--- title: "AI & machine learning — papers, page 8" description: "AI & machine learning: 240 papers of the Elamur Research library · page 8 of 10 — our summary of each, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/shelves/ai/page/8/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/ai/page/8/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [AI & machine learning](https://md.research.elamur.ai/free/en/shelves/ai/) / page 8 # 🤖 AI & machine learning 240 papers · page 8 of 10 · newest first · [all shelves](https://md.research.elamur.ai/free/en/shelves/) Neural networks and learning, reasoning, knowledge graphs, continual and interactive learning, how AI is evaluated. [← prev](https://md.research.elamur.ai/free/en/shelves/ai/page/7/) [1](https://md.research.elamur.ai/free/en/shelves/ai/) … [7](https://md.research.elamur.ai/free/en/shelves/ai/page/7/) **8** [9](https://md.research.elamur.ai/free/en/shelves/ai/page/9/) [10](https://md.research.elamur.ai/free/en/shelves/ai/page/10/) [next →](https://md.research.elamur.ai/free/en/shelves/ai/page/9/) - [How Can We Accelerate Progress Towards Human-like Linguistic Generalization?](https://md.research.elamur.ai/free/en/papers/linzen-2020-how-can-we-accelerate-progress/) — Tal Linzen · 2020 - [Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings](https://md.research.elamur.ai/free/en/papers/saxena-2020-improving-multi-hop-question-answering/) — Apoorv Saxena, Aditay Tripathi, Partha Talukdar · 2020 - [Knowledge Graphs](https://md.research.elamur.ai/free/en/papers/hogan-2020-knowledge-graphs/) — Aidan Hogan, Eva Blomqvist, Michael Cochez, et al. · 2020 - [Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics](https://md.research.elamur.ai/free/en/papers/mathur-2020-tangled-up-bleu-reevaluating-evaluation/) — Nitika Mathur, Timothy Baldwin, Trevor Cohn · 2020 - [Temporal Reasoning in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/vashishtha-2020-temporal-reasoning-natural-language-inference/) — Siddharth Vashishtha, Adam Poliak, Yash Kumar Lal, et al. · 2020 - [Abductive Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/) — Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, et al. · 2019 - [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://md.research.elamur.ai/free/en/papers/bosselut-2019-comet-commonsense-transformers-automatic-knowledge/) — Antoine Bosselut, Hannah Rashkin, Maarten Sap, et al. · 2019 - [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/) — Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, et al. · 2019 - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/tan-2019-efficientnet-rethinking-model-scaling-convolutional/) — Mingxing Tan, Quoc V. Le · 2019 - [GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://md.research.elamur.ai/free/en/papers/huang-2019-glossbert-bert-word-sense-disambiguation/) — Luyao Huang, Chi Sun, Xipeng Qiu, et al. · 2019 - [Invariant Risk Minimization](https://md.research.elamur.ai/free/en/papers/arjovsky-2019-invariant-risk-minimization/) — Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, et al. · 2019 - [Language Models as Knowledge Bases?](https://md.research.elamur.ai/free/en/papers/petroni-2019-language-models-as-knowledge-bases/) — Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, et al. · 2019 - [PIQA: Reasoning about Physical Commonsense in Natural Language](https://md.research.elamur.ai/free/en/papers/bisk-2019-piqa-reasoning-about-physical-commonsense/) — Yonatan Bisk, Rowan Zellers, Ronan Le Bras, et al. · 2019 - [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/) — Shane Storks, Qiaozi Gao, Joyce Y. Chai · 2019 - [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/) — R. Thomas McCoy, Ellie Pavlick, Tal Linzen · 2019 - [Semi-automatic Annotation of Event Structure, Argument Structure, and Opposition Structure to WordNet by Using Event Structure Frame](https://md.research.elamur.ai/free/en/papers/im-2019-semi-automatic-annotation-event-structure/) — Seohyun Im · 2019 - [TuckER: Tensor Factorization for Knowledge Graph Completion](https://md.research.elamur.ai/free/en/papers/balazevic-2019-tucker-tensor-factorization-knowledge-graph/) — Ivana Balažević, Carl Allen, Timothy M. Hospedales · 2019 - [WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations](https://md.research.elamur.ai/free/en/papers/pilehvar-2019-wic-word-context-dataset-evaluating/) — Mohammad Taher Pilehvar, Jose Camacho-Collados · 2019 - [A Call for Clarity in Reporting BLEU Scores](https://md.research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/) — Matt Post · 2018 - [A Structured Review of the Validity of BLEU](https://md.research.elamur.ai/free/en/papers/reiter-2018-structured-review-validity-bleu/) — Ehud Reiter · 2018 - [Breaking NLI Systems with Sentences that Require Simple Lexical Inferences](https://md.research.elamur.ai/free/en/papers/glockner-2018-breaking-nli-systems-sentences-that/) — Max Glockner, Vered Shwartz, Yoav Goldberg · 2018 - [Continual Lifelong Learning with Neural Networks: A Review](https://md.research.elamur.ai/free/en/papers/parisi-2018-continual-lifelong-learning-neural-networks/) — German I. Parisi, Ronald Kemker, Jose L. Part, et al. · 2018 - [How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks](https://md.research.elamur.ai/free/en/papers/kaushik-2018-how-much-reading-does-reading/) — Divyansh Kaushik, Zachary C. Lipton · 2018 - [Relational inductive biases, deep learning, and graph networks](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/) — Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al. · 2018 - [Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead](https://md.research.elamur.ai/free/en/papers/rudin-2018-stop-explaining-black-box/) — Cynthia Rudin · 2018 [← prev](https://md.research.elamur.ai/free/en/shelves/ai/page/7/) [1](https://md.research.elamur.ai/free/en/shelves/ai/) … [7](https://md.research.elamur.ai/free/en/shelves/ai/page/7/) **8** [9](https://md.research.elamur.ai/free/en/shelves/ai/page/9/) [10](https://md.research.elamur.ai/free/en/shelves/ai/page/10/) [next →](https://md.research.elamur.ai/free/en/shelves/ai/page/9/)