{"total":34,"rows":[{"id":"2307.15703","title":"Uncertainty in Natural Language Generation: From Theory to Applications","created":"2023-07-28","cited_by_count":61,"area":null},{"id":"2103.16775","title":"Attention, please! A survey of Neural Attention Models in Deep Learning","created":"2021-03-31","cited_by_count":51,"area":null},{"id":"2204.07756","title":"Visual Attention Methods in Deep Learning: An In-Depth Survey","created":"2022-04-16","cited_by_count":37,"area":null},{"id":"2109.01135","title":"Sequence-to-Sequence Learning with Latent Neural Grammars","created":"2021-09-02","cited_by_count":30,"area":null},{"id":"2103.04181","title":"Contextual Dropout: An Efficient Sample-Dependent Dropout Module","created":"2021-03-06","cited_by_count":26,"area":null},{"id":"2106.05251","title":"Bayesian Attention Belief Networks","created":"2021-06-09","cited_by_count":25,"area":null},{"id":"2109.04325","title":"Learning Opinion Summarizers by Selecting Informative Reviews","created":"2021-09-09","cited_by_count":25,"area":null},{"id":"2106.03257","title":"Structured Reordering for Modeling Latent Alignments in Sequence Transduction","created":"2021-06-06","cited_by_count":17,"area":null},{"id":"2204.05449","title":"Neural Processes with Stochastic Attention: Paying more attention to the context dataset","created":"2022-04-11","cited_by_count":16,"area":null},{"id":"2102.03424","title":"Learning Audio-Visual Correlations from Variational Cross-Modal Generation","created":"2021-02-05","cited_by_count":14,"area":null},{"id":"2110.08226","title":"Guiding Visual Question Generation","created":"2021-10-15","cited_by_count":14,"area":null},{"id":"2110.12567","title":"Alignment Attention by Matching Key and Query Distributions","created":"2021-10-25","cited_by_count":14,"area":null},{"id":"2208.03197","title":"Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey","created":"2022-08-05","cited_by_count":14,"area":null},{"id":"2110.04020","title":"Pathologies in priors and inference for Bayesian transformers","created":"2021-10-08","cited_by_count":13,"area":null},{"id":"2204.01227","title":"Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors","created":"2022-04-04","cited_by_count":13,"area":null},{"id":"2106.15338","title":"Probabilistic Attention for Interactive Segmentation","created":"2021-06-23","cited_by_count":12,"area":null},{"id":"2205.01464","title":"Inducing and Using Alignments for Transition-based AMR Parsing","created":"2022-05-03","cited_by_count":12,"area":null},{"id":"2110.02667","title":"Attentive Walk-Aggregating Graph Neural Networks","created":"2021-10-06","cited_by_count":10,"area":null},{"id":"2112.05909","title":"Neural Attention Models in Deep Learning: Survey and Taxonomy","created":"2021-12-11","cited_by_count":8,"area":null},{"id":"2211.04928","title":"miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings","created":"2022-11-09","cited_by_count":7,"area":null},{"id":"2405.02801","title":"Mozart's Touch: A Lightweight Multi-modal Music Generation Framework Based on Pre-Trained Large Models","created":"2024-05-05","cited_by_count":7,"area":null},{"id":"2103.16710","title":"A study of latent monotonic attention variants","created":"2021-03-30","cited_by_count":5,"area":null},{"id":"2212.14776","title":"On the Interpretability of Attention Networks","created":"2022-12-30","cited_by_count":5,"area":null},{"id":"2406.01189","title":"MultiMax: Sparse and Multi-Modal Attention Learning","created":"2024-06-03","cited_by_count":5,"area":null},{"id":"2304.04556","title":"Attention: Marginal Probability is All You Need?","created":"2023-04-07","cited_by_count":4,"area":null},{"id":"2106.08571","title":"Discrete Auto-regressive Variational Attention Models for Text Modeling","created":"2021-06-16","cited_by_count":2,"area":null},{"id":"2307.13421","title":"On the Learning Dynamics of Attention Networks","created":"2023-07-25","cited_by_count":2,"area":null},{"id":"2603.06875","title":"Stochastic Attention via Langevin Dynamics on the Modern Hopfield Energy","created":"2026-03-06","cited_by_count":2,"area":null},{"id":"2312.02748","title":"Compositional Generalization for Data-to-Text Generation","created":"2023-12-05","cited_by_count":1,"area":null},{"id":"2105.14850","title":"Cascaded Head-colliding Attention","created":"2021-05-31","cited_by_count":0,"area":null},{"id":"2112.08093","title":"Towards Controllable Agent in MOBA Games with Generative Modeling","created":"2021-12-15","cited_by_count":0,"area":null},{"id":"2203.03244","title":"Precognition in Task-oriented Dialogue Understanding: Posterior Regularization by Future Context","created":"2022-03-07","cited_by_count":0,"area":null},{"id":"2305.05162","title":"Effective Medical Code Prediction via Label Internal Alignment","created":"2023-05-09","cited_by_count":0,"area":null},{"id":"2306.02568","title":"Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming","created":"2023-06-05","cited_by_count":0,"area":null}],"next_cursor":null}