Modeling Output-Level Task Relatedness in Multi-Task Learning with Feedback Mechanism

• URL: http://arxiv.org/abs/2404.00885v1
• Date: Mon, 1 Apr 2024 03:27:34 GMT
• Title: Modeling Output-Level Task Relatedness in Multi-Task Learning with Feedback Mechanism
• Authors: Xiangming Xi, Feng Gao, Jun Xu, Fangtai Guo, Tianlei Jin,
• Abstract summary: Multi-task learning (MTL) is a paradigm that simultaneously learns multiple tasks by sharing information at different levels. We introduce a posteriori information into the model, considering that different tasks may produce correlated outputs with mutual influences. We achieve this by incorporating a feedback mechanism into MTL models, where the output of one task serves as a hidden feature for another task.
• Score: 7.479892725446205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-task learning (MTL) is a paradigm that simultaneously learns multiple tasks by sharing information at different levels, enhancing the performance of each individual task. While previous research has primarily focused on feature-level or parameter-level task relatedness, and proposed various model architectures and learning algorithms to improve learning performance, we aim to explore output-level task relatedness. This approach introduces a posteriori information into the model, considering that different tasks may produce correlated outputs with mutual influences. We achieve this by incorporating a feedback mechanism into MTL models, where the output of one task serves as a hidden feature for another task, thereby transforming a static MTL model into a dynamic one. To ensure the training process converges, we introduce a convergence loss that measures the trend of a task's outputs during each iteration. Additionally, we propose a Gumbel gating mechanism to determine the optimal projection of feedback signals. We validate the effectiveness of our method and evaluate its performance through experiments conducted on several baseline models in spoken language understanding.

Related papers

• Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis [53.38518232934096]
  Multi-task learning (MTL) is a powerful machine learning paradigm designed to leverage shared knowledge across tasks to improve generalization and performance. We propose an MTL approach at the intersection between task clustering and feature transformation based on a two-phase iterative aggregation of targets and features. In both phases, a key aspect is to preserve the interpretability of the reduced targets and features through the aggregation with the mean, which is motivated by applications to Earth science.
  arXiv  Detail & Related papers  (2024-06-12T08:30:16Z)
• Multi-Task Cooperative Learning via Searching for Flat Minima [8.835287696319641]
  We propose to formulate MTL as a multi/bi-level optimization problem, and therefore force features to learn from each task in a cooperative approach. Specifically, we update the sub-model for each task alternatively taking advantage of the learned sub-models of the other tasks. To alleviate the negative transfer problem during the optimization, we search for flat minima for the current objective function.
  arXiv  Detail & Related papers  (2023-09-21T14:00:11Z)
• SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models [22.472167814814448]
  We propose a new model-based imitation learning algorithm named Separated Model-based Adversarial Imitation Learning (SeMAIL) Our method achieves near-expert performance on various visual control tasks with complex observations and the more challenging tasks with different backgrounds from expert observations.
  arXiv  Detail & Related papers  (2023-06-19T04:33:44Z)
• Task Formulation Matters When Learning Continually: A Case Study in Visual Question Answering [58.82325933356066]
  Continual learning aims to train a model incrementally on a sequence of tasks without forgetting previous knowledge. We present a detailed study of how different settings affect performance for Visual Question Answering.
  arXiv  Detail & Related papers  (2022-09-30T19:12:58Z)
• The Effect of Diversity in Meta-Learning [79.56118674435844]
  Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that task distribution plays a vital role in the model's performance. We study different task distributions on a myriad of models and datasets to evaluate the effect of task diversity on meta-learning algorithms.
  arXiv  Detail & Related papers  (2022-01-27T19:39:07Z)
• Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks [73.63892022944198]
  We present a generic perception architecture named Uni-Perceiver. It processes a variety of modalities and tasks with unified modeling and shared parameters. Results show that our pre-trained model without any tuning can achieve reasonable performance even on novel tasks.
  arXiv  Detail & Related papers  (2021-12-02T18:59:50Z)
• Cross-Task Consistency Learning Framework for Multi-Task Learning [9.991706230252708]
  We propose a new learning framework for 2-task MTL problem. We define two new loss terms inspired by cycle-consistency loss and contrastive learning. We theoretically prove that both losses help the model learn more efficiently and that cross-task consistency loss is better in terms of alignment with the straight-forward predictions.
  arXiv  Detail & Related papers  (2021-11-28T11:55:19Z)
• Variational Multi-Task Learning with Gumbel-Softmax Priors [105.22406384964144]
  Multi-task learning aims to explore task relatedness to improve individual tasks. We propose variational multi-task learning (VMTL), a general probabilistic inference framework for learning multiple related tasks.
  arXiv  Detail & Related papers  (2021-11-09T18:49:45Z)
• Learning Functions to Study the Benefit of Multitask Learning [25.325601027501836]
  We study and quantify the generalization patterns of multitask learning (MTL) models for sequence labeling tasks. Although multitask learning has achieved improved performance in some problems, there are also tasks that lose performance when trained together.
  arXiv  Detail & Related papers  (2020-06-09T23:51:32Z)
• Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction [166.87111665908333]
  We propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL) Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks.
  arXiv  Detail & Related papers  (2020-04-29T02:32:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.