Update README.md

README.md

@@ -1,3 +1,60 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- AI
+- NIPS
+- NIPS2021
+---
+## Neural Information Processing Systems NeurIPS 2021 Accepted Paper Meta Info Dataset
+
+This dataset is collected from the NeurIPS 2021 Advances in Neural Information Processing Systems 35 conference accepted paper (https://papers.nips.cc/paper_files/paper/2021) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/nips2021). For researchers who are interested in doing analysis of NIPS 2021 accepted papers and potential research trends, you can use the already cleaned up json file in the dataset. Each row contains the meta information of a paper in the NIPS 2021 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain. 
+
+
+### Meta Information of Json File
+
+```
+{
+    "title": "Backward-Compatible Prediction Updates: A Probabilistic Approach",
+    "url": "https://papers.nips.cc/paper_files/paper/2021/hash/012d9fe15b2493f21902cd55603382ec-Abstract.html",
+    "authors": "Frederik Tr\u00e4uble, Julius von K\u00fcgelgen, Matth\u00e4us Kleindessner, Francesco Locatello, Bernhard Sch\u00f6lkopf, Peter Gehler",
+    "detail_url": "https://papers.nips.cc/paper_files/paper/2021/hash/012d9fe15b2493f21902cd55603382ec-Abstract.html",
+    "tags": "NIPS 2021",
+    "Bibtex": "https://papers.nips.cc/paper_files/paper/11633-/bibtex",
+    "Paper": "https://papers.nips.cc/paper_files/paper/2021/file/012d9fe15b2493f21902cd55603382ec-Paper.pdf",
+    "Reviews And Public Comment \u00bb": "https://papers.nips.cchttps://openreview.net/forum?id=YjZoWjTKYvH",
+    "Supplemental": "https://papers.nips.cc/paper_files/paper/2021/file/012d9fe15b2493f21902cd55603382ec-Supplemental.pdf",
+    "abstract": "When machine learning systems meet real world applications, accuracy is only one of several requirements. In this paper, we assay a complementary perspective originating from the increasing availability of pre-trained and regularly improving state-of-the-art models. While new improved models develop at a fast pace, downstream tasks vary more slowly or stay constant. Assume that we have a large unlabelled data set for which we want to maintain accurate predictions. Whenever a new and presumably better ML models becomes available, we encounter two problems: (i) given a limited budget, which data points should be re-evaluated using the new model?; and (ii) if the new predictions differ from the current ones, should we update? Problem (i) is about compute cost, which matters for very large data sets and models. Problem (ii) is about maintaining consistency of the predictions, which can be highly relevant for downstream applications; our demand is to avoid negative flips, i.e., changing correct to incorrect predictions. In this paper, we formalize the Prediction Update Problem and present an efficient probabilistic approach as answer to the above questions. In extensive experiments on standard classification benchmark data sets, we show that our method outperforms alternative strategies along key metrics for backward-compatible prediction updates."
+}
+
+```
+
+
+## Related
+
+## AI Agent Marketplace and Search
+[AI Agent Marketplace and Search](http://www.deepnlp.org/search/agent) <br>
+[Robot Search](http://www.deepnlp.org/search/robot) <br>
+[Equation and Academic search](http://www.deepnlp.org/search/equation) <br>
+[AI & Robot Comprehensive Search](http://www.deepnlp.org/search) <br>
+[AI & Robot Question](http://www.deepnlp.org/question) <br>
+[AI & Robot Community](http://www.deepnlp.org/community) <br>
+[AI Agent Marketplace Blog](http://www.deepnlp.org/blog/ai-agent-marketplace-and-search-portal-reviews-2025) <br>
+
+## AI Agent Reviews
+[AI Agent Marketplace Directory](http://www.deepnlp.org/store/ai-agent) <br>
+[Microsoft AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-microsoft-ai-agent) <br>
+[Claude AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-claude-ai-agent) <br>
+[OpenAI AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-openai-ai-agent) <br>
+[Saleforce AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-salesforce-ai-agent) <br>
+[AI Agent Builder Reviews](http://www.deepnlp.org/store/ai-agent/ai-agent-builder) <br>
+
+## AI Equation
+[List of AI Equations and Latex](http://www.deepnlp.org/equation/category/ai) <br>
+[List of Math Equations and Latex](http://www.deepnlp.org/equation/category/math) <br>
+[List of Physics Equations and Latex](http://www.deepnlp.org/equation/category/physics) <br>
+[List of Statistics Equations and Latex](http://www.deepnlp.org/equation/category/statistics) <br>
+[List of Machine Learning Equations and Latex](http://www.deepnlp.org/equation/category/machine%20learning) <br>