Datasets:
license: mit
task_categories:
- text-classification
tags:
- AI
- ICLR2022
- ICLR
ICLR 2022 International Conference on Learning Representations 2022 Accepted Paper Meta Info Dataset
This dataset is collect from the ICLR 2022 OpenReview website (https://openreview.net/group?id=ICLR.cc/2022/Conference#tab-accept-oral) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/iclr2022). For researchers who are interested in doing analysis of ICLR 2022 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 ICLR 2022 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": "Domino: Discovering Systematic Errors with Cross-Modal Embeddings",
"url": "https://openreview.net/forum?id=FPCMqjI0jXN",
"detail_url": "https://openreview.net/forum?id=FPCMqjI0jXN",
"authors": "Sabri Eyuboglu,Maya Varma,Khaled Kamal Saab,Jean-Benoit Delbrouck,Christopher Lee-Messer,Jared Dunnmon,James Zou,Christopher Re",
"tags": "ICLR 2022,Oral",
"abstract": "Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly challenging when working with high-dimensional inputs (e.g. images, audio), where important slices are often unlabeled. In order to address this issue, recent studies have proposed automated slice discovery methods (SDMs), which leverage learned model representations to mine input data for slices on which a model performs poorly. To be useful to a practitioner, these methods must identify slices that are both underperforming and coherent (i.e. united by a human-understandable concept). However, no quantitative evaluation framework currently exists for rigorously assessing SDMs with respect to these criteria. Additionally, prior qualitative evaluations have shown that SDMs often identify slices that are incoherent. In this work, we address these challenges by first designing a principled evaluation framework that enables a quantitative comparison of SDMs across 1,235 slice discovery settings in three input domains (natural images, medical images, and time-series data).\nThen, motivated by the recent development of powerful cross-modal representation learning approaches, we present Domino, an SDM that leverages cross-modal embeddings and a novel error-aware mixture model to discover and describe coherent slices. We find that Domino accurately identifies 36% of the 1,235 slices in our framework -- a 12 percentage point improvement over prior methods. Further, Domino is the first SDM that can provide natural language descriptions of identified slices, correctly generating the exact name of the slice in 35% of settings. ",
"pdf": "https://openreview.net/pdf/a5ca838a35d810400cfa090453cd85abe02ab6b0.pdf"
}
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