While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …

Deep Learning (DL) techniques for Natural Language Processing have been evolving
remarkably fast. Recently, the DL advances in language modeling, machine translation, and …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

Deep neural networks excel at function approximation, yet they are typically trained from
scratch for each new function. On the other hand, Bayesian methods, such as Gaussian …

Neural Processes (NPs)(Garnelo et al 2018a; b) approach regression by learning to map a
context set of observed input-output pairs to a distribution over regression functions. Each …

Many different methods to train deep generative models have been introduced in the past. In
this paper, we propose to extend the variational auto-encoder (VAE) framework with a new …

We present a meta-learning approach for adaptive text-to-speech (TTS) with few data.
During training, we learn a multi-speaker model using a shared conditional WaveNet core …

Neural attention has become central to many state-of-the-art models in natural language
processing and related domains. Attention networks are an easy-to-train and effective …

Abstract Few-Shot 3D Point Cloud Object Detection (FS3D) is a challenging task, aiming to
detect 3D objects of novel classes using only limited annotated samples for training …

This paper focuses on the task of cross domain few-shot named entity recognition (NER),
which aims to adapt the knowledge learned from source domain to recognize named entities …