Human motion generation aims to generate natural human pose sequences an...
Explainable question answering (XQA) aims to answer a given question and...
In passage retrieval system, the initial passage retrieval results may b...
Efficiently capturing the long-range patterns in sequential data sources...
Traditional models of glucose-insulin dynamics rely on heuristic
paramet...
In this work, we are dedicated to leveraging the BERT pre-training succe...
We provide a first finite-particle convergence rate for Stein variationa...
In the past few years, the emergence of vision-language pre-training (VL...
Subject to the semantic gap lying between natural and formal language, n...
Learning the principal eigenfunctions of an integral operator defined by...
Gradient estimation – approximating the gradient of an expectation with
...
Dependency parsing aims to extract syntactic dependency structure or sem...
Stochastic gradient-based optimisation for discrete latent variable mode...
Semantic parsing in KBQA aims to parse natural language questions into
l...
Wikipedia abstract generation aims to distill a Wikipedia abstract from ...
We introduce a new family of particle evolution samplers suitable for
co...
We introduce a new scalable variational Gaussian process approximation w...
Multi-hop Question Answering (QA) is a challenging task because it requi...
Complex question answering over knowledge base (Complex KBQA) is challen...
Estimating the score, i.e., the gradient of log density function, from a...
Today's scene graph generation (SGG) task is still far from practical, m...
We introduce a new interpretation of sparse variational approximations f...
Inference in Gaussian process (GP) models is computationally challenging...
Score matching is a popular method for estimating unnormalized statistic...
Variational Bayesian neural networks (BNNs) perform variational inferenc...
We aim to dismantle the prevalent black-box neural architectures used in...
We propose DeepChannel, a robust, data-efficient, and interpretable neur...
Sentence embedding is an effective feature representation for most deep
...
We consider the semi-supervised clustering problem where crowdsourcing
p...
Recently there have been increasing interests in learning and inference ...
In this paper we introduce ZhuSuan, a python probabilistic programming
l...
Recent progress in variational inference has paid much attention to the
...
Deep convolutional neural networks (CNNs) have achieved breakthrough
per...
We present a new perspective on neural knowledge base (KB) embeddings, f...