In this paper, we study the salient object detection problem for images. We formulate this
problem as a binary labeling task where we separate the salient object from the background. …

… Liu, “Are algorithms directly optimizing IR measures really direct?,” Technical Report, Microsoft
… Liu, “Generalization analysis of listwise learning-to-rank algorithms,” in ICML 2009, 2009. …

Dropout is a powerful and widely used technique to regularize the training of deep neural
networks. Though effective and performing well, the randomness introduced by dropout …

The paper is concerned with learning to rank, which is to construct a model or a function for
ranking objects. Learning to rank is useful for document retrieval, collaborative filtering, and …

Pre-training and fine-tuning, eg, BERT, have achieved great success in language
understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource …

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, helping scientists to generate hypotheses, design experiments, …

Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithm, and has
quite a few effective implementations such as XGBoost and pGBRT. Although many …

Pre-trained language models have attracted increasing attention in the biomedical domain,
inspired by their great success in the general natural language domain. Among the two main …

The Transformer architecture has become a dominant choice in many domains, such as
natural language processing and computer vision. Yet, it has not achieved competitive …

Non-autoregressive text to speech (TTS) models such as FastSpeech can synthesize speech
significantly faster than previous autoregressive models with comparable quality. The …