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Circuit Design and Efficient Simulation of Quantum Inner Product and Empirical Studies of Its Effect on Near-Term Hybrid Quantum-Classic Machine Learning

Circuit Design and Efficient Simulation of Quantum Inner Pro...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hao Xiong Yehui Tang Xinyu Ye Junchi Yan Department of Computer Science and Engineering MoE Key Lab of AI Shanghai Jiao Tong University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

For the essential operation, namely inner product (IP) as widely adopted in classic computing e.g. matrix multi-plication, its quantum counterpart: quantum inner product (QIP), has also been recently theoretically explored with a verifiable lower complexity on quantum computers. How-ever, it remains unclear for the embodiment of the quantum circuits (QC) for QIP, let alone a (thorough) evaluation of the QIP circuits, especially in a practical context in the NISQ era by applying QIP to ML via hybrid quantum-classic pipelines. In this paper, we carefully design the QIP circuits from scratch, whose complexity is in accordance with the theoretical complexity. To make the simulation tractable on classic computers, especially when it is integrated in the gradient-based hybrid ML pipelines, we further devise a highly-efficient simulation scheme by directly simulates the output state. Experiments show that the scheme acceler-ates the simulation for more than 68k times compared with the previous circuit simulator. This allows our empirical evaluation on typical machine learning tasks, ranging from supervised and self-supervised learning via neural nets, to K-Means clustering. The results show that the calculation error brought by typical quantum mechanisms would incur in general little influence on the final numerical results given sufficient qubits. However, certain tasks e.g. ranking in K-Means could be more sensitive to quantum noise.

关键词： computers Pipelines Qubit Quantum mechanics Machine learning Self-supervised learning Complexity theory

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Front-End Adapter: Adapting Front-End Input of Speech Based Self-Supervised Learning for Speech Recognition 48

Front-End Adapter: Adapting Front-End Input of Speech Based ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chen, Xie Ma, Ziyang Tang, Changli Wang, Yujin Zheng, Zhisheng Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-Lance Lab Department of Computer Science and Engineering Shanghai China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9781728163277

Recent years have witnessed a boom in self-supervised learning (SSL) in various areas including speech processing. Speech based SSL models present promising performance in a range of speech related tasks. However, the training of SSL models is computationally expensive and a common practice is to fine-tune a released SSL model on the specific task. It is essential to use consistent front-end input during pre-training and fine-tuning. This consistency may introduce potential issues when the optimal front-end is not the same as that used in pre-training. In this paper, we propose a simple but effective front-end adapter to address this front-end discrepancy. By minimizing the distance between the outputs of different front-ends, the filterbank feature (Fbank) can be compatible with SSL models which are pre-trained with waveform. The experiment results demonstrate the effectiveness of our proposed front-end adapter on several popular SSL models for the speech recognition task. © 2023 IEEE.

关键词： Adapter Automatic Speech Recognition Front-end Feature Self-Supervised Learning

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Boosting Order-Preserving and Transferability for Neural Architecture Search: A Joint Architecture Refined Search and Fine-Tuning Approach

Boosting Order-Preserving and Transferability for Neural Arc...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Beichen Zhang Xiaoxing Wang Xiaohan Qin Junchi Yan Department of Computer Science and Engineering & MoE Key Lab of AI Shanghai Jiao Tong University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Supernet is a core component in many recent Neural Architecture Search (NAS) methods. It not only helps embody the search space but also provides a (relative) estimation of the final performance of candidate architectures. Thus, it is critical that the top architectures ranked by a supernet should be consistent with those ranked by true performance, which is known as the order-preserving ability. In this work, we analyze the order-preserving ability on the whole search space (global) and a sub-space of top architectures (local), and empirically show that the local order-preserving for current two-stage NAS methods still need to be improved. To rectify this, we propose a novel concept of Supernet Shifting, a refined search strategy combining architecture searching with supernet fine-tuning. Specifically, apart from evaluating, the training loss is also accumulated in searching and the supernet is updated every iteration. Since superior architectures are sampled more frequently in evolutionary searching, the supernet is encouraged to focus on top architectures, thus improving local order-preserving. Besides, a pre-trained supernet is often un-reusable for one-shot methods. We show that Supernet Shifting can fulfill transferring supernet to a new dataset. Specifically, the last classifier layer will be unset and trained through evolutionary searching. Comprehensive experiments show that our method has better order-preserving ability and can find a dominating architecture. Moreover, the pre-trained supernet can be easily transferred into a new dataset with no loss of performance.

关键词： Training computer vision Estimation computer architecture Search problems Boosting Pattern recognition

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CAM-GUI: A Conversational Assistant on Mobile GUI 18th

CAM-GUI: A Conversational Assistant on Mobile GUI

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18th National Conference on Man-Machine Speech Communication, NCMMSC 2023

作者： Zhu, Zichen Sun, Liangtai Yang, Jingkai Peng, Yifan Zou, Weilin Li, Ziyuan Li, Wutao Chen, Lu Ma, Yingzi Zhang, Danyang Fan, Shuai Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Sichuan University Sichuan Chengdu China AISpeech Ltd. Suzhou China

ISBN: (纸本)9789819706006

Smartphone assistants are becoming more and more popular in our daily lives. These assistants mostly rely on the API-based Task-Oreiented Dialogue (TOD) systems, which limits the generality of these assistants, and the development of APIs would cost much labor and time. In this paper, we develop a Conversational Assistant on Mobile GUI (CAM-GUI), which can directly perform GUI operations on real devices, without the need of TOD-related backend APIs. To evaluate the performance of our assistant, we collect a dataset containing dialogues and GUI operation traces. From the experiment demonstrations and user studies, we show that the CAM-GUI reaches promising results. Our system demonstration can be downloaded from this link. You can watch our demo through this link. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Human computer interaction

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Boosting Order-Preserving and Transferability for Neural Architecture Search: a Joint Architecture Refined Search and Fine-tuning Approach

arXiv

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arXiv 2024年

作者： Zhang, Beichen Wang, Xiaoxing Qin, Xiaohan Yan, Junchi Department of Computer Science and Engineering MoE Key Lab of AI Shanghai Jiao Tong University China

关键词： Iterative methods

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Evolving Subnetwork Training for Large Language Models 41

Evolving Subnetwork Training for Large Language Models

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Hanqi Chen, Lu Ma, Da Wu, Zijian Zhu, Su Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Suzhou Laboratory Suzhou China AISpeech Co. Ltd. Suzhou China

Large language models have ushered in a new era of artificial intelligence research. However, their substantial training costs hinder further development and widespread adoption. In this paper, inspired by the redundancy in the parameters of large language models, we propose a novel training paradigm: Evolving Subnetwork Training (EST). EST samples subnetworks from the layers of the large language model and from commonly used modules within each layer, Multi-Head Attention (MHA) and Multi-Layer Perceptron (MLP). By gradually increasing the size of the subnetworks during the training process, EST can save the cost of training. We apply EST to train GPT2 model and TinyLlama model, resulting in 26.7% FLOPs saving for GPT2 and 25.0% for TinyLlama without an increase in loss on the pre-training dataset. Moreover, EST leads to performance improvements in downstream tasks, indicating that it benefits generalization. Additionally, we provide intuitive theoretical studies based on training dynamics and Dropout theory to ensure the feasibility of EST. Copyright 2024 by the author(s)

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Improving Few-Shot Learning for Talking Face System with TTS Data Augmentation 48

Improving Few-Shot Learning for Talking Face System with TTS...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chen, Qi Ma, Ziyang Liu, Tao Tan, Xu Lu, Qu Yu, Kai Chen, Xie Shanghai Jiao Tong University X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence Ai Institute China Microsoft Research Asia China Shanghai Media Tech China

ISBN: (纸本)9781728163277

Audio-driven talking face has attracted broad interest from academia and industry recently. However, data acquisition and labeling in audio-driven talking face are labor-intensive and costly. The lack of data resource results in poor synthesis effect. To alleviate this issue, we propose to use TTS (Text-To-Speech) for data augmentation to improve few-shot ability of the talking face system. The misalignment problem brought by the TTS audio is solved with the introduction of soft-DTW, which is first adopted in the talking face task. Moreover, features extracted by HuBERT are explored to utilize underlying information of audio, and found to be superior over other features. The proposed method achieves 17%, 14%, 38% dominance on MSE score, DTW score and user study preference repectively over the baseline model, which shows the effectiveness of improving few-shot learning for talking face system with TTS augmentation. © 2023 IEEE.

关键词： few-shot HuBERT low-resource soft-DTW

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Exploring Effective Distillation of Self-Supervised Speech Models for Automatic Speech Recognition

Exploring Effective Distillation of Self-Supervised Speech M...

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2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Wang, Yujin Tang, Changli Ma, Ziyang Zheng, Zhisheng Chen, Xie Zhang, Wei-Qiang Tsinghua University Department of Electronic Engineering China Ai Institute X-LANCE Lab Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350306897

Self-supervised learning (SSL) has achieved great success in speech processing, but always with a large model size to increase the modeling capacity. This may limit its potential applications due to the expensive computation and memory costs introduced by the oversize model. Compression for SSL models has become an important research direction of practical value. To this end, we explore the effective distillation of HuBERT-based SSL models for automatic speech recognition. First, a comprehensive study of different student model structures is conducted. On top of this, as a supplement to the regression loss widely adopted in previous works, a discriminative loss is introduced for HuBERT to enhance the distillation performance, especially in low-resource scenarios. In addition, we design a simple and effective algorithm to distill the front-end input from waveform to Fbank feature, resulting in 17% parameter reduction and doubling inference speed, at marginal performance degradation. © 2023 IEEE.

关键词： Distillation

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Generalizable Audio Deepfake Detection via Latent Space Refinement and Augmentation

Generalizable Audio Deepfake Detection via Latent Space Refi...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Wen Gu, Yanmei Wang, Zhiming Zhu, Huijia Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China SJTU Paris Elite Institute of Technology Shanghai China Ant Group Shanghai China

ISBN: (纸本)9798350368741

Advances in speech synthesis technologies, like text-to-speech (TTS) and voice conversion (VC), have made detecting deepfake speech increasingly challenging. Spoofing countermeasures often struggle to generalize effectively, particularly when faced with unseen attacks. To address this, we propose a novel strategy that integrates Latent Space Refinement (LSR) and Latent Space Augmentation (LSA) to improve the generalization of deepfake detection systems. LSR introduces multiple learnable prototypes for the spoof class, refining the latent space to better capture the intricate variations within spoofed data. LSA further diversifies spoofed data representations by applying augmentation techniques directly in the latent space, enabling the model to learn a broader range of spoofing patterns. We evaluated our approach on four representative datasets, i.e. ASVspoof 2019 LA, ASVspoof 2021 LA and DF, and In-The-Wild. The results show that LSR and LSA perform well individually, and their integration achieves competitive results, matching or surpassing current state-of-the-art methods. © 2025 IEEE.

关键词： anti-spoofing audio deepfake detection generalization

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AdapterShare: Task Correlation Modeling with Adapter Differentiation

AdapterShare: Task Correlation Modeling with Adapter Differe...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Chen, Zhi Chen, Bei Chen, Lu Yu, Kai Lou, Jian-Guang X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Microsoft Research Asia

Thanks to the development of pre-trained language models, multitask learning (MTL) methods have achieved great success in natural language understanding. However, current MTL methods pay more attention to task selection or model design to fuse as much knowledge as possible, while the intrinsic task correlation is often neglected. It is important to learn sharing strategies among multiple tasks rather than sharing everything. In this paper, we propose AdapterShare, an adapter differentiation method to explicitly model task correlation among multiple tasks. AdapterShare is automatically learned based on the gradients on tiny held-out validation data. Compared to single-task learning and fully shared MTL methods, our proposed method obtains obvious performance improvements. Compared to the existing MTL method AdapterFusion, AdapterShare achieves an absolute average improvement of 1.90 points on five dialogue understanding tasks and 2.33 points on NLU tasks. Our implementation is available at https://***/microsoft/ContextualSP. © 2022 Association for Computational Linguistics.

关键词： Learning systems

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