The recent emergence of Large Language Models based on the Transformer architecture
has enabled dramatic advancements in the field of Natural Language Processing. However …

The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a
unique blend of opportunities and challenges. Although the field has expanded and is …

Adversarial examples expose the vulnerabilities of natural language processing (NLP)
models, and can be used to evaluate and improve their robustness. Existing techniques of …

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 …

Abstract Masked Image Modeling (MIM) achieves outstanding success in self-supervised
representation learning. Unfortunately, MIM models typically have huge computational …

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

In this paper we propose a new generative model of text, Step-unrolled Denoising
Autoencoder (SUNDAE), that does not rely on autoregressive models. Similarly to denoising …

Much recent effort has been invested in non-autoregressive neural machine translation,
which appears to be an efficient alternative to state-of-the-art autoregressive machine …

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 …