Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

Deep neural networks have been well-known for their superb handling of various machine
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …

Recent success in deep learning has partially been driven by training increasingly
overparametrized networks on ever larger datasets. It is therefore natural to ask: how much …

Estimating the difficulty of a dataset typically involves comparing state-of-the-art models to
humans; the bigger the performance gap, the harder the dataset is said to be. However, this …

Large datasets have become commonplace in NLP research. However, the increased
emphasis on data quantity has made it challenging to assess the quality of data. We …

The memorization effects of deep networks show that they will first memorize training data
with clean labels and then those with noisy labels. The early stopping method therefore can …

The sample selection approach is popular in learning with noisy labels. The state-of-the-art
methods train two deep networks simultaneously for sample selection, which aims to employ …

Training on web-scale data can take months. But much computation and time is wasted on
redundant and noisy points that are already learnt or not learnable. To accelerate training …

Abstract The Randomized Response (RR) algorithm is a classical technique to improve
robustness in survey aggregation, and has been widely adopted in applications with …

In learning with noisy labels, the sample selection approach is very popular, which regards
small-loss data as correctly labeled during training. However, losses are generated on-the …