arXiv research

This paper reviews dMTL and methods for selecting auxiliary tasks.

APT-Gen generates tasks to help RL learn in hard problems.

OCEAN infers online task identities from context variables.

Deep RL multi-task learning outperforms single-task learning on new tasks.

Sharing information between multiple tasks enables algorithms to achieve good generalization performance even from small amounts of training data. However, in a realistic scenario of multi-task learning not all tasks are equally related to each other, hence it could be advantageous to transfer information only between …

Proposes a new method for clustering tasks in multi-task learning.

MBML improves multi-task RL by inferring task identity from state-action pairs.

Multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help of some other related tasks. In this paper, we propose a regularization formulation for learning the relationships between tasks in multi-task learning. This formulation can be viewed as a n…

HydaLearn dynamically adjusts task weights for better MTL performance.

AutoSeM automatically selects and balances auxiliary tasks in MTL.

New distance metric for neural architecture search reduces search space complexity.

HGNN learns augmented features for deep multi-task learning.

We investigate task clustering for deep-learning based multi-task and few-shot learning in a many-task setting. We propose a new method to measure task similarities with cross-task transfer performance matrix for the deep learning scenario. Although this matrix provides us critical information regarding similarity betw…

ST-MAML tackles task ambiguity in meta-learning by encoding tasks with stochastic representations.

New algorithm combines curriculum learning with HER for complex object manipulation tasks.

Transformers infer tasks from context via two modes, geometrically shaped task vectors explain their behavior.

MTL-NAS combines NAS with GP-MTL for task-agnostic multi-task learning.

Meta-learning framework uses task similarity through nonparametric kernel regression.

Novel meta-RL strategy improves efficiency in learning novel tasks.

RTE enables extrapolation to new tasks by learning task transformations.

Study shows perceptual boost of visual attention varies with task difficulty and size.

New approach for multi-task reinforcement learning without task interference.

Meta-RL learns shared and task-specific information for quick adaptation.

Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims at improving the performance of a primary task, by jointly training on a secondary task. This paper introduces automated tasks, which exploit the sequential nature of the input data, as second…

Robot learns multiple tasks hierarchically by transferring knowledge.

In the paradigm of multi-task learning, mul- tiple related prediction tasks are learned jointly, sharing information across the tasks. We propose a framework for multi-task learn- ing that enables one to selectively share the information across the tasks. We assume that each task parameter vector is a linear combi- nat…

Transformers can generalize to a large task family with only a few demonstrations.

Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and thus properly grasping and manipulating the tool to achieve the task. Task-agnostic grasping optimizes for grasp robustness while ignoring crucial task-specific constrain…

Framework adapts to new tasks based on prior knowledge.

Gradient surgery improves multi-task learning efficiency.

Method reweights auxiliary tasks to reduce data need for main task.

SON-GOKU uses graph coloring to improve multi-task learning by partitioning tasks into compatible groups.

Paper tackles graph class-incremental learning with task profiling and prompting.

Optimal task order improves continual learning performance.

Paper presents IMRCs for evolving tasks with forward and backward learning.

Small parameterized towers improve multi-task learning efficiency and generalization.

Single neural network learns multiple tasks from combined data.

Sequential learning of multiple tasks in artificial neural networks using gradient descent leads to catastrophic forgetting, whereby previously learned knowledge is erased during learning of new, disjoint knowledge. Here, we propose a new approach to sequential learning which leverages the recent discovery of adversari…

MT-HAL learns features and task associations for multiple tasks with a shared sparse structure.

Proposes a method to cluster tasks for constructive cooperative multi-tasking.

FedGTEA learns new tasks in federated learning with task embeddings and alignment.

Knowledge transfer between tasks can improve the performance of learned models, but requires an accurate estimate of the inter-task relationships to identify the relevant knowledge to transfer. These inter-task relationships are typically estimated based on training data for each task, which is inefficient in lifelong …

Deep multitask learning boosts performance by sharing learned structure across related tasks. This paper adapts ideas from deep multitask learning to the setting where only a single task is available. The method is formalized as pseudo-task augmentation, in which models are trained with multiple decoders for each task.…

Proposes a simple framework to balance task difficulty in multi-task learning.

Multi-task learning aims to learn multiple tasks jointly by exploiting their relatedness to improve the generalization performance for each task. Traditionally, to perform multi-task learning, one needs to centralize data from all the tasks to a single machine. However, in many real-world applications, data of differen…

FinCausal 2020 task detects financial document causality.

Crowdsourced labeling recovers task types with minimal queries.

In multi-task learning, a learner is given a collection of prediction tasks and needs to solve all of them. In contrast to previous work, which required that annotated training data is available for all tasks, we consider a new setting, in which for some tasks, potentially most of them, only unlabeled training data is …