\n# Papers\nThese will either be paper implementations or/and reviews of various papers and notes for conference sessions, I will read/watch over time. I currently research on Abstractive Summarization ( A task within NLP) \n\n\n\n## \u003ca id=\"adversarial-examples\"\u003e\u003c/a\u003eAdversarial Examples\n- Explaining and Harnessing Adversarial Examples [[Paper](https://arxiv.org/pdf/1412.6572v3.pdf)] [[Review](https://github.com/Shashi456/Papers/blob/master/Review/Explaining%20and%20Harnesssing%20Adversarial%20Examples.md)]\n- Intriguing Properties of Neural Networks [[Paper](https://arxiv.org/abs/1312.6199)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/Intriguing%20Properties%20of%20Neural%20Networks.md)]\n- Practical BlackBox attacks against machine learning [[Paper](https://arxiv.org/abs/1602.02697)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/Practical%20Black%20Box%20Attack%20against%20Machine%20Learning.md)]\n\u003c?--The Limitations of deep learning in adversarial settings [[Paper](https://arxiv.org/abs/151\n\u003c!-- ## Adversarial Examples\n- Explaining and Harnessing Adversarial Examples [[Paper](https://arxiv.org/pdf/1412.6572v3.pdf)] [[Review](https://github.com/Shashi456/Papers/blob/master/Review/Explaining%20and%20Harnesssing%20Adversarial%20Examples.md)]\n- Intriguing Properties of Neural Networks [[Paper](https://arxiv.org/abs/1312.6199)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/Intriguing%20Properties%20of%20Neural%20Networks.md)]\n- Practical BlackBox attacks against machine learning [[Paper](https://arxiv.org/abs/1602.02697)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/Practical%20Black%20Box%20Attack%20against%20Machine%20Learning.md)]\n\u003c?--The Limitations of deep learning in adversarial settings [[Paper](https://arxiv.org/abs/1511.07528)][[Review]()])--?\u003e --\u003e\n\n## Neural Style\n- Neural Algorithm of Artistic Style [[Paper](https://arxiv.org/pdf/1508.06576.pdf)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/A%20Neural%20Algorithm%20of%20Artistic%20Style.md)][[Code](https://github.com/Shashi456/Neural-Style)][[Article](https://towardsdatascience.com/neural-style-transfer-series-part-2-91baad306b24)]\n\n## Image Classification\n- Very Deep Convolutional Networks for Large Scale Image Recognition [[Paper](https://arxiv.org/pdf/1409.1556.pdf)][[Review](./Review/VGG.md)]\n\n## One Shot Learning\n- Siamese Neural networks for One-Shot Image Recognition [[Paper](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/Siamese%20Neural%20Networks%20for%20One-shot%20Image%20Recogniton.md)]\n- Learning to compare: Relation Network for Few shot Learning  [[Paper](http://www.robots.ox.ac.uk/~tvg/publications/2018/0431.pdf)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/LTC%20Relation%20Network%20for%20few%20shot%20learning.md)][[Code](https://github.com/Shashi456/Papers/blob/master/Implementations/Learning%20to%20Compare%20-%20One%20shot%20Leanring/One%20Shot%20Classification(2).ipynb)]\n\n\n## Natural Language Processing\n## Sequence to Sequence Learning\n\n- Sequence to Sequence Learning with Neural Networks. [[Paper](https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/Sequence%20to%20Sequence%20Learning%20with%20Neural%20Networks.md)]\n\n\n## Attention Based Models\n- Neural Machine Translation by jointly learning to align and translate [[Paper](https://arxiv.org/abs/1409.0473)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/Neural%20Machine%20Translation%20by%20Jointly%20learning%20to%20align%20and%20translate.md)]\n\n\n## Text Classification\n- Universal Language Model Fine-tuning for Text Classification [[Paper](https://arxiv.org/abs/1801.06146)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/Universal%20Language%20Model%20Fine-Tuning%20for%20Text%20Classification.md)]\n\n\n## Machine Translation\n- Incorporating BERT for Machine Translation. \n[[Paper](https://arxiv.org/abs/2002.06823v1)][[Review](./Review/BERTMT.md)]\n\n## Abstractive Summarization\n- A Neural Attention Model for Abstractive Sentence Summarization [[Paper](https://arxiv.org/pdf/1509.00685.pdf)][[Review](Review/NeuralattnAbs.md)]\n- Abstractive Text Summarization Using Sequence to Sequence RNNs and Beyond [[Paper](https://www.aclweb.org/anthology/K16-1028.pdf)][[Review](Review/AbstractiveTextSummUsingRNNs.md)]\n- Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting [[Paper](https://arxiv.org/abs/1805.11080)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/Fast%20Abstractive%20Summarization%20with%20Reinforce-Selected%20Sentence%20Rewriting.md)]\n- Improving Abstraction in Text Summarization [[Paper](https://arxiv.org/abs/1808.07913)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/Improving%20Abstraction%20in%20Text%20Summarization.md)]\n- Multi-Reward Reinforced Summarization with Saliency and Entailment [[Paper](https://arxiv.org/abs/1804.06451)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/Multi%20Reward%20Reinforced%20Summarization.md)]\n- Bottom-Up Abstractive Summarization [[Paper](https://arxiv.org/abs/1808.10792)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/Bottom%20Up%20Abstractive%20Summarization.md)]\n- Topic Augmented Generator for Abstractive Summarization [[Paper](https://arxiv.org/abs/1908.07026)][[Review](./Review/TopicAugmentedGenAbsSumm)]\n- Earlier Isn’t Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization [[Paper](https://arxiv.org/abs/1908.11723)][[Review](./Review/SummBiases.md)]\n- Neural Text Summarization: A Critical Evaluation [[Paper](https://www.aclweb.org/anthology/D19-1051.pdf)][[Review](./Review/NeuralTextSumm.md)]\n- What have we achieved on Text Summarization [[Paper](https://arxiv.org/pdf/2010.04529.pdf)][[Review](./Review/WHWAIS.md)]\n- Re-evaluating evaluaton in Text Summarization [[Paper](https://arxiv.org/pdf/2010.07100.pdf)][[Review](./Review/REITS.md)]\n- Asking and answering questions to evaluate the factual consistency of summaries [[Paper](https://arxiv.org/pdf/2004.04228.pdf)][[Review](./Review/factconssumm.md)]\n- On Faithfulness and Factuality in Abstractive Summarization [[Paper](https://arxiv.org/pdf/2005.00661.pdf)][[Review](./Review/OFFAS.md)]\n- FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization [[Paper](https://arxiv.org/pdf/2005.03754.pdf)][[Review](./Review/FEQA.md)]\n- Analyzing sentence fusion in Abstractive Summarization [[Paper](https://www.aclweb.org/anthology/D19-5413.pdf)][[Review](./Review/ASFAS.md)]\n- On the Abstractiveness of Neural Document Summarization [[Paper](https://www.aclweb.org/anthology/D18-1089.pdf)][[Review](./Review/OANDS.md)]\n- Evaluating the Factual Consistency of Abstractive Text Summarization. [[Paper](https://arxiv.org/pdf/1910.12840.pdf)][[Review](./Review/EFCATS.md)]\n- Summ-Eval: Re-evaluating Summarization Evaluation [[Paper](https://arxiv.org/pdf/2007.12626.pdf)][[Review](./Review/summeval.md)]\n\u003c!-- - Abstractive Text Summarization by Incorporating Reader Comments [[Paper]()][[Review]()]\n- Global Encoding For Abstractive Summarization [[Paper]()][[Review]()]\n- HIBERT [[Paper]()][[Review]()] --\u003e\n### Topic-Based \u0026 Query-Based Summarization\n- A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization [[Paper](https://arxiv.org/abs/1805.03616)][[Review](./Review/RConvS2Ssummarization.md)]\n- Query-Based Abstractive Summarization Using Neural Networks [[Paper](https://arxiv.org/abs/1712.06100)][[Review](./Review/QueryBasedSummNN.md)]\n- Transforming Wikipedia into Augmented Data for Query Focused Summarization [[Paper](https://arxiv.org/abs/1911.03324)][[Review](./Review/AugmentWikiforQueryBasedSumm.md)]\n- Extreme Summarization with Topic Aware Convolutional Neural Networks [[Paper][[v2](https://arxiv.org/abs/1907.08722)][[v1](https://arxiv.org/pdf/1808.08745.pdf)]][[Review](./Review/XSUM.md)]\n\n## Language Modeling\n- CTRL: A Conditional Transformer Language Model for Controllable Generation [[Paper](https://arxiv.org/abs/1909.05858)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/CTRL.md)]\n\n## Training\n- Von Mises-Fisher Loss for training Seq2Seq Models with Continous Outputs [[Paper](https://arxiv.org/pdf/1812.04616.pdf)][[Review](https://github.com/Shashi456/Papers/blob/master/Review/VonMisesLoss.md)]\n- Neural Text Degeneration with Unlikelihood Training [[Paper](https://arxiv.org/abs/1908.04319)][[Review](./Review/unlikelihooddegen.md)]\n- The curious case of neural text degeneration [[Paper](https://arxiv.org/abs/1904.09751)] [[Review](./Review/ccdegen.md)]\n- Parameter Selection: Why We Should Pay More Attention to It [[Paper](https://arxiv.org/abs/2107.05393)] [[Review](./Review/parasel.md)]\n\n## Question Answering\n- Generalizing Question Answering System with Pre-trained Language Model Fine-tuning [[Paper](https://www.aclweb.org/anthology/D19-5827/)][[Review](./Review/GeneralizingQAXLNET.md)]\n- MULTI QA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension [[Paper](https://arxiv.org/abs/1905.13453)][[Review](./Review/MultiQA.md)]\n\n\n## Word Representations and Embeddings \n- Deep Contextualized word representations - ELMO [[Paper](https://arxiv.org/abs/1802.05365)][[Review](./Review/ELMO.md)]\n- Information-Theoretic Probing with Minimum Description Length [[Paper](https://arxiv.org/abs/2003.12298)][[Review](https://github.com/Shashi456/Deep-Learning-Papers/blob/master/Review/Information-Theretic%20Probing%20with%20Minimum%20Description%20Length.md)]\n- SimCSE: Simple constrastive learning for sentence embeddings [[Paper](https://arxiv.org/pdf/2104.08821.pdf)] [[Review](./Review/SimCSE.md)]\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshashi456%2Fdeep-learning-research","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshashi456%2Fdeep-learning-research","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshashi456%2Fdeep-learning-research/lists"}