The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

Nowadays speech synthesis or text to speech (TTS), an ability of system to produce human
like natural sounding voice from the written text, is gaining popularity in the field of speech …

Neural network based end-to-end text to speech (TTS) has significantly improved the quality
of synthesized speech. Prominent methods (eg, Tacotron 2) usually first generate mel …

Self-attention is a useful mechanism to build generative models for language and images. It
determines the importance of context elements by comparing each element to the current …

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …

In the rapidly evolving landscape of artificial intelligence (AI), generative large language
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …

Recent work on non-autoregressive neural machine translation (NAT) aims at improving the
efficiency by parallel decoding without sacrificing the quality. However, existing NAT …

Abstract 3D hand pose estimation is still far from a well-solved problem mainly due to the
highly nonlinear dynamics of hand pose and the difficulties of modeling its inherent …

Knowledge distillation (KD) is essential for training non-autoregressive translation (NAT)
models by reducing the complexity of the raw data with an autoregressive teacher model. In …

Abstract Non-autoregressive Transformers (NATs) significantly reduce the decoding latency
by generating all tokens in parallel. However, such independent predictions prevent NATs …