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SSL4Q: Semi-Supervised Learning of Quantum Data with Application to Quantum State Classification 41

SSL4Q: Semi-Supervised Learning of Quantum Data with Applica...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Tang, Yehui Yang, Nianzu Long, Mabiao Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University Shanghai China

The accurate classification of quantum states is crucial for advancing quantum computing, as it allows for the effective analysis and correct functioning of quantum devices by analyzing the statistics of the data from quantum measurements. Traditional supervised methods, which rely on extensive labeled measurement outcomes, are used to categorize unknown quantum states with different properties. However, the labeling process demands computational and memory resources that increase exponentially with the number of qubits. We propose SSL4Q, manage to achieve (for the first time) semi-supervised learning specifically designed for quantum state classification. SSL4Q’s architecture is tailored to ensure permutation invariance for unordered quantum measurements and maintain robustness in the face of measurement uncertainties. Our empirical studies encompass simulations on two types of quantum systems: the Heisenberg Model and the Variational Quantum Circuit (VQC) Model, with system size reaching up to 50 qubits. The numerical results demonstrate SSL4Q’s superiority over traditional supervised models in scenarios with limited labels, highlighting its potential in efficiently classifying quantum states with reduced computational and resource overhead. Copyright 2024 by the author(s)

关键词： Qubits

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Towards Practical Edge Inference Attacks Against Graph Neural Networks 48

Towards Practical Edge Inference Attacks Against Graph Neura...

引用

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

作者： Li, Kailai Sun, Jiawei Chen, Ruoxin Ding, Wei Yu, Kexue Li, Jie Wu, Chentao Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering China

ISBN: (纸本)9781728163277

Graph Neural Networks (GNNs) have demonstrated superior performance in numerous real-world applications. Despite their success, recent studies have shown that GNNs are vulnerable under edge inference attacks aimed to infer the connectivity of a given pair of nodes. However, existing methods primarily focus on the scenario when properties of target nodes are revealed. In this paper, we propose an edge inference attack in a more realistic and practical setting. In our threat model, the adversary cannot obtain properties of target nodes but can inject a single probing node and query the target GNN for its prediction. By connecting the probing and target nodes, the adversary can infer the connectivity of the target node pair based on the prediction of the probing node. Extensive experiments show that our attack performs comparably to ones that require properties of target nodes. And when given such auxiliary knowledge, our attack outperforms state-of-the-art methods. © 2023 IEEE.

关键词： Graph neural networks

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Fast and High-Quality Auto-Regressive Speech Synthesis via Speculative Decoding

Fast and High-Quality Auto-Regressive Speech Synthesis via S...

引用

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

作者： Li, Bohan Wang, Hankun Zhang, Situo Guo, Yiwei Yu, Kai MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

The auto-regressive (AR) architecture, exemplified by models such as GPT, is extensively utilized in modern Text-to-Speech (TTS) systems. However, it often leads to considerable inference delays, primarily due to the challenges associated with next-token prediction in long speech sequences. In this work, we introduce VADUSA, one of the first approaches to accelerate AR-based TTS through speculative decoding. Our findings demonstrate that VADUSA not only delivers a significant reduction in inference time but also enhances TTS quality by employing draft heads to predict future speech tokens in an auto-regressive manner. Additionally, the incorporation of a tolerance mechanism during the sampling process further boosts performance, yielding approximately a 3× speedup in AR TTS. Moreover, our approach exhibits strong generalization across diverse datasets and various speech token types. © 2025 IEEE.

关键词： inference speedup speculative decoding text-to-speech

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Attack Named Entity Recognition by Entity Boundary Interference 30

Attack Named Entity Recognition by Entity Boundary Interfere...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Yang, Yifei Wu, Hongqiu Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

ISBN: (纸本)9782493814104

Named Entity Recognition (NER) is a cornerstone natural language processing task while its robustness has been given little attention. This paper rethinks the principles of the conventional text attack, as they can easily violate the label consistency between the original and adversarial NER samples. This is due to the fine-grained nature of NER, as even minor word changes in the sentence can result in the emergence or mutation of any entity, producing invalid adversarial samples. To this end, we propose a novel one-word modification NER attack based on a key insight, NER models are always vulnerable to the boundary position of an entity to make their decision. We thus strategically insert a new boundary into the sentence and trigger the victim model to make a wrong recognition either on this boundary word or on other words in the sentence. We call this attack Virtual Boundary Attack (ViBA), which is shown to be remarkably effective when attacking both English and Chinese models with a 70%-90% attack success rate on state-of-the-art language models, and also significantly faster than previous methods. We share the code in https://***/yangyifei729/ViBA. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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Exploiting Persistent CPU Cache for Scalable Persistent Hash Index 40

Exploiting Persistent CPU Cache for Scalable Persistent Hash...

引用

40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhang, Bowen Zheng, Shengan Nie, Liangxu Qi, Zhenlin Huang, Linpeng Mei, Hong Shanghai Jiao Tong University Department of Computer Science and Engineering China AI Institute Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence China

ISBN: (纸本)9798350317152

Byte-addressable persistent memory (PM) has been widely studied in the past few years. Recently, the emerging eADR technology further incorporates CPU cache into the persistence domain. The persistent CPU cache is promising to optimize the write performance of PM-based storage systems and facilitate the design of concurrent crash-consistent data structures. In this paper, we propose Spash, a highly scalable persistent hash index for PM systems with persistent CPU cache. Spash fully exploits the benefits of persistent CPU cache to implement a durable linearizable index with low PM access overhead and high concurrency. Spash employs a fine-grained extendible hash architecture and a metadata-free segment design to minimize the number of PM accesses. Moreover, Spash adopts adaptive in-place updates and compacted-flush insertions, which dramatically conserve scarce PM write bandwidth by absorbing a large amount of PM write in the persistent CPU cache. Furthermore, Spash proposes a two-phase concurrency protocol and a collaborative staged doubling mechanism, which leverage the persistent CPU cache and hardware transactional memory to achieve lock-free concurrency and durable linearizability. Spash outperforms the other state-of-the-art persistent hash indexes in YCSB workloads by up to 19.6×. © 2024 IEEE.

关键词： Concurrency control

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On the Emergence of Cross-Task Linearity in Pretraining-Finetuning Paradigm 41

On the Emergence of Cross-Task Linearity in Pretraining-Fine...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Zhou, Zhanpeng Chen, Zijun Chen, Yilan Zhang, Bo Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University Shanghai China Shanghai Artificial Intelligence Laboratory China Computer Science and Engineering University of California San Diego United States

The pretraining-finetuning paradigm has become the prevailing trend in modern deep *** this work, we discover an intriguing linear phenomenon in models that are initialized from a common pretrained checkpoint and finetuned on different tasks1, termed as Cross-Task Linearity (CTL).Specifically, we show that if we linearly interpolate the weights of two finetuned models, the features in the weight-interpolated model are often approximately equal to the linear interpolation of features in two finetuned models at each *** provide comprehensive empirical evidence supporting that CTL consistently occurs for finetuned models that start from the same pretrained *** conjecture that in the pretraining-finetuning paradigm, neural networks approximately function as linear maps, mapping from the parameter space to the feature *** on this viewpoint, our study unveils novel insights into explaining model merging/editing, particularly by translating operations from the parameter space to the feature ***, we delve deeper into the root cause for the emergence of CTL, highlighting the role of *** released our source code at https://***/zzp1012/Cross-Task-Linearity. Copyright 2024 by the author(s)

关键词： Deep learning

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Efficient Text-Only Domain Adaptation For CTC-Based ASR

Efficient Text-Only Domain Adaptation For CTC-Based ASR

引用

2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Chen, Chang Gong, Xun Qian, Yanmin Ai Institute Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350306897

For connectionist temporal classification (CTC) based speech recognition (ASR) models, text-only domain adaptation still faces several challenges. In this study, we propose an efficient text-only domain adaptation method for CTC-based models. We introduce the assistant textual adapter (ATA) to learn textual features and transform them into the latent space of the acoustic encoder. With the help of the ATA module, the adaptation is achieved by fine-tuning the top layers of the acoustic encoder with the target domain text. Meanwhile, further improvement can be obtained by the integration with shallow fusion (SF). Adapted from LibriSpeech, experiments show that the proposed method can achieve averaged 29.7% relative WER reduction (WERR) compared with the un-adapted baseline on WSJ, and 10.5% WERR compared to SF as well. Moreover, it also shows 15.4∼37.1% WERR for 10 GigaSpeech target domains test sets compared to the un-adapted baseline, and also 6.5% WERR on average compared with SF. © 2023 IEEE.

关键词： Speech recognition

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FAT-HuBERT: Front-End Adaptive Training of Hidden-Unit BERT For Distortion-Invariant Robust Speech Recognition

FAT-HuBERT: Front-End Adaptive Training of Hidden-Unit BERT ...

引用

2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Yang, Dongning Wang, Wei Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350306897

Advancements in monaural speech enhancement (SE) techniques have greatly improved the perceptual quality of speech. However, integrating these techniques into automatic speech recognition (ASR) systems has not yielded the expected performance gains, primarily due to the introduction of distortions during the SE process. In this paper, we propose a novel approach called FAT-HuBERT, which leverages distortion-invariant self-supervised learning (SSL) to enhance the robustness of ASR. To address the distortions introduced by the SE frontends, we introduce layer-wise fusion modules that incorporate features extracted from both observed noisy signals and enhanced signals. During training, the SE frontend is randomly selected from a pool of models. We evaluate the performance of FAT-HuBERT on simulated noisy speech generated from LIBRISPEECH as well as real-world noisy speech from the CHIME-4 1-channel dataset. The experimental results demonstrate a significant relative reduction in word error rate (WER). © 2023 IEEE.

关键词： Speech enhancement

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Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Models 31

Converging to a Lingua Franca: Evolution of Linguistic Regio...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Zeng, Hongchuan Han, Senyu Chen, Lu 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

ISBN: (纸本)9798891761964

Large language models (LLMs) have demonstrated remarkable performance, particularly in multilingual contexts. While recent studies suggest that LLMs can transfer skills learned in one language to others, the internal mechanisms behind this ability remain unclear. We observed that the neuron activation patterns of LLMs exhibit similarities when processing the same language, revealing the existence and location of key linguistic regions. Additionally, we found that neuron activation patterns are similar when processing sentences with the same semantic meaning in different languages. This indicates that LLMs map semantically identical inputs from different languages into a "Lingua Franca", a common semantic latent space that allows for consistent processing across languages. This semantic alignment becomes more pronounced with training and increased model size, resulting in a more language-agnostic activation pattern. Moreover, we found that key linguistic neurons are concentrated in the first and last layers of LLMs, becoming denser in the first layers as training progresses. Experiments on BLOOM and LLaMA2 support these findings, highlighting the structural evolution of multilingual LLMs during training and scaling up. This paper provides insights into the internal workings of LLMs, offering a foundation for future improvements in their cross-lingual capabilities. The codes are available at: https://***/X-LANCE/LinguaFranca. © 2025 Association for Computational Linguistics.

关键词： Neurons

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From Generalist to Specialist: A Survey of Large Language Models for Chemistry 31

From Generalist to Specialist: A Survey of Large Language Mo...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Han, Yang Wan, Ziping Chen, Lu Yu, Kai Chen, Xin 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

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have significantly transformed our daily life and established a new paradigm in natural language processing (NLP). However, the predominant pretraining of LLMs on extensive web-based texts remains insufficient for advanced scientific discovery, particularly in chemistry. The scarcity of specialized chemistry data, coupled with the complexity of multi-modal data such as 2D graph, 3D structure and spectrum, present distinct challenges. Although several studies have reviewed Pretrained Language Models (PLMs) in chemistry, there is a conspicuous absence of a systematic survey specifically focused on chemistry-oriented LLMs. In this paper, we outline methodologies for incorporating domain-specific chemistry knowledge and multi-modal information into LLMs, we also conceptualize chemistry LLMs as agents using chemistry tools and investigate their potential to accelerate scientific research. Additionally, we conclude the existing benchmarks to evaluate chemistry ability of LLMs. Finally, we critically examine the current challenges and identify promising directions for future research. Through this comprehensive survey, we aim to assist researchers in staying at the forefront of developments in chemistry LLMs and to inspire innovative applications in the field. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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