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 …

Ultrathin, molecular-sieving membranes have great potential to realize high-flux, high-selectivity
mixture separation at low energy cost. Current microporous membranes [pore size < 1 …

We address an important problem in sequence-to-sequence (Seq2Seq) learning referred to
as copying, in which certain segments in the input sequence are selectively replicated in the …

Semantic matching is of central importance to many natural language tasks\cite {bordes2014semantic,
RetrievalQA}. A successful matching algorithm needs to adequately model the …

We propose Neural Responding Machine (NRM), a neural network-based response
generator for Short-Text Conversation. NRM takes the general encoder-decoder framework: it …

Few-shot learning is challenging for learning algorithms that learn each task in isolation and
from scratch. In contrast, meta-learning learns from many related tasks a meta-learner that …

From January to February 2020, self-administered online questionnaires were distributed to
700 individuals, consisting of physicians and nurses who worked in the designated …

In this paper we address the issue of learning to rank for document retrieval. In the task, a
model is automatically created with some training data and then is utilized for ranking of …

Health professions preventing and controlling Coronavirus Disease 2019 are prone to skin
and mucous membrane injury, which may cause acute and chronic dermatitis, secondary …

The paper is concerned with applying learning to rank to document retrieval. Ranking SVM
is a typical method of learning to rank. We point out that there are two factors one must …