Label noise is a pervasive problem in deep learning that often compromises
the generalization performance of trained models. Recently, leveraging
privileged information (PI) -- information available only during training but
not at test time -- has emerged as an effective approach to mitigate this
issue. Yet, existing PI-based methods have failed to consistently outperform
their no-PI counterparts in terms of preventing overfitting to label noise. To
address this deficiency, we introduce Pi-DUAL, an architecture designed to
harness PI to distinguish clean from wrong labels. Pi-DUAL decomposes the
output logits into a prediction term, based on conventional input features, and
a noise-fitting term influenced solely by PI. A gating mechanism steered by PI
adaptively shifts focus between these terms, allowing the model to implicitly
separate the learning paths of clean and wrong labels. Empirically, Pi-DUAL
achieves significant performance improvements on key PI benchmarks (e.g., +6.8%
on ImageNet-PI), establishing a new state-of-the-art test set accuracy.
Additionally, Pi-DUAL is a potent method for identifying noisy samples
post-training, outperforming other strong methods at this task. Overall,
Pi-DUAL is a simple, scalable and practical approach for mitigating the effects
of label noise in a variety of real-world scenarios with PI.

Pi-DUAL 是一种利用特权信息来减轻标签噪声的简单、可扩展和实用的方法，通过将输出的逻辑分解为基于传统输入特征的预测项和仅受特权信息影响的噪声适配项，Pi-DUAL 能够自适应地在这两个项之间切换焦点，从而使模型隐性地区分干净标签和错误标签的学习路径，实现了在关键特权信息基准上显著提高性能（例如，在 ImageNet-PI 上提高了 6.8% 的测试集准确率），此外，Pi-DUAL 也是一种在训练后识别嘈杂样本的有效方法，在这方面超过了其他强大的方法。

Pi-DUAL：利用特权信息区分干净和嘈杂标签

Pi-DUAL: Using Privileged Information to Distinguish Clean from Noisy  Labels

Sentiment classification is one the best use case of classical natural
language processing (NLP) where we can witness its power in various daily life
domains such as banking, business and marketing industry. We already know how
classical AI and machine learning can change and improve technology. Quantum
natural language processing (QNLP) is a young and gradually emerging technology
which has the potential to provide quantum advantage for NLP tasks. In this
paper we show the first application of QNLP for sentiment analysis and achieve
perfect test set accuracy for three different kinds of simulations and a decent
accuracy for experiments ran on a noisy quantum device. We utilize the lambeq
QNLP toolkit and $t|ket>$ by Cambridge Quantum (Quantinuum) to bring out the
results.

本文介绍了量子自然语言处理（QNLP）技术在情感分析中的应用，使用了 lambeq QNLP 工具包和 Cambridge Quantum（Quantinuum）的 $t|ket>$ 来实现，针对三种不同类型的模拟实现了完美的测试集准确性并在噪声量子装置上实现了尚可的准确性。

基于量子自然语言处理的情感分析 - lambeq 工具包

Quantum Natural Language Processing based Sentiment Analysis using  lambeq Toolkit

Interpreting the performance of deep learning models beyond test set accuracy
is challenging. Characteristics of individual data points are often not
considered during evaluation, and each data point is treated equally. We
examine the impact of a test set question's difficulty to determine if there is
a relationship between difficulty and performance. We model difficulty using
well-studied psychometric methods on human response patterns. Experiments on
Natural Language Inference (NLI) and Sentiment Analysis (SA) show that the
likelihood of answering a question correctly is impacted by the question's
difficulty. As DNNs are trained with more data, easy examples are learned more
quickly than hard examples.

研究了深度学习模型性能评估中忽略的数据点特征和难度对测试集准确性的影响，通过用已有的心理测量学方法对人类的反应模式进行建模来估计难度，实验结果发现难度对于测试的结果有重要影响，同时易于学习的实例被模型学得更快。