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Found 10 papers, 2 papers with code
PCBERT: Parent and Child BERT for Chinese Few-shot NER
Achieving good performance on few-shot or zero-shot datasets has been a long-term challenge for NER.
Task-Aware Low-Rank Adaptation of Segment Anything Model
Furthermore, we introduce modified SAM (mSAM) for multi-task learning where we remove the prompt encoder of SAM and use task-specific no mask embeddings and mask decoder for each task.
A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization
In this paper, we study the Multi-Objective Bi-Level Optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level subproblem is for scalar optimization.
A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting
Specifically, the proposed LIWUDA method constructs a weight network to assign weights to each instance based on its probability of belonging to common classes, and designs Weighted Optimal Transport (WOT) for domain alignment by leveraging instance weights.
FedLPA: Personalized One-shot Federated Learning with Layer-Wise Posterior Aggregation
Efficiently aggregating trained neural networks from local clients into a global model on a server is a widely researched topic in federated learning.
Dual-Balancing for Multi-Task Learning
Multi-task learning (MTL), a learning paradigm to learn multiple related tasks simultaneously, has achieved great success in various fields.
A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices
In this paper, we propose a Fast Attention Network (FAN) for joint intent detection and slot filling tasks, guaranteeing both accuracy and latency.
Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning
Multi-Task Learning (MTL) has achieved success in various fields.
Deep Safe Multi-Task Learning
We theoretically study the safeness of both learning strategies in the DSMTL model to show that the proposed methods can achieve some versions of safe multi-task learning.
Multi-Objective Meta Learning
Empirically, we show the effectiveness of the proposed MOML framework in several meta learning problems, including few-shot learning, neural architecture search, domain adaptation, and multi-task learning.
