30 citations · 63 across the 10 of their papers we have counts for
10 papers
Line-Coupled Language Model
Shiyuan Li, Shaorong Zhang, Zhaorui Yang +3
Autoregressive language models generate one token per decoding step, limiting the useful output of each forward pass. Although diffusion models, insertion-based decoding, and multi…
Robust Vision-Language Models via Tensor Decomposition: A Defense Against Adversarial Attacks
Het Patel, Muzammil Allie, Qian Zhang +2
Vision language models (VLMs) excel in multimodal understanding but are prone to adversarial attacks. Existing defenses often demand costly retraining or significant architecture c…
Clustering and Mining Accented Speech for Inclusive and Fair Speech Recognition
Jaeyoung Kim, Han Lu, Soheil Khorram +3
Modern automatic speech recognition (ASR) systems are typically trained on more than tens of thousands hours of speech data, which is one of the main factors for their great succes…
VAR-CLIP: Text-to-Image Generator with Visual Auto-Regressive Modeling
Qian Zhang, Xiangzi Dai, Ninghua Yang +3
VAR is a new generation paradigm that employs 'next-scale prediction' as opposed to 'next-token prediction'. This innovative transformation enables auto-regressive (AR) transformer…
MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition
Chuanguang Yang, Zhulin An, Helong Zhou +5
Unlike the conventional Knowledge Distillation (KD), Self-KD allows a network to learn knowledge from itself without any guidance from extra networks. This paper proposes to perfor…
Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition
Chuanguang Yang, Zhulin An, Helong Zhou +3
The teacher-free online Knowledge Distillation (KD) aims to train an ensemble of multiple student models collaboratively and distill knowledge from each other. Although existing on…
Contrastive Siamese Network for Semi-supervised Speech Recognition
Soheil Khorram, Jaeyoung Kim, Anshuman Tripathi +3
This paper introduces contrastive siamese (c-siam) network, an architecture for leveraging unlabeled acoustic data in speech recognition. c-siam is the first network that extracts…
Reducing Streaming ASR Model Delay with Self Alignment
Jaeyoung Kim, Han Lu, Anshuman Tripathi +2
Reducing prediction delay for streaming end-to-end ASR models with minimal performance regression is a challenging problem. Constrained alignment is a well-known existing approach…
Transformer Transducer: One Model Unifying Streaming and Non-streaming Speech Recognition
Anshuman Tripathi, Jaeyoung Kim, Qian Zhang +2
In this paper we present a Transformer-Transducer model architecture and a training technique to unify streaming and non-streaming speech recognition models into one model. The mod…
Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss
Qian Zhang, Han Lu, Hasim Sak +4
In this paper we present an end-to-end speech recognition model with Transformer encoders that can be used in a streaming speech recognition system. Transformer computation blocks…