Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Data and its (dis) contents: A survey of dataset development and use in machine learning research
In this work, we survey a breadth of literature that has revealed the limitations of
predominant practices for dataset collection and use in the field of machine learning. We …
predominant practices for dataset collection and use in the field of machine learning. We …
Counterfactual vqa: A cause-effect look at language bias
Recent VQA models may tend to rely on language bias as a shortcut and thus fail to
sufficiently learn the multi-modal knowledge from both vision and language. In this paper …
sufficiently learn the multi-modal knowledge from both vision and language. In this paper …
Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models
T Wu, MT Ribeiro, J Heer, DS Weld - ar** trustworthy ai systems
State-of-the-art AI models largely lack an understanding of the cause-effect relationship that
governs human understanding of the real world. Consequently, these models do not …
governs human understanding of the real world. Consequently, these models do not …
On the value of out-of-distribution testing: An example of goodhart's law
Abstract Out-of-distribution (OOD) testing is increasingly popular for evaluating a machine
learning system's ability to generalize beyond the biases of a training set. OOD benchmarks …
learning system's ability to generalize beyond the biases of a training set. OOD benchmarks …
Evading the simplicity bias: Training a diverse set of models discovers solutions with superior ood generalization
Neural networks trained with SGD were recently shown to rely preferentially on linearly-
predictive features and can ignore complex, equally-predictive ones. This simplicity bias can …
predictive features and can ignore complex, equally-predictive ones. This simplicity bias can …
Counterfactual generative networks
Neural networks are prone to learning shortcuts--they often model simple correlations,
ignoring more complex ones that potentially generalize better. Prior works on image …
ignoring more complex ones that potentially generalize better. Prior works on image …
Learning to contrast the counterfactual samples for robust visual question answering
In the task of Visual Question Answering (VQA), most state-of-the-art models tend to learn
spurious correlations in the training set and achieve poor performance in out-of-distribution …
spurious correlations in the training set and achieve poor performance in out-of-distribution …
Explaining NLP models via minimal contrastive editing (MiCE)
Humans have been shown to give contrastive explanations, which explain why an observed
event happened rather than some other counterfactual event (the contrast case). Despite the …
event happened rather than some other counterfactual event (the contrast case). Despite the …
Mutant: A training paradigm for out-of-distribution generalization in visual question answering
While progress has been made on the visual question answering leaderboards, models
often utilize spurious correlations and priors in datasets under the iid setting. As such …
often utilize spurious correlations and priors in datasets under the iid setting. As such …