--- title: "Other — papers, page 23" description: "Other: 743 papers of the Elamur Research library · page 23 of 30 — our summary of each, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/shelves/other/page/23/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/other/page/23/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [Other](https://md.research.elamur.ai/free/en/shelves/other/) / page 23 # 📦 Other 743 papers · page 23 of 30 · newest first · [all shelves](https://md.research.elamur.ai/free/en/shelves/) Works whose topics fit none of the fields above yet. [← prev](https://md.research.elamur.ai/free/en/shelves/other/page/22/) [1](https://md.research.elamur.ai/free/en/shelves/other/) … [22](https://md.research.elamur.ai/free/en/shelves/other/page/22/) **23** [24](https://md.research.elamur.ai/free/en/shelves/other/page/24/) … [30](https://md.research.elamur.ai/free/en/shelves/other/page/30/) [next →](https://md.research.elamur.ai/free/en/shelves/other/page/24/) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://md.research.elamur.ai/free/en/papers/paszke-2019-pytorch-imperative-style-high-performance/) — Adam Paszke, Sam Gross, Francisco Massa, et al. · 2019 - [Quantifying the Carbon Emissions of Machine Learning](https://md.research.elamur.ai/free/en/papers/lacoste-2019-quantifying-carbon-emissions-machine-learning/) — Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, et al. · 2019 - [Reducing Transformer Depth on Demand with Structured Dropout](https://md.research.elamur.ai/free/en/papers/fan-2019-reducing-transformer-depth-demand-structured/) — Angela Fan, Edouard Grave, Armand Joulin · 2019 - [Release Strategies and the Social Impacts of Language Models](https://md.research.elamur.ai/free/en/papers/solaiman-2019-release-strategies-social-impacts-language/) — Irene Solaiman, Miles Brundage, Jack Clark, et al. · 2019 - [Representation Degeneration Problem in Training Natural Language Generation Models](https://md.research.elamur.ai/free/en/papers/gao-2019-representation-degeneration-problem-training-natural/) — Jun Gao, Di He, Xu Tan, et al. · 2019 - [Revealing the Dark Secrets of BERT](https://md.research.elamur.ai/free/en/papers/kovaleva-2019-revealing-dark-secrets-bert/) — Olga Kovaleva, Alexey Romanov, Anna Rogers, et al. · 2019 - [S2ORC: The Semantic Scholar Open Research Corpus](https://md.research.elamur.ai/free/en/papers/lo-2019-s2orc-semantic-scholar-open-research/) — Kyle Lo, Lucy Lu Wang, Mark Neumann, et al. · 2019 - [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://md.research.elamur.ai/free/en/papers/zampieri-2019-semeval-2019-task-6-identifying/) — Marcos Zampieri, Shervin Malmasi, Preslav Nakov, et al. · 2019 - [Similarity of Neural Network Representations Revisited](https://md.research.elamur.ai/free/en/papers/kornblith-2019-similarity-neural-network-representations-revisited/) — Simon Kornblith, Mohammad Norouzi, Honglak Lee, et al. · 2019 - [SocialIQA: Commonsense Reasoning about Social Interactions](https://md.research.elamur.ai/free/en/papers/sap-2019-socialiqa-commonsense-reasoning-about-social/) — Maarten Sap, Hannah Rashkin, Derek Chen, et al. · 2019 - [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://md.research.elamur.ai/free/en/papers/park-2019-specaugment-simple-data-augmentation-method/) — Daniel S. Park, William Chan, Yu Zhang, et al. · 2019 - [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/) — Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al. · 2019 - [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://md.research.elamur.ai/free/en/papers/chen-2019-tabfact-large-scale-dataset-table/) — Wenhu Chen, Hongmin Wang, Jianshu Chen, et al. · 2019 - [The Second Conversational Intelligence Challenge (ConvAI2)](https://md.research.elamur.ai/free/en/papers/dinan-2019-second-conversational-intelligence-challenge-convai2/) — Emily Dinan, Varvara Logacheva, Valentin Malykh, et al. · 2019 - [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://md.research.elamur.ai/free/en/papers/dong-2019-unified-language-model-pre-training/) — Li Dong, Nan Yang, Wenhui Wang, et al. · 2019 - [UNITER: UNiversal Image-TExt Representation Learning](https://md.research.elamur.ai/free/en/papers/chen-2019-uniter-universal-image-text-representation/) — Yen-Chun Chen, Linjie Li, Licheng Yu, et al. · 2019 - [Unsupervised Data Augmentation for Consistency Training](https://md.research.elamur.ai/free/en/papers/xie-2019-unsupervised-data-augmentation-consistency-training/) — Qizhe Xie, Zihang Dai, Eduard Hovy, et al. · 2019 - [VideoBERT: A Joint Model for Video and Language Representation Learning](https://md.research.elamur.ai/free/en/papers/sun-2019-videobert-joint-model-video-language/) — Chen Sun, Austin Myers, Carl Vondrick, et al. · 2019 - [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://md.research.elamur.ai/free/en/papers/lu-2019-vilbert-pretraining-task-agnostic-visiolinguistic/) — Jiasen Lu, Dhruv Batra, Devi Parikh, et al. · 2019 - [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://md.research.elamur.ai/free/en/papers/li-2019-visualbert-simple-performant-baseline-vision/) — Liunian Harold Li, Mark Yatskar, Da Yin, et al. · 2019 - [Visualizing and Measuring the Geometry of BERT](https://md.research.elamur.ai/free/en/papers/coenen-2019-visualizing-measuring-geometry-bert/) — Andy Coenen, Emily Reif, Ann Yuan, et al. · 2019 - [VL-BERT: Pre-training of Generic Visual-Linguistic Representations](https://md.research.elamur.ai/free/en/papers/su-2019-vl-bert-pre-training-generic/) — Weijie Su, Xizhou Zhu, Yue Cao, et al. · 2019 - [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://md.research.elamur.ai/free/en/papers/turc-2019-well-read-students-learn-better/) — Iulia Turc, Ming-Wei Chang, Kenton Lee, et al. · 2019 - [What do you learn from context? Probing for sentence structure in contextualized word representations](https://md.research.elamur.ai/free/en/papers/tenney-2019-learn-context-probing-sentence-structure/) — Ian Tenney, Patrick Xia, Berlin Chen, et al. · 2019 - [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://md.research.elamur.ai/free/en/papers/yang-2019-xlnet-generalized-autoregressive-pretraining-language/) — Zhilin Yang, Zihang Dai, Yiming Yang, et al. · 2019 [← prev](https://md.research.elamur.ai/free/en/shelves/other/page/22/) [1](https://md.research.elamur.ai/free/en/shelves/other/) … [22](https://md.research.elamur.ai/free/en/shelves/other/page/22/) **23** [24](https://md.research.elamur.ai/free/en/shelves/other/page/24/) … [30](https://md.research.elamur.ai/free/en/shelves/other/page/30/) [next →](https://md.research.elamur.ai/free/en/shelves/other/page/24/)