Update README.md

README.md

@@ -1,3 +1,59 @@
 ---
 license: mit
+task_categories:
+- text-classification
+tags:
+- AI
+- ICML
+- ICML2023
 ---
+## ICML 2023 International Conference on Machine Learning 2023 Accepted Paper Meta Info Dataset
+
+This dataset is collect from the ICML 2024 OpenReview website (https://openreview.net/group?id=ICML.cc/2023/Conference#tab-accept-oral) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/icml2023). For researchers who are interested in doing analysis of ICML 2023 accepted papers and potential trends, you can use the already cleaned up json files. 
+Each row contains the meta information of a paper in the ICML 2023 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
+
+```
+{
+    "abs": "https://proceedings.mlr.press/v202/aamand23a.html",
+    "Download PDF": "https://proceedings.mlr.press/v202/aamand23a/aamand23a.pdf",
+    "OpenReview": "https://openreview.net/forum?id=BVomXLJQoH",
+    "title": "Data Structures for Density Estimation",
+    "url": "https://proceedings.mlr.press/v202/aamand23a.html",
+    "authors": "Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal",
+    "detail_url": "https://proceedings.mlr.press/v202/aamand23a.html",
+    "tags": "ICML 2023",
+    "abstract": "We study statistical/computational tradeoffs for the following density estimation problem: given $k$ distributions $v_1, \\ldots, v_k$ over a discrete domain of size $n$, and sampling access to a distribution $p$, identify $v_i$ that is \"close\" to $p$. Our main result is the first data structure that, given a sublinear (in $n$) number of samples from $p$, identifies $v_i$ in time sublinear in $k$. We also give an improved version of the algorithm of Acharya et al. (2018) that reports $v_i$ in time linear in $k$. The experimental evaluation of the latter algorithm shows that it achieves a significant reduction in the number of operations needed to achieve a given accuracy compared to prior work."
+}
+
+```
+
+
+
+## Related
+
+## 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>
+
+## 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>