active learning

Learning to Actively Learn Neural Machine Translation CoNLL 2018 Learning to Multi-Task by Active Sampling ICLR 2018 Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus ACL 2018 New Balanced Active Learning Model and Optimization Algorithm IJCAI 2018 On the Efficiency of Data Collection for Crowdsourced Classification IJCAI 2018 On the Relationship between Data Efficiency and Error for Uncertainty Sampling ICML 2018 On Training and Evaluation of Grapheme-to-Phoneme Mappings with Limited Data Interspeech 2018 On Whom Should I Perform this Lab Test Next? An Active Feature Elicitation Approach IJCAI 2018 Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian Resource COLING 2018 Personalized Text Retrieval for Learners of Chinese as a Foreign Language COLING 2018 Prompsit’s submission to WMT 2018 Parallel Corpus Filtering shared task EMNLP 2018 Query K-means Clustering and the Double Dixie Cup Problem NeurIPS 2018 Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels IJCAI 2018 Risk-Aware Active Inverse Reinforcement Learning CoRL 2018 Robust Active Label Correction AISTATS 2018 RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian COLING 2018 Safe Active Learning for Time-Series Modeling with Gaussian Processes NeurIPS 2018 Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model Refinement for a Low Resource Indian Language Interspeech 2018 Sprucing up the trees – Error detection in treebanks COLING 2018 Submodularity-Inspired Data Selection for Goal-Oriented Chatbot Training Based on Sentence Embeddings IJCAI 2018 The Power of Ensembles for Active Learning in Image Classification CVPR 2018 Towards Black-box Iterative Machine Teaching ICML 2018 Towards Human-Machine Cooperation: Self-Supervised Sample Mining for Object Detection CVPR 2018 Uncertainty Sampling for Action Recognition via Maximizing Expected Average Precision IJCAI 2018 Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss NeurIPS 2018