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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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- Towards Data-centric Graph Machine Learning: Review and Outlook29
A systematic framework is introduced that encompasses all stages of the graph data lifecycle, including graph data collection, exploration, improvement, exploitation, and maintenance, and a thorough taxonomy of each stage is presented to answer three critical graph-centric questions.
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- GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language Models23
This work proposes a bias evaluation framework named GPTBIAS that leverages the high performance of LLMs (e.g., GPT-4 \cite{openai2023gpt4}) to assess bias in models and introduces prompts specifically designed for evaluating model bias.
- You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets22
The first-of-its-kind exploration of discovering matching untrained GNNs with sparsity as the core tool is carried out and it is shown that the found untrained subnetworks can substantially mitigate the GNN over-smoothing problem, hence becoming a powerful tool to enable deeper GNN's without bells and whistles.
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- Are Large Kernels Better Teachers than Transformers for ConvNets?21
A first-of-its-kind study unveiling that modern large-kernel ConvNets, a compelling competitor to Vision Transformers, are remarkably more effective teachers for small-kernel convolutional Neural Networks, due to more similar architectures.
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- Towards Query Efficient Black-box Attacks21
Through extensive experiments, it is shown that with only 1,701 queries, an adversarial example can perturb a gray image to any target class of ImageNet with a 100% success rate on InceptionV3 and the algorithm has successfully defeated two real-world systems.
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- SpeechR: A Benchmark for Speech Reasoning in Large Audio-Language Models16
SpeechR, a unified benchmark for evaluating reasoning over speech in large audio-language models, establishes a structured benchmark for evaluating reasoning in spoken language, enabling more targeted analysis of model capabilities across diverse dialogue-based tasks.
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- Multi-label Active Learning Based on Maximum Correntropy Criterion: Towards Robust and Discriminative Labeling15
A novel active learning approach to reduce the annotation costs greatly for multi-label classification by merging uncertainty and representativeness by using the MCC to alleviate the influence of outlier labels for discriminative labeling.
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- Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games10
CommExpl, an exploration technique that injects external commonsense knowledge, via a pretrained language model (LM), into the agent during training when the agent is the most uncertain about its next action, is proposed.
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- REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement9
A recursive erasure memory network (REM-Net) equipped with a module to refine the evidence by recursively erasing the low-quality evidence that does not explain the question answering, which demonstrates the performance of REM- Net and shows that the refined evidence is explainable.
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- ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection3
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- Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction–
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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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