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- Reviewer Guidelines - NeurIPS
Reviewer Guidelines Thank you for agreeing to serve as a reviewer for NeurIPS 2022! This page provides an overview of reviewer responsibilities and key dates Frequently Asked Questions You can find answers to FAQs here Contact Information The Area Chair (AC) assigned to a paper should be your first point of contact for that paper You can contact the AC by leaving a comment in OpenReview
- NeurIPS 2022 Oral-Equivalent Papers
We parse public discourse as an Abstract Meaning Representation (AMR) graph and use the powerful hyperbolic geometric representation to model graphs with hierarchical structure
- Paper Digest: NeurIPS 2022 Highlights
If you are interested in browsing papers by author, we have a comprehensive list of ~ 9,900 authors (NIPS-2022) Additionally, you may want to explore our “Best Paper” Digest (NeurIPS), which lists the most influential NeurIPS papers in the past 30 years
- Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using . . .
In recommendation tasks, we can only access the user’s final choice at each moment, without knowledge of the interest cluster, reflecting incomplete knowledge
- Search for: NeurIPS 2022 OpenReview paper review decision author Yuri . . .
In many operations management problems, we need to make decisions sequentially to minimize the cost, satisfying certain constraints One modeling approach to such problems is the constrained Markov decision process (CMDP) In this work, we develop a data-driven primal-dual algorithm to solve CMDPs
- Peer reviews of peer reviews: A randomized controlled trial and other . . .
Motivated by the need for evaluations of review quality, we conducted a quantitative study into the reliability of evaluating review quality at the Neural Information Processing Systems (NeurIPS) 2022 conference, a top-tier conference in the field of machine learning
- NeurIPS 2022 Posters
Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking? Why do We Need Large Batchsizes in Contrastive Learning? A Gradient-Bias Perspective When Do Flat Minima Optimizers Work? How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?
- The Minority Matters: A Diversity-Promoting Collaborative . . . - NeurIPS
In this paper, we focus on how to develop an effective CML-based recommendation system on top of the implicit feedback signals (say clicks, browses, and bookmarks)
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