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no code implementations • ICML 2020 • Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu

Graph matching, also known as network alignment, aims at recovering the latent vertex correspondence between two unlabeled, edge-correlated weighted graphs.

no code implementations • 28 Nov 2023 • Jinhao Li, Shiyao Li, Jiaming Xu, Shan Huang, Yaoxiu Lian, Jun Liu, Yu Wang, Guohao Dai

Weights are quantized by groups, while the ranges of weights are large in some groups, resulting in large quantization errors and nonnegligible accuracy loss (e. g. >3% for Llama2-7b with 2-bit quantization in GPTQ and Greenbit).

no code implementations • 2 Nov 2023 • Ke Hong, Guohao Dai, Jiaming Xu, Qiuli Mao, Xiuhong Li, Jun Liu, Kangdi Chen, Yuhan Dong, Yu Wang

A single and static dataflow may lead to a 50. 25% performance loss for GEMMs of different shapes in LLM inference.

no code implementations • 31 May 2023 • Lili Su, Jiaming Xu, Pengkun Yang

In this paper, we relax those assumptions and consider adversarial client unavailability.

no code implementations • 25 Sep 2022 • Cheng Mao, Yihong Wu, Jiaming Xu, Sophie H. Yu

We propose an efficient algorithm for graph matching based on similarity scores constructed from counting a certain family of weighted trees rooted at each vertex.

no code implementations • 15 Jun 2022 • Lili Su, Jiaming Xu, Pengkun Yang

This paper studies the problem of model training under Federated Learning when clients exhibit cluster structure.

1 code implementation • 26 May 2022 • Liren Yu, Jiaming Xu, Xiaojun Lin

However, most previous GNNs for this task use a semi-supervised approach, which requires a large number of seeds and cannot learn knowledge that is transferable to unseen graphs.

no code implementations • 22 Feb 2022 • Haoyu Wang, Yihong Wu, Jiaming Xu, Israel Yolou

This paper studies the problem of matching two complete graphs with edge weights correlated through latent geometries, extending a recent line of research on random graph matching with independent edge weights to geometric models.

1 code implementation • 7 Nov 2021 • Qinghua Liu, Yating Huang, Yunzhe Hao, Jiaming Xu, Bo Xu

Multi-modal cues, including spatial information, facial expression and voiceprint, are introduced to the speech separation and speaker extraction tasks to serve as complementary information to achieve better performance.

no code implementations • 22 Oct 2021 • Cheng Mao, Yihong Wu, Jiaming Xu, Sophie H. Yu

We propose a new procedure for testing whether two networks are edge-correlated through some latent vertex correspondence.

no code implementations • 29 Jun 2021 • Lili Su, Jiaming Xu, Pengkun Yang

We discover that when the data heterogeneity is moderate, a client with limited local data can benefit from a common model with a large federation gain.

1 code implementation • 13 Jun 2021 • Yunzhe Hao, Jiaming Xu, Peng Zhang, Bo Xu

In the speaker extraction problem, it is found that additional information from the target speaker contributes to the tracking and extraction of the target speaker, which includes voiceprint, lip movement, facial expression, and spatial information.

no code implementations • 1 May 2021 • Jiaming Xu, Hanjing Zhu

There has been a recent surge of interest in understanding the convergence of gradient descent (GD) and stochastic gradient descent (SGD) in overparameterized neural networks.

no code implementations • 17 Apr 2021 • Xiyun Li, Yong Xu, Meng Yu, Shi-Xiong Zhang, Jiaming Xu, Bo Xu, Dong Yu

The spatial self-attention module is designed to attend on the cross-channel correlation in the covariance matrices.

Automatic Speech Recognition
Automatic Speech Recognition (ASR)
**+2**

no code implementations • 17 Mar 2021 • Jian Ding, Yihong Wu, Jiaming Xu, Dana Yang

Conversely, if $\sqrt{d} B(\mathcal{P},\mathcal{Q}) \ge 1+\epsilon$ for an arbitrarily small constant $\epsilon>0$, the reconstruction error for any estimator is shown to be bounded away from $0$ under both the sparse and dense model, resolving the conjecture in [Moharrami et al. 2019, Semerjian et al. 2020].

no code implementations • 23 Feb 2021 • Liren Yu, Jiaming Xu, Xiaojun Lin

Under the Chung-Lu random graph model with $n$ vertices, max degree $\Theta(\sqrt{n})$, and the power-law exponent $2<\beta<3$, we show that as soon as $D> \frac{4-\beta}{3-\beta}$, by optimally choosing the first slice, with high probability our algorithm can correctly match a constant fraction of the true pairs without any error, provided with only $\Omega((\log n)^{4-\beta})$ initial seeds.

no code implementations • 23 Feb 2021 • Jiaming Xu, Kuang Xu, Dana Yang

Convex optimization with feedback is a framework where a learner relies on iterative queries and feedback to arrive at the minimizer of a convex function.

no code implementations • 29 Jan 2021 • Yihong Wu, Jiaming Xu, Sophie H. Yu

This paper studies the problem of recovering the hidden vertex correspondence between two edge-correlated random graphs.

no code implementations • 29 Nov 2020 • Peng Zhang, Jiaming Xu, Jing Shi, Yunzhe Hao, Bo Xu

In our model, we use the face detector to detect the number of speakers in the scene and use visual information to avoid the permutation problem.

no code implementations • 23 Aug 2020 • Yihong Wu, Jiaming Xu, Sophie H. Yu

We study the problem of detecting the edge correlation between two random graphs with $n$ unlabeled nodes.

no code implementations • 25 Jun 2020 • Jing Shi, Jiaming Xu, Yusuke Fujita, Shinji Watanabe, Bo Xu

With the predicted speaker information from whole observation, our model is helpful to solve the problem of conventional speech separation and speaker extraction for multi-round long recordings.

Audio and Speech Processing Sound

no code implementations • NeurIPS 2020 • Jing Shi, Xuankai Chang, Pengcheng Guo, Shinji Watanabe, Yusuke Fujita, Jiaming Xu, Bo Xu, Lei Xie

This model additionally has a simple and efficient stop criterion for the end of the transduction, making it able to infer the variable number of output sequences.

Ranked #2 on Speech Separation on WSJ0-5mix

1 code implementation • 8 Apr 2020 • Liren Yu, Jiaming Xu, Xiaojun Lin

We establish non-asymptotic performance guarantees of perfect matching for both $1$-hop and $2$-hop algorithms, showing that our new $2$-hop algorithm requires substantially fewer correct seeds than the $1$-hop algorithm when graphs are sparse.

1 code implementation • 18 Dec 2019 • Feilong Chen, Fandong Meng, Jiaming Xu, Peng Li, Bo Xu, Jie zhou

Visual Dialog is a vision-language task that requires an AI agent to engage in a conversation with humans grounded in an image.

no code implementations • 18 Dec 2019 • Mehrdad Moharrami, Cristopher Moore, Jiaming Xu

We study the problem of recovering a planted matching in randomly weighted complete bipartite graphs $K_{n, n}$.

no code implementations • 18 Nov 2019 • Jian Ding, Yihong Wu, Jiaming Xu, Dana Yang

Motivated by applications such as discovering strong ties in social networks and assembling genome subsequences in biology, we study the problem of recovering a hidden $2k$-nearest neighbor (NN) graph in an $n$-vertex complete graph, whose edge weights are independent and distributed according to $P_n$ for edges in the hidden $2k$-NN graph and $Q_n$ otherwise.

no code implementations • 21 Sep 2019 • Jiaming Xu, Kuang Xu, Dana Yang

We study the query complexity of a learner-private sequential learning problem, motivated by the privacy and security concerns due to eavesdropping that arise in practical applications such as pricing and Federated Learning.

no code implementations • 20 Jul 2019 • Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu

We analyze a new spectral graph matching algorithm, GRAph Matching by Pairwise eigen-Alignments (GRAMPA), for recovering the latent vertex correspondence between two unlabeled, edge-correlated weighted graphs.

no code implementations • 20 Jul 2019 • Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu

Departing from prior spectral approaches that only compare top eigenvectors, or eigenvectors of the same order, GRAMPA first constructs a similarity matrix as a weighted sum of outer products between all pairs of eigenvectors of the two graphs, with weights given by a Cauchy kernel applied to the separation of the corresponding eigenvalues, then outputs a matching by a simple rounding procedure.

no code implementations • ACL 2019 • Xiuyi Chen, Jiaming Xu, Bo Xu

Our WMM2Seq adopts a working memory to interact with two separated long-term memories, which are the episodic memory for memorizing dialog history and the semantic memory for storing KB tuples.

no code implementations • NAACL 2019 • Yiqun Yao, Jiaming Xu, Bo Xu

Visual Dialog is a multi-modal task that requires a model to participate in a multi-turn human dialog grounded on an image, and generate correct, human-like responses.

1 code implementation • 6 May 2019 • Wen Chen, Pipei Huang, Jiaming Xu, Xin Guo, Cheng Guo, Fei Sun, Chao Li, Andreas Pfadler, Huan Zhao, Binqiang Zhao

In particular, there exist two requirements for fashion outfit recommendation: the Compatibility of the generated fashion outfits, and the Personalization in the recommendation process.

1 code implementation • 19 Nov 2018 • Jian Ding, Zongming Ma, Yihong Wu, Jiaming Xu

This work develops an $\tilde{O}(n d^2+n^2)$-time algorithm which perfectly recovers the true vertex correspondence with high probability, provided that the average degree is at least $d = \Omega(\log^2 n)$ and the two graphs differ by at most $\delta = O( \log^{-2}(n) )$ fraction of edges.

no code implementations • 15 Nov 2018 • Jing Shi, Jiaming Xu, Yiqun Yao, Bo Xu

In this paper, we present a memory-augmented neural network which is motivated by the process of human concept learning.

no code implementations • 30 Sep 2018 • Xiao-Dong Li, Yudong Chen, Jiaming Xu

We introduce some important theoretical techniques and results for establishing the consistency of convex community detection under various statistical models.

1 code implementation • EMNLP 2018 • Yiqun Yao, Jiaming Xu, Feng Wang, Bo Xu

Our code is available at https://github. com/FlamingHorizon/CMM-VR.

no code implementations • 26 Jul 2018 • Elchanan Mossel, Jiaming Xu

We study a well known noisy model of the graph isomorphism problem.

no code implementations • 26 Apr 2018 • Lili Su, Jiaming Xu

Nevertheless, the empirical risk (sample version) is allowed to be non-convex.

no code implementations • 15 Apr 2018 • Vivek Bagaria, Jian Ding, David Tse, Yihong Wu, Jiaming Xu

Represented as bicolored multi-graphs, these extreme points are further decomposed into simpler "blossom-type" structures for the large deviation analysis and counting arguments.

no code implementations • ICML 2018 • Jiaming Xu

Recent studies have identified the minimax error rate of estimating the graphon from a single realization of the random graph.

2 code implementations • 16 May 2017 • Yudong Chen, Lili Su, Jiaming Xu

The total computational complexity of our algorithm is of $O((Nd/m) \log N)$ at each working machine and $O(md + kd \log^3 N)$ at the central server, and the total communication cost is of $O(m d \log N)$.

no code implementations • 24 Apr 2017 • Sahand Negahban, Sewoong Oh, Kiran K. Thekumparampil, Jiaming Xu

This also allows one to compute similarities among users and items to be used for categorization and search.

1 code implementation • 1 Jan 2017 • Jiaming Xu, Peng Wang, Suncong Zheng, Guanhua Tian, Jun Zhao, Bo Xu

Short text clustering is a challenging problem due to its sparseness of text representation.

Ranked #2 on Short Text Clustering on Stackoverflow

no code implementations • WS 2016 • Jing Shi, Jiaming Xu, Yiqun Yao, Suncong Zheng, Bo Xu

As the result of the evaluation shows, our solution provides a valuable and brief model which could be used in modelling question answering or sentence semantic relevance.

3 code implementations • COLING 2016 • Peng Zhou, Zhenyu Qi, Suncong Zheng, Jiaming Xu, Hongyun Bao, Bo Xu

To integrate the features on both dimensions of the matrix, this paper explores applying 2D max pooling operation to obtain a fixed-length representation of the text.

Ranked #5 on Text Classification on TREC-6

1 code implementation • COLING 2016 • Jiaming Xu, Jing Shi, Yiqun Yao, Suncong Zheng, Bo Xu

Recently, end-to-end memory networks have shown promising results on Question Answering task, which encode the past facts into an explicit memory and perform reasoning ability by making multiple computational steps on the memory.

no code implementations • 20 Feb 2016 • Bruce Hajek, Yihong Wu, Jiaming Xu

We study a semidefinite programming (SDP) relaxation of the maximum likelihood estimation for exactly recovering a hidden community of cardinality $K$ from an $n \times n$ symmetric data matrix $A$, where for distinct indices $i, j$, $A_{ij} \sim P$ if $i, j$ are both in the community and $A_{ij} \sim Q$ otherwise, for two known probability distributions $P$ and $Q$.

no code implementations • 28 Dec 2015 • Yudong Chen, Xiao-Dong Li, Jiaming Xu

We establish non-asymptotic theoretical guarantees for both approximate clustering and perfect clustering.

no code implementations • 30 Oct 2015 • Bruce Hajek, Yihong Wu, Jiaming Xu

The principal submatrix localization problem deals with recovering a $K\times K$ principal submatrix of elevated mean $\mu$ in a large $n\times n$ symmetric matrix subject to additive standard Gaussian noise.

no code implementations • 9 Oct 2015 • Bruce Hajek, Yihong Wu, Jiaming Xu

We show that a belief propagation algorithm achieves weak recovery if $\lambda>1/e$, beyond the Kesten-Stigum threshold by a factor of $1/e.$ The belief propagation algorithm only needs to run for $\log^\ast n+O(1) $ iterations, with the total time complexity $O(|E| \log^*n)$, where $\log^*n$ is the iterated logarithm of $n.$ Conversely, if $\lambda \leq 1/e$, no local algorithm can asymptotically outperform trivial random guessing.

no code implementations • 25 Sep 2015 • Bruce Hajek, Yihong Wu, Jiaming Xu

We study the problem of recovering a hidden community of cardinality $K$ from an $n \times n$ symmetric data matrix $A$, where for distinct indices $i, j$, $A_{ij} \sim P$ if $i, j$ both belong to the community and $A_{ij} \sim Q$ otherwise, for two known probability distributions $P$ and $Q$ depending on $n$.

no code implementations • 10 Sep 2015 • Elchanan Mossel, Jiaming Xu

There is a recent surge of interest in identifying the sharp recovery thresholds for cluster recovery under the stochastic block model.

no code implementations • 10 Aug 2015 • Elchanan Mossel, Jiaming Xu

There has been a recent interest in understanding the power of local algorithms for optimization and inference problems on sparse graphs.

no code implementations • IJCAI 2015 • Jiaming Xu, PengWang, Guanhua Tian, Bo Xu, Jun Zhao, Fangyuan Wang, HongWei Hao

Meanwhile word features and position features are together fed into a convolutional network to learn the implicit features which are further incorporated with the explicit features to fit the pretrained binary code.

no code implementations • NeurIPS 2015 • Sewoong Oh, Kiran K. Thekumparampil, Jiaming Xu

In order to predict the preferences, we want to learn the underlying model from noisy observations of the low-rank matrix, collected as revealed preferences in various forms of ordinal data.

1 code implementation • 10 Mar 2015 • Jiaming Xu, Bo Xu, Guanhua Tian, Jun Zhao, Fangyuan Wang, Hong-Wei Hao

However, topics of certain granularity are not adequate to represent the intrinsic semantic information.

no code implementations • 26 Feb 2015 • Bruce Hajek, Yihong Wu, Jiaming Xu

Extending the proof techniques, in this paper it is shown that SDP relaxations also achieve the sharp recovery threshold in the following cases: (1) Binary stochastic block model with two clusters of sizes proportional to network size but not necessarily equal; (2) Stochastic block model with a fixed number of equal-sized clusters; (3) Binary censored block model with the background graph being Erd\H{o}s-R\'enyi.

no code implementations • 16 Feb 2015 • Rui Wu, Jiaming Xu, R. Srikant, Laurent Massoulié, Marc Lelarge, Bruce Hajek

We propose an efficient algorithm that accurately estimates the individual preferences for almost all users, if there are $r \max \{m, n\}\log m \log^2 n$ pairwise comparisons per type, which is near optimal in sample complexity when $r$ only grows logarithmically with $m$ or $n$.

no code implementations • 11 Feb 2015 • Marc Lelarge, Laurent Massoulié, Jiaming Xu

The labeled stochastic block model is a random graph model representing networks with community structure and interactions of multiple types.

no code implementations • 24 Nov 2014 • Bruce Hajek, Yihong Wu, Jiaming Xu

The binary symmetric stochastic block model deals with a random graph of $n$ vertices partitioned into two equal-sized clusters, such that each pair of vertices is connected independently with probability $p$ within clusters and $q$ across clusters.

no code implementations • 26 Jun 2014 • Jiaming Xu, Laurent Massoulié, Marc Lelarge

The classical setting of community detection consists of networks exhibiting a clustered structure.

no code implementations • 25 Jun 2014 • Bruce Hajek, Yihong Wu, Jiaming Xu

This paper studies the problem of detecting the presence of a small dense community planted in a large Erd\H{o}s-R\'enyi random graph $\mathcal{G}(N, q)$, where the edge probability within the community exceeds $q$ by a constant factor.

no code implementations • NeurIPS 2014 • Bruce Hajek, Sewoong Oh, Jiaming Xu

For a given assignment of items to users, we first derive an oracle lower bound of the estimation error that holds even for the more general Thurstone models.

no code implementations • 6 Feb 2014 • Yudong Chen, Jiaming Xu

Of particular interest is the setting where the number of clusters/submatrices is allowed to grow unbounded with the problem size.

no code implementations • 1 Oct 2013 • Jiaming Xu, Rui Wu, Kai Zhu, Bruce Hajek, R. Srikant, Lei Ying

In standard clustering problems, data points are represented by vectors, and by stacking them together, one forms a data matrix with row or column cluster structure.

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