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Papers (including subtopics)

Look-ahead Meta Learning for Continual Learning NeurIPS 2020 Loss Function Search for Face Recognition ICML 2020 Lower Bounds and Optimal Algorithms for Personalized Federated Learning NeurIPS 2020 Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing EMNLP 2020 Low Resource Sequence Tagging using Sentence Reconstruction ACL 2020 Low Resource Sequence Tagging with Weak Labels AAAI 2020 LRG at SemEval-2020 Task 7: Assessing the Ability of BERT and Derivative Models to Perform Short-Edits Based Humor Grading SemEval 2020 LT3 at SemEval-2020 Task 7: Comparing Feature-Based and Transformer-Based Approaches to Detect Funny Headlines SemEval 2020 Machine-oriented NMT Adaptation for Zero-shot NLP tasks: Comparing the Usefulness of Close and Distant Languages COLING 2020 MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer EMNLP 2020 MaintNet: A Collaborative Open-Source Library for Predictive Maintenance Language Resources COLING 2020 ManyEnt: A Dataset for Few-shot Entity Typing COLING 2020 ManyModalQA: Modality Disambiguation and QA over Diverse Inputs AAAI 2020 Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling EMNLP 2020 Marthe: Scheduling the Learning Rate Via Online Hypergradients IJCAI 2020 MATE: Plugging in Model Awareness to Task Embedding for Meta Learning NeurIPS 2020 Matrix Inference and Estimation in Multi-Layer Models NeurIPS 2020 MedDialog: Large-scale Medical Dialogue Datasets EMNLP 2020 Medical Knowledge-enriched Textual Entailment Framework COLING 2020 MELD: Meta-Reinforcement Learning from Images via Latent State Models CoRL 2020 Memory Augmented Graph Neural Networks for Sequential Recommendation AAAI 2020 Mentoring-Reverse Mentoring for Unsupervised Multi-Channel Speech Source Separation Interspeech 2020 Meta-Amortized Variational Inference and Learning AAAI 2020 Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation AAAI 2020 Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples ICLR 2020