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Found 8 papers, 1 papers with code
Boosting Model Resilience via Implicit Adversarial Data Augmentation
This insight leads us to develop a meta-learning-based framework for optimizing classifiers with this novel loss, introducing the effects of augmentation while bypassing the explicit augmentation process.
Implicit Counterfactual Data Augmentation for Deep Neural Networks
Machine-learning models are prone to capturing the spurious correlations between non-causal attributes and classes, with counterfactual data augmentation being a promising direction for breaking these spurious associations.
Combining Adversaries with Anti-adversaries in Training
On the basis of our theoretical findings, a more general learning objective that combines adversaries and anti-adversaries with varied bounds on each training sample is presented.
Understanding Difficulty-based Sample Weighting with a Universal Difficulty Measure
In this study, we theoretically prove that the generalization error of a sample can be used as a universal difficulty measure.
Which Samples Should be Learned First: Easy or Hard?
Factors including the distribution of samples' learning difficulties and the validation data determine which samples should be learned first in a learning task.
WHICH SAMPLES SHOULD BE LEARNED FIRST：EASY OR HARD？
Second, a flexible weighting scheme is proposed to overcome the defects of existing schemes.
A Recurrent Model for Collective Entity Linking with Adaptive Features
Traditional machine learning based methods for NED were outperformed and made obsolete by the state-of-the-art deep learning based models.
Antonym-Synonym Classification Based on New Sub-space Embeddings
We hypothesize that the pre-trained embeddings comprehend a blend of lexical-semantic information and we may distill the task-specific information using Distiller, a model proposed in this paper.
