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ChemDFM-X: towards large multimodal model for chemistry

science China(Information sciences) 2024年第12期67卷 99-100页

作者： Zihan ZHAO Bo CHEN Jingpiao LI Lu CHEN Liyang WEN Pengyu WANG Zichen ZHU Danyang ZHANG Yansi LI Zhongyang Dai Xin CHEN Kai YU X-LANCE Lab Department of Computer Science and EngineeringMoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Suzhou Laboratory

Chemistry, as a naturally multimodal discipline, plays a crucial role in various vital fields such as pharmaceutical research and material manufacturing. Therefore, research on artificial intelligence(ai) for chemistry has garnered increasing attention. Despite the rapid development, most of the chemical ai models today mainly focus on single tasks with unimodal input [1].

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Towards Practical Edge Inference Attacks Against Graph Neural Networks 48

Towards Practical Edge Inference Attacks Against Graph Neura...

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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...

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

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

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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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Learning Divergence Fields for Shift-Robust Graph Representations 41

Learning Divergence Fields for Shift-Robust Graph Representa...

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41st International Conference on Machine Learning, ICML 2024

作者： Wu, Qitian Nie, Fan Yang, Chenxiao Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University Shanghai China

Real-world data generation often involves certain geometries (e.g., graphs) that induce instance-level interdependence. This characteristic makes the generalization of learning models more difficult due to the intricate interdependent patterns that impact data-generative distributions and can vary from training to testing. In this work, we propose a geometric diffusion model with learnable divergence fields for the challenging generalization problem with interdependent data. We generalize the diffusion equation with stochastic diffusivity at each time step, which aims to capture the multi-faceted information flows among interdependent data. Furthermore, we derive a new learning objective through causal inference, which can guide the model to learn generalizable patterns of interdependence that are insensitive across domains. Regarding practical implementation, we introduce three model instantiations that can be considered as the generalized versions of GCN, GAT, and Transformers, respectively, which possess advanced robustness against distribution shifts. We demonstrate their promising efficacy for out-of-distribution generalization on diverse real-world datasets. Source codes are available at https://***/fannie1208/GLIND. Copyright 2024 by the author(s)

关键词： Stochastic systems

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Attack Named Entity Recognition by Entity Boundary Interference 30

Attack Named Entity Recognition by Entity Boundary Interfere...

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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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MILP-FBGen: LP/MILP Instance Generation with Feasibility/Boundedness 41

MILP-FBGen: LP/MILP Instance Generation with Feasibility/Bou...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Yahong Fan, Chenchen Chen, Donghui Li, Congrui Ouyang, Wenli Zhu, Mingda Yan, Junchi AI Lab Lenovo Research Beijing China School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University Shanghai China

Machine learning (ML) has been actively adopted in Linear Programming (LP) and Mixed-Integer Linear Programming (MILP), whose potential is hindered by instance *** synthetic instance generation methods often fall short in closely mirroring the distribution of original datasets or ensuring the feasibility and boundedness of the generated data - a critical requirement for obtaining reliable supervised labels in model *** this paper, we present a diffusion-based LP/MILP instance generative framework called *** strikes a balance between structural similarity and novelty while maintaining feasibility/boundedness via a meticulously designed structure-preserving generation module and a feasibility/boundedness-constrained sampling *** method shows superiority on two fronts: 1) preservation of key properties (hardness, feasibility, and boundedness) of LP/MILP instances, and 2) enhanced performance on downstream *** studies show two-fold superiority that our method ensures higher distributional similarity and 100% feasibility in both easy and hard datasets, surpassing current state-of-the-art techniques. Copyright 2024 by the author(s)

关键词： Mixed-integer linear programming

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ACM-MILP: Adaptive Constraint Modification via Grouping and Selection for Hardness-Preserving MILP Instance Generation 41

ACM-MILP: Adaptive Constraint Modification via Grouping and ...

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41st International Conference on Machine Learning, ICML 2024

作者： Guo, Ziao Li, Yang Liu, Chang Ouyang, Wenli Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University Shanghai China AI Lab Lenovo Research Beijing China

Data plays a pivotal role in the development of both classic and learning-based methods for Mixed-Integer Linear Programming (MILP). However, the scarcity of data in real-world applications underscores the necessity for MILP instance generation methods. Currently, these methods primarily rely on iterating random single-constraint modifications, disregarding the underlying problem structure with constraint interrelations, thereby leading to compromised quality and solvability. In this paper, we propose ACM-MILP, a framework for MILP instance generation, to achieve adaptive constraint modification and constraint interrelation modeling. It employs an adaptive constraint selection mechanism based on probability estimation within the latent space to preserve instance characteristics. Meanwhile, it detects and groups strongly related constraints through community detection, enabling collective modifications that account for constraint dependencies. Experimental results show significant improvements in problem-solving hardness similarity under our framework. Additionally, in the downstream task, we showcase the efficacy of our generated instances for hyperparameter tuning. Source code is available: https://***/Thinklab-SJTU/ACM-MILP. Copyright 2024 by the author(s)

关键词： Mixed-integer linear programming

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Exploiting Persistent CPU Cache for Scalable Persistent Hash Index 40

Exploiting Persistent CPU Cache for Scalable Persistent Hash...

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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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Efficient Text-Only Domain Adaptation For CTC-Based ASR

Efficient Text-Only Domain Adaptation For CTC-Based ASR

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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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