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Found 10 papers, 3 papers with code
Exposing Bias in Online Communities through Large-Scale Language Models
This work utilises the flaw of bias in language models to explore the biases of six different online communities.
Self-Supervised Pretraining of Graph Neural Network for the Retrieval of Related Mathematical Expressions in Scientific Articles
This is mainly due to the retrieval with keyword queries: technical terms differ in different sciences or at different times.
Explaining Deep Learning Representations by Tracing the Training Process
We propose a novel explanation method that explains the decisions of a deep neural network by investigating how the intermediate representations at each layer of the deep network were refined during the training process.
Noisy Labels for Weakly Supervised Gamma Hadron Classification
Gamma hadron classification, a central machine learning task in gamma ray astronomy, is conventionally tackled with supervised learning.
Bit Error Tolerance Metrics for Binarized Neural Networks
In this study, our objective is to investigate the internal changes in the NNs that bit flip training causes, with a focus on binarized NNs (BNNs).
Fighting Filterbubbles with Adversarial BERT-Training for News-Recommendation
Our experiments show that the features we can extract this way are significantly less predictive of the news outlet and thus offer the possibility to reduce the risk of manifestation of new filter bubbles.
Generalized Negative Correlation Learning for Deep Ensembling
Ensemble algorithms offer state of the art performance in many machine learning applications.
Very Fast Streaming Submodular Function Maximization
Data summarization has become a valuable tool in understanding even terabytes of data.
Towards Explainable Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks
Finally, we explore the influence of a novel regularizer that optimizes with respect to this metric, with the aim of providing a configurable trade-off in accuracy and BET.
Evolution of Eigenvalue Decay in Deep Networks
The linear transformations in converged deep networks show fast eigenvalue decay.
