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Found 20 papers, 8 papers with code
Eliminating the Invariance on the Loss Landscape of Linear Autoencoders
For this new loss, we characterize the full structure of the loss landscape in the following sense: we establish analytical expression for the set of all critical points, show that it is a subset of critical points of MSE, and that all local minima are still global.
GO4Align: Group Optimization for Multi-Task Alignment
This paper proposes \textit{GO4Align}, a multi-task optimization approach that tackles task imbalance by explicitly aligning the optimization across tasks.
Any-Shift Prompting for Generalization over Distributions
To effectively encode the distribution information and their relationships, we further introduce a transformer inference network with a pseudo-shift training mechanism.
Episodic Multi-Task Learning with Heterogeneous Neural Processes
This paper focuses on the data-insufficiency problem in multi-task learning within an episodic training setup.
Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks
The variety and complexity of relations in multimedia data lead to Heterogeneous Information Networks (HINs).
Learning Variational Neighbor Labels for Test-Time Domain Generalization
We formulate the generalization at test time as a variational inference problem by modeling pseudo labels as distributions to consider the uncertainty during generalization and alleviate the misleading signal of inaccurate pseudo labels.
EMO: Episodic Memory Optimization for Few-Shot Meta-Learning
By learning to retain and recall the learning process of past training tasks, EMO nudges parameter updates in the right direction, even when the gradients provided by a limited number of examples are uninformative.
SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail
Modern image classifiers perform well on populated classes, while degrading considerably on tail classes with only a few instances.
Association Graph Learning for Multi-Task Classification with Category Shifts
To generalize to such test data, it is crucial for individual tasks to leverage knowledge from related tasks.
E^2VTS: Energy-Efficient Video Text Spotting from Unmanned Aerial Vehicles
Unmanned Aerial Vehicles (UAVs) based video text spotting has been extensively used in civil and military domains.
NFormer: Robust Person Re-identification with Neighbor Transformer
To tackle this issue, we propose a Neighbor Transformer Network, or NFormer, which explicitly models interactions across all input images, thus suppressing outlier features and leading to more robust representations overall.
Multi-Task Neural Processes
Our multi-task neural processes methodologically expand the scope of vanilla neural processes and provide a new way of exploring task relatedness in function spaces for multi-task learning.
Variational Multi-Task Learning with Gumbel-Softmax Priors
Multi-task learning aims to explore task relatedness to improve individual tasks, which is of particular significance in the challenging scenario that only limited data is available for each task.
A Bit More Bayesian: Domain-Invariant Learning with Uncertainty
Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data.
UMEC: Unified model and embedding compression for efficient recommendation systems
The recommendation system (RS) plays an important role in the content recommendation and retrieval scenarios.
Learning A Minimax Optimizer: A Pilot Study
We first present Twin L2O, the first dedicated minimax L2O framework consisting of two LSTMs for updating min and max variables, respectively.
Variational Invariant Learning for Bayesian Domain Generalization
In the probabilistic modeling framework, we introduce a domain-invariant principle to explore invariance across domains in a unified way.
Variational Multi-Task Learning
Multi-task learning aims to improve the overall performance of a set of tasks by leveraging their relatedness.
Neural Networks for Principal Component Analysis: A New Loss Function Provably Yields Ordered Exact Eigenvectors
In this paper, we propose a new loss function for performing principal component analysis (PCA) using linear autoencoders (LAEs).
