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Category: MachineLearning

Paper Digest: ICLR 2025 Papers & Highlights
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The International Conference on Learning Representations (ICLR) is one of the top machine learning conferences in the world. In 2025, it is to be held in Singapore. To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights to quickly get the main idea of each paper.

Note: ICLR-2025 accepts more than 3,700 papers, this page only includes 500 of them selected by our daily paper digest algorithm. Interested users can choose to read All 3,700 ICLR-2025 papers in a separate page, which takes quite some time to load.

To search for papers presented at ICLR-2025 on a specific topic, please make use of the search by venue (ICLR-2025) service. To summarize the latest research published at ICLR-2025 on a specific topic, you can utilize the review by venue (ICLR-2025) service. If you are interested in browsing papers by author, we have a comprehensive list of ~ 15,000 authors (ICLR-2025). Additionally, you may want to explore our "Best Paper" Digest (ICLR), which lists the most influential ICLR papers since 2018.

We've developed a service - ICLR-2025 Research that synthesizes the latest findings from ICLR 2025 into comprehensive reports. For instance, we've generated a report on Advances in Large Language Model Training: Insights from ICLR 2025 Papers. We encourage interested users to utilize our service to create tailored reports on other emerging topics.

Most Influential ArXiv (Machine Learning) Papers (2025-03 Version)
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The field of Machine Learning in arXiv covers papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. It is also an appropriate primary category for applications of machine learning methods. Paper Digest Team analyzes all papers published in this field in the past years, and presents up to 30 most influential papers for each year. This ranking list is automatically constructed based upon citations from both research papers and granted patents, and will be frequently updated to reflect the most recent changes. To find the latest version of this list or the most influential papers from other conferences/journals, please visit Best Paper Digest page. Note: the most influential papers may or may not include the papers that won the best paper awards. (Version: 2025-03).

Most Influential ICML Papers (2025-03 Version)
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The International Conference on Machine Learning (ICML) is one of the top machine learning conferences in the world. Paper Digest Team analyzes all papers published on ICML in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically constructed based upon citations from both research papers and granted patents, and will be frequently updated to reflect the most recent changes. To find the latest version of this list or the most influential papers from other conferences/journals, please visit Best Paper Digest page. Note: the most influential papers may or may not include the papers that won the best paper awards. (Version: 2025-03)

To search or review papers within ICML related to a specific topic, please use the search by venue (ICML) and review by venue (ICML) services. To browse the most productive ICML authors by year ranked by #papers accepted, here are the most productive ICML authors grouped by year.

Most Influential NIPS Papers (2025-03 Version)
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The Conference on Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world. Paper Digest Team analyzes all papers published on NIPS in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically constructed based upon citations from both research papers and granted patents, and will be frequently updated to reflect the most recent changes. To find the latest version of this list or the most influential papers from other conferences/journals, please visit Best Paper Digest page. Note: the most influential papers may or may not include the papers that won the best paper awards. (Version: 2025-03)

To search or review papers within NIPS related to a specific topic, please use the search by venue (NIPS) and review by venue (NIPS) services. To browse the most productive NIPS authors by year ranked by #papers accepted, here are the most productive NIPS authors grouped by year.

Most Influential ICLR Papers (2025-03 Version)
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The International Conference on Learning Representations (ICLR) is one of the top machine learning conferences in the world. Paper Digest Team analyzes all papers published on ICLR in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically constructed based upon citations from both research papers and granted patents, and will be frequently updated to reflect the most recent changes. To find the latest version of this list or the most influential papers from other conferences/journals, please visit Best Paper Digest page. Note: the most influential papers may or may not include the papers that won the best paper awards. (Version: 2025-03)

To search or review papers within ICLR related to a specific topic, please use the search by venue (ICLR) and review by venue (ICLR) services. To browse the most productive ICLR authors by year ranked by #papers accepted, here are the most productive ICLR authors grouped by year.

Paper Digest: Most Cited Papers on ChatGPT
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Paper Digest Team extracted all recent ChatGPT related papers on our radar, and generated related features for each of them. The results are sorted by impact. ChatGPT (Generative Pre-trained Transformer) is a chatbot launched by OpenAI in November 2022. It is built on top of OpenAI's GPT-3 family of large language models, and is fine-tuned with both supervised and reinforcement learning techniques.