arXiv research

A new score measures data reliability without ground truth.

This paper tackles online reinforcement learning for unseen tasks with unknown boundaries.

Paper presents a GAN-based method to automate robust hedging.

Generative models learn distributions of continuous functions.

FedReLa: A novel data-level approach for imbalanced federated learning

HED Score improves temporal evaluation of detection accuracy.

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.

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.

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.

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

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.

Deep multi-task learning attracts much attention in recent years as it achieves good performance in many applications. Feature learning is important to deep multi-task learning for sharing common information among tasks. In this paper, we propose a Hierarchical Graph Neural Network (HGNN) to learn augmented features fo…

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

RTE enables extrapolation to new tasks by learning task transformations.

In this paper we investigate two hypothesis regarding the use of deep reinforcement learning in multiple tasks. The first hypothesis is driven by the question of whether a deep reinforcement learning algorithm, trained on two similar tasks, is able to outperform two single-task, individually trained algorithms, by more…

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.

Multi-task learning (MTL) has achieved success over a wide range of problems, where the goal is to improve the performance of a primary task using a set of relevant auxiliary tasks. However, when the usefulness of the auxiliary tasks w.r.t. the primary task is not known a priori, the success of MTL models depends on th…

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.

Top-down attention allows people to focus on task-relevant visual information. Is the resulting perceptual boost task-dependent in naturalistic settings? We aim to answer this with a large-scale computational experiment. First, we design a collection of visual tasks, each consisting of classifying images from a chosen …

Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization. Recent efforts apply meta-learning to learn a meta-learner from a set of RL tasks such that a novel but related task could be solved quickly. Though specific in some ways, different tasks in meta-RL…

We tackle the Multi-task Batch Reinforcement Learning problem. Given multiple datasets collected from different tasks, we train a multi-task policy to perform well in unseen tasks sampled from the same distribution. The task identities of the unseen tasks are not provided. To perform well, the policy must infer the tas…

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.

Optimal task order improves continual learning performance.

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

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.

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

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.…

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 …

FinCausal 2020 task detects financial document causality.

Crowdsourced labeling recovers task types with minimal queries.