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- Dacheng TaoSuggested from co-authorship
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- Xingquan ZhuSuggested from co-authorship
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Works120 from public data
- Learning how to Active Learn: A Deep Reinforcement Learning Approach321
A novel formulation of active learning is introduced by reframing the active learning as a reinforcement learning problem and explicitly learning a data selection policy, where the policy takes the role of the activelearning heuristic.
- LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning200
This paper proposes to merge the two directions of MARL and learn each agent an intrinsic reward function which diversely stimulates the agents at each time step, and compares LIIR with a number of state-of-the-art MARL methods on battle games in StarCraft II.
- Curriculum-guided Hindsight Experience Replay192
This paper proposes to adaptively select the failed experiences for replay according to the proximity to the true goals and the curiosity of exploration over diverse pseudo goals, and adopts a human-like learning strategy that enforces more curiosity in earlier stages and changes to larger goal-proximity later.
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- Revisiting metric learning for few-shot image classification121
This work revisits the classical triplet network from deep metric learning, and extends it into a deep K-tuplet network for few-shot learning, utilizing the relationship among the input samples to learn a general representation learning via episode-training.
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- Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition103
Two variants of DATNet are investigated to explore effective feature fusion between high and low resource, and a novel Generalized Resource-Adversarial Discriminator (GRAD) is proposed to address the noisy and imbalanced training data.
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- BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering86
A Bi-directional Attention Entity Graph Convolutional Network (BAG) is proposed, leveraging relationships between nodes in an entity graph and attention information between a query and the entity graph to solve the multi-hop reasoning question answering task.
- Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary80
This work proposes a novel neural network model for joint training from both sources of data based on cross-lingual word embeddings, and shows substantial empirical improvements over baseline techniques.
- DAGN: Discourse-Aware Graph Network for Logical Reasoning71
This work proposes a discourse-aware graph network (DAGN) that reasons relying on the discourse structure of the texts, and encodes discourse information as a graph with elementary discourse units (EDUs) and discourse relations, and learns the discourse- aware features via a graph network for downstream QA tasks.
- Transfer Learning across Networks for Collective Classification71
A new transfer learning algorithm is proposed that attempts to transfer common latent structure features across the source and target networks and can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.
- Active Learning for Crowdsourcing Using Knowledge Transfer69
This paper proposes a new probabilistic model that transfers knowledge from abundant unlabeled data in auxiliary domains to help estimate labelers' expertise and presents a novel active learning algorithm that simultaneously selects the most informative example and queries its label from the labeler with the best expertise.
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- Improving performance of tensor-based context-aware recommenders using Bias Tensor Factorization with context feature auto-encoding55
It is argued that rating can't be totally explained by the interactions and the rating also influenced by the combined impact of overall mean, user bias, item bias and context bias, and a novel context-aware recommendation model named Bias Tensor Factorization is proposed, which take all this factors into account.
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- Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection46
This paper introduces a novel approach to sequence tagging that learns to correct the errors from cross-lingual projection using an explicit debiasing layer and equals or exceeds the state-of-the-art on eight simulated low-resource settings, as well as two real low- resource languages, Malagasy and Kinyarwanda.
- Unsupervised Domain Adaptation on Reading Comprehension42
This work provides a novel conditional adversarial self-training method (CASe), which leverages a BERT model fine-tuned on the source dataset along with the confidence filtering to generate reliable pseudo-labeled samples in the target domain for self- training.
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- Active Multi-task Learning via Bandits36
This paper introduces a new active multi- task learning paradigm, which selectively samples effective instances for multi-task learning and cast the selection procedure as a bandit framework, and provides an implementation of the algorithm based on a popular multi- Task learning algorithm that is trace-norm regularization method.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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