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Found 20 papers, 4 papers with code
Data Optimization in Deep Learning: A Survey
Consequently, a huge number of studies in the existing literature have focused on the data aspect in deep learning tasks.
Rethinking Class Imbalance in Machine Learning
Imbalance learning is a subfield of machine learning that focuses on learning tasks in the presence of class imbalance.
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.
Class-Level Logit Perturbation
However, limited studies have explicitly explored for the perturbation of logit vectors.
Exploring the Learning Difficulty of Data Theory and Measure
As learning difficulty is crucial for machine learning (e. g., difficulty-based weighting learning strategies), previous literature has proposed a number of learning difficulty measures.
Tackling the Imbalance for GNNs
Different from deep neural networks for non-graph data classification, graph neural networks (GNNs) leverage the information exchange between nodes (or samples) when representing nodes.
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.
Compensation Learning
Furthermore, a family of new learning algorithms can be obtained by plugging the compensation learning into existing learning algorithms.
A Mathematical Foundation for Robust Machine Learning based on Bias-Variance Trade-off
Conducting a clear comparison for existing RML algorithms in dealing with different samples is difficult due to lack of a unified theoretical framework for RML.
Improving the Expressive Power of Graph Neural Network with Tinhofer Algorithm
In recent years, Graph Neural Network (GNN) has bloomly progressed for its power in processing graph-based data.
AI Marker-based Large-scale AI Literature Mining
Inspired by the idea of molecular markers tracing in the field of biochemistry, three named entities, namely, methods, datasets and metrics are used as AI markers for AI literature.
Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention
We finally apply our model on PAKDD papers published from 2009-2019 to mine insightful results from scientific papers published in a longer time span.
Deep Human Answer Understanding for Natural Reverse QA
This study focuses on a reverse question answering (QA) procedure, in which machines proactively raise questions and humans supply the answers.
Method and Dataset Mining in Scientific Papers
In the field of machine learning, the involved methods (M) and datasets (D) are key information in papers.
Semi-interactive Attention Network for Answer Understanding in Reverse-QA
Question answering (QA) is an important natural language processing (NLP) task and has received much attention in academic research and industry communities.
Detecting Adversarial Examples via Key-based Network
In contrast to existing defense methods, the proposed method does not require knowledge of the process for generating adversarial examples and can be applied to defend against different types of attacks.
$ρ$-hot Lexicon Embedding-based Two-level LSTM for Sentiment Analysis
Lexical cues are useful for sentiment analysis, and they have been utilized in conventional studies.
