arXiv research

GACAN combines multi-granularity time series for traffic forecasting.

Nested learning improves model performance on multi-granular tasks.

The clustering ensemble technique aims to combine multiple clusterings into a probably better and more robust clustering and has been receiving an increasing attention in recent years. There are mainly two aspects of limitations in the existing clustering ensemble approaches. Firstly, many approaches lack the ability t…

Develops MgCSL for discovering causal structures in high-dimensional data.

SGQuant reduces GNN memory usage without significant accuracy loss.

Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT). However, due to the especially expensive and labor-intensive labeling, existing public corpora in AT-level are…

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffe…

Conditional Random Fields (CRFs) are undirected graphical models, a special case of which correspond to conditionally-trained finite state machines. A key advantage of these models is their great flexibility to include a wide array of overlapping, multi-granularity, non-independent features of the input. In face of thi…

Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses. In this paper, we present AutoCross, an automatic feature crossing tool provided by 4Paradigm to its customers, ranging from banks, hospitals, to Internet corporations. By perf…

To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and …

In regression problems, the use of TSK fuzzy systems is widely extended due to the precision of the obtained models. Moreover, the use of simple linear TSK models is a good choice in many real problems due to the easy understanding of the relationship between the output and input variables. In this paper we present FRU…

Paper tackles many-class few-shot learning with class hierarchy, improving 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.

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.

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

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.

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…

Gradient surgery improves multi-task learning efficiency.

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.