arxiv_id stringlengths 10 10 | month stringdate 2023-01-01 00:00:00 2024-11-01 00:00:00 | title stringlengths 0 484 | text stringlengths 3.85k 1.94M |
|---|---|---|---|
2301.00004 | 2023-01 | SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering | # SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
Mingchen $\mathrm{Li^{1,4^{\dag}}}$ , Liqi Kang1,2†, Yi Xiong5, Yu Guang Wang1, Guisheng
Fan4, Pan Tan1\*, Liang Hong1,2,3\*
1. Shanghai National Center for Applied Mathematics (SJTU Center), & Institute... |
2301.00006 | 2023-01 | Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing | # Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing
Hyeonsu Jeong 1 Hye Won Chung
# Abstract
Crowdsourcing has emerged as an effective platform for labeling large amounts of data in a costand time-efficient manner. Most previous work has focused on designing an efficient algori... |
2301.00007 | 2023-01 | Selected aspects of complex, hypercomplex and fuzzy neural networks | # Selected aspects of complex, hypercomplex and fuzzy neural networks
edited by Agnieszka Niemczynowicz $^{1}$ and Radostaw A. Kycia $^{2,3}$ $^1$ Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, Poland $^2$ Faculty of Computer Science and Telecommunications, T. Kosciuszko Cra... |
2301.00008 | 2023-01 | Effects of Data Geometry in Early Deep Learning | # Effects of Data Geometry in Early Deep Learning
Saket Tiwari Department of Computer Science Brown University Providence, RI 02906 saket tiwari@brown.edu
George Konidaris Department of Computer Science Brown University Providence, RI 02906
# Abstract
Deep neural networks can approximate functions on differ... |
2301.00011 | 2023-01 | eVAE: Evolutionary Variational Autoencoder | # eVAE: Evolutionary Variational Autoencoder
Zhangkai Wu,1 Longbing Cao, 1 Lei Qi 2
1 University of Technology Sydney 2 Southeast University berenwu1938@gmail.com, Longbing.Cao@uts.edu.au, qilei@seu.edu.cn
# Abstract
The surrogate loss of variational autoencoders (VAEs) poses various challenges to their tra... |
2301.00012 | 2023-01 | GANExplainer: GAN-based Graph Neural Networks Explainer | # GANExplainer: GAN-based Graph Neural Networks Explainer
Yiqiao Li, Jianlong Zhou, Boyuan Zheng, and Fang Chen
University of Technology Sydney, Sydney, Australia yiqiao.li-1@student.uts.edu.au jianlong.zhou@uts.edu.au boyuan.zheng-1@student.uts.edu.au Fang.Chen@uts.edu.au
Abstract. With the rapid deployment o... |
2301.00014 | 2023-01 | Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters | # Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters
T. Barbariol, Università degli Studi di Padova D. Masiero, Università degli Studi di Padova E. Feltresi, Pietro Fiorentini G.A. Susto, Università degli Studi di Padova
# INTRODUCTION
Multiphase flow meters (MPFM) are inline mete... |
2301.00015 | 2023-01 | Self-organization Preserved Graph Structure Learning with Principle of Relevant Information | # Self-organization Preserved Graph Structure Learning with Principle of Relevant Information
Qingyun Sun12, Jianxin $\mathbf{Li}^{12}$ , Beining Yang12, Xingcheng $\mathbf{F}\mathbf{u}^{12}$ , Hao Peng1, Philip S. $\mathbf{Y}\mathbf{u}^{3}$
1 Beijing Advanced Innovation Center for Big Data and Brain Computing, B... |
2301.00032 | 2023-01 | Bayesian Learning for Dynamic Inference | # Bayesian Learning for Dynamic Inference
Aolin Xu Peng Guan
# Abstract
The traditional statistical inference is static, in the sense that the estimate of the quantity of interest does not affect the future evolution of the quantity. In some sequential estimation problems however, the future values of the quan... |
2301.00036 | 2023-01 | Modified Query Expansion Through Generative Adversarial Networks for Information Extraction in E-Commerce | # Modified Query Expansion Through Generative Adversarial Networks for Information Extraction in E-Commerce
Altan Cakir∗,1, Mert Gurkan2
# A R T I C L E I N F O
# A B S T R A C T
Keywords:
Generative Adversarial Networks
Query Expansion
Conditional Neural Networks
Information Retrieval
E-Comm... |
2301.00051 | 2023-01 | Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks | # Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks
Trevor Ablett1, Bryan Chan2, and Jonathan Kelly1
Abstract—Adversarial imitation learning (AIL) has become a popular alternative to supervised imitation learning that reduces the distribution shift suf... |
2301.00061 | 2023-01 | A Global Optimization Algorithm for $K$ -Center Clustering of One Billion Samples | # A Global Optimization Algorithm for $K$ -Center Clustering of One Billion Samples
Jiayang Ren $^{1}$ , Ningning You $^2$ , Kaixun Hua $\mathbf{\rho}_{1}^{1}$ , Chaojie Ji $^{3}$ , Yankai Cao $^{1}$ $\mathbf{\Delta}$ Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, BC, C... |
2301.00092 | 2023-01 | Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves | # Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves
Xiaohong Chen∗ Yuan Liao† Weichen Wang‡
First draft: September 2020, revised January 4, 2023
# Abstract
General nonlinear sieve learnings are classes of nonlinear sieves that can approximate nonlinear functions of ... |
2301.00106 | 2023-01 | Physics-informed Neural Networks approach to solve the Blasius function | # Physics-informed Neural Networks approach to solve the Blasius function
Greeshma Krishna Department of Mathematics Amrita Vishwa Vidyapeetham Amritapuri, India greeshmakrishna $@$ am.students.amrita.edu
Malavika S Nair Department of Mathematics Amrita Vishwa Vidyapeetham Amritapuri, India malavikasnair $@$ am.s... |
2301.00109 | 2023-01 | QUANTUM MACHINE LEARNING APPLIED TO THE CLASSIFICATION OF DIABETES | # QUANTUM MACHINE LEARNING APPLIED TO THE CLASSIFICATION OF DIABETES
Hancco-Quispe Juan Kenyhy Faculty of Statistic and Computer Engineering, Universidad Nacional del Altiplano de Puno, P.O. Box 291 Puno - Peru. Email: jkenyhyhq@gmail.com
Borda-Colque Jordan Piero Faculty of Statistic and Computer Engineering, Un... |
2301.00117 | 2023-01 | Adapting Node-Place Model to Predict and Monitor COVID-19 Footprints and Transmission Risks | # Adapting Node-Place Model to Predict and Monitor COVID-19 Footprints and Transmission Risks
Jiali Zhou
Department of Urban Planning and Design, University of Hong Kong
Address: 8/F, Knowles Building, The University of Hong Kong, Pokfulam Road, Hong Kong
Email: jlzhou@hku.hk
ORCID: https://orcid.org/000... |
2301.00122 | 2023-01 | Hair and Scalp Disease Detection using Machine Learning and Image Processing | # Hair and Scalp Disease Detection using Machine Learning and Image Processing
Mrinmoy Roy and Anica Tasnim Protity
# ABSTRACT
Almost 80 million Americans suffer from hair loss due to aging, stress, medication, or genetic makeup. Hair and scalp-related diseases often go unnoticed in the beginning. Sometimes, a... |
2301.00126 | 2023-01 | Broad learning system with Takagi-Sugeno fuzzy subsystem for tobacco origin identification based on near infrared spectroscopy | # Broad learning system with Takagi-Sugeno fuzzy subsystem for tobacco origin identification based on near infrared spectroscopy
Di Wanga, Simon X. Yangb
$\alpha$ School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China
$^{b}$ School of Engineering, University of Gu... |
2301.00130 | 2023-01 | Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning | # Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning
Wen Wu, Member, IEEE, Peng Yang, Member, IEEE, Weiting Zhang, Student Member, IEEE, Conghao Zhou, Student Member, IEEE, and Xuemin (Sherman) Shen, Fellow, IEEE
Abstract—Collaboration among industrial Internet of Th... |
2301.00134 | 2023-01 | Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment | # Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment
Eleonora Achiluzzi, Menglu Li, Md Fahd Al Georgy, and Rasha Kashef
Toronto Metropolitan University {eachiluzzi, menglu.li, mgeorgy, rkashef} @ryerson.ca
Abstract—The Internet of Things (IoT) is a sys... |
2301.00139 | 2023-01 | ON HIGH DIMENSIONAL POISSON MODELS WITH MEASUREMENT ERROR: HYPOTHESIS TESTING FOR NONLINEAR NONCONVEX OPTIMIZATION | # ON HIGH DIMENSIONAL POISSON MODELS WITH MEASUREMENT ERROR: HYPOTHESIS TESTING FOR NONLINEAR NONCONVEX OPTIMIZATION
BY FEI JIANG $1$ , YEQING ZHOU $^{2,*}$ , JIANXUAN LIU $^{3,\dagger}$ AND YANYUAN MA $^{4,\ddagger}$
$1$ Department of Epidemiology and Biostatistics, The University of California, San Francisco, f... |
2301.00141 | 2023-01 | SELF-ACTIVATING NEURAL ENSEMBLES FOR CONTINUAL REINFORCEMENT LEARNING | # SELF-ACTIVATING NEURAL ENSEMBLES FOR CONTINUAL REINFORCEMENT LEARNING
Sam Powers Carnegie Mellon University snpowers@cs.cmu.edu
Eliot Xing Georgia Institute of Technology exing@gatech.edu
Abhinav Gupta Carnegie Mellon University gabhinav@cs.cmu.edu
# ABSTRACT
The ability for an agent to continuously le... |
2301.00142 | 2023-01 | Computational Charisma – A Brick by Brick Blueprint for Building Charismatic Artificial Intelligence | # Computational Charisma – A Brick by Brick Blueprint for Building Charismatic Artificial Intelligence
Bjorn W. Schuller 1,2, Shahin Amiriparian 1, Anton Batliner 1,
Alexander Gebhard 1, Maurice Gerzcuk 1, Vincent Karas 1, Alexander Kathan 1,
Lennart Seizer ?, Johanna Lochner 3
1EIHW – Chair of Embedded Int... |
2301.00152 | 2023-01 | Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents | # Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents
SAYAR GHOSH ROY, IIIT Hyderabad, India ANSHUL PADHI, IIIT Hyderabad, India RISUBH JAIN, IIIT Hyderabad, India MANISH GUPTA, IIIT Hyderabad, India and Microsoft, India VASUDEVA VARMA, IIIT Hyderabad, India
Multi... |
2301.00181 | 2023-01 | Smooth Mathematical Function from Compact Neural Networks | # Smooth Mathematical Function from Compact Neural Networks
I. K. Hong1a
1Department of Physics and IPAP, Yonsei University, Seoul 03722, Korea Abstract
This is paper for the smooth function approximation by neural networks (NN). Mathematical or physical functions can be replaced by NN models through regressio... |
2301.00188 | 2023-01 | New Challenges in Reinforcement Learning: A Survey of Security and Privacy | # New Challenges in Reinforcement Learning: A Survey of Security and Privacy
Yunjiao Lei $^{1}$ , Dayong Ye $\cdot^{1}$ , Sheng Shen $\mathbf{\Psi}_{.}^{1}$ , Yulei Sui $^{\mathrm{~1~}}{}$ , Tianqing Zhu1\* and Wanlei Zhou $^2$
1\*School of Computer Science, University of Technology Sydney, Broadway, Sydney, 2007... |
2301.00189 | 2023-01 | Mapping Knowledge Representations to Concepts: A Review and New Perspectives | # Mapping Knowledge Representations to Concepts: A Review and New Perspectives
Lars Holmberg,1\* Paul Davidsson, 1 Per Linde 2
1 Department of Computer Science and Media Technology 2 School of Arts and Communication Malmo University, Sweden lars.holmberg@mau.se, paul.davidsson $@$ mau.se, per.linde@mau.se
# Ab... |
2301.00201 | 2023-01 | EXPLORING SINGULARITIES IN DATA WITH THE GRAPH LAPLACIAN: AN EXPLICIT APPROACH | # EXPLORING SINGULARITIES IN DATA WITH THE GRAPH LAPLACIAN: AN EXPLICIT APPROACH
MARTIN ANDERSSON AND BENNY AVELIN
Abstract. We develop theory and methods that use the graph Laplacian to analyze the geometry of the underlying manifold of datasets. Our theory provides theoretical guarantees and explicit bounds on ... |
2301.00216 | 2023-01 | An Efficient Hierarchical Kriging Modeling Method for High | # An Efficient Hierarchical Kriging Modeling Method for High
# dimension Multi-fidelity Problems
Youwei He, Jinliang Luo\*
School of Mechanical Engineering, University of South China, Hengyang 421001, China
Abstract: Multi-fidelity Kriging model is a promising technique in surrogate-based design as it can b... |
2301.00241 | 2023-01 | CONTEXTUAL BANDITS AND OPTIMISTICALLY UNIVERSAL LEARNING | # CONTEXTUAL BANDITS AND OPTIMISTICALLY UNIVERSAL LEARNING
BY MOÏSE BLANCHARD\* STEVE HANNEKE $^\dagger$ , PATRICK JAILLET‡ $^*$ Massachusetts Institute of Technology, moiseb@mit.edu †Purdue University, steve.hanneke@gmail.com ‡Massachusetts Institute of Technology, jaillet@mit.edu
We consider the contextual band... |
2301.00243 | 2023-01 | Approaching Peak Ground Truth | # Approaching Peak Ground Truth
Florian Kofler $^{1,2,3,4}$ , Johannes Wahle $^{14,1}$ , Ivan Ezhov $^{2,3}$ , Sophia Wagner $^{1,2}$ , Rami Al-Maskari $^{2,6}$ , Emilia Gryska $^{11}$ , Mihail Todorov $^{6,7}$ , Christina Bukas $^{1}$ , Felix Meissen $^{2,13}$ , Tingying Peng $^{1,2}$ , Ali Ertürk $^{6,7,8,9}$ , Da... |
2301.00252 | 2023-01 | A Comparative Study of Image Disguising Methods for Confidential Outsourced Learning | # A Comparative Study of Image Disguising Methods for Confidential Outsourced Learning
Sagar Sharma Bytedance Seattle, WA sagar.sharma $@$ bytedance.com
Yuechun Gu, Keke Chen Trustworthy and Intelligent Computing Lab Marquette University, Milwaukee WI {ethan.gu, keke.chen}@marquette.edu
# Abstract
Large tra... |
2301.00265 | 2023-01 | Source-Free Unsupervised Domain Adaptation: A Survey | # Source-Free Unsupervised Domain Adaptation: A Survey
Yuqi Fang, Pew-Thian Yap, Weili Lin, Hongtu Zhu, and Mingxia Liu
Abstract—Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different domains. ... |
2301.00270 | 2023-01 | NETEFFECT: Discovery and Exploitation of Generalized Network Effects | # NETEFFECT: Discovery and Exploitation of Generalized Network Effects
Meng-Chieh Lee1, Shubhranshu Shekhar2, Jaemin $\mathrm{Yoo^{3}}$ , and Christos Faloutsos1
1 Carnegie Mellon University, Pittsburgh, USA
{mengchil, christos}@cs.cmu.edu 2 Brandeis University, Waltham, USA sshekhar@brandeis.edu 3 KAIST, Seou... |
2301.00281 | 2023-01 | Lightmorphic Signatures Analysis Toolkit | # Lightmorphic Signatures Analysis Toolkit
dumitrudamian@yahoo.com
Dumitru Damian
Information and Communication Engineering
Research and development consultant
Timisoara, RO
# Abstract
In this paper we discuss the theory used in the design of an open source lightmorphic signatures analysis toolkit ... |
2301.00301 | 2023-01 | Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy | # Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy
Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang
University of California, Santa Barbara {rredberg, yuqingzhu, yuxiangw}@ucsb.edu
January 3, 2023
# Abstract
The “Propose-Test-Release” (PTR) framework [Dwork and Lei, ... |
2301.00314 | 2023-01 | Causal Deep Learning | # Causal Deep Learning
M. Alex O. Vasilescu⋆ B IPAM, University of California, Los Angeles CA, USA Tensor Vision, Los Angeles CA, USA
Abstract. We derive a set of causal deep neural networks whose architectures are a consequence of tensor (multilinear) factor analysis, a framework that facilitates causal inferenc... |
2301.00327 | 2023-01 | Neural Networks with Sparse Activation Induced by Large Bias: Tighter Analysis with Bias-Generalized NTK | # Neural Networks with Sparse Activation Induced by Large Bias: Tighter Analysis with Bias-Generalized NTK
Hongru Yang UT Austin hy6385@utexas.edu
Ziyu Jiang NEC Labs America jiangziyu@tamu.edu
Ruizhe Zhang
Simons Institute,
UC Berkeley
rzzhang@berkeley.edu
Yingbin Liang Zhangyang Wang OSU UT Austi... |
2301.00328 | 2023-01 | Internet of Things: Digital Footprints Carry A Device Identity | # Internet of Things: Digital Footprints Carry A Device Identity
Rajarshi Roy Chowdhury1, 2, a), Azam Che Idris1 and Pg Emeroylariffion Abas1
1Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei Darussalam
2Department of Computer Science and Engineering, S... |
2301.00330 | 2023-01 | Efficient On-device Training via Gradient Filtering | # Efficient On-device Training via Gradient Filtering
Yuedong Yang Guihong Li Radu Marculescu The University of Texas at Austin {albertyoung, lgh, radum}@utexas.edu
# Abstract
Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open prob... |
2301.00335 | 2023-01 | Pruning Before Training May Improve Generalization, Provably | # Pruning Before Training May Improve Generalization, Provably
Hongru Yang \* Yingbin Liang t Xiaojie Guo $\ddagger$ Lingfei Wus Zhangyang Wang $\P$
# Abstract
It has been observed in practice that applying pruning-at-initialization methods to neural networks and training the sparsified networks can not only ... |
2301.00344 | 2023-01 | Semidefinite programming on population clustering: a global analysis | # Semidefinite programming on population clustering: a global analysis
Shuheng Zhou University of California, Riverside, CA 92521
# Abstract
In this paper, we consider the problem of partitioning a small data sample of size $n$ drawn from a mixture of 2 sub-gaussian distributions. Our work is motivated by the ... |
2301.00345 | 2023-01 | MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction | # MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
Jorge Quesada1∗, Lakshmi Sathidevi1∗, Ran Liu1, Nauman Ahad1, Joy M. Jackson1,
Mehdi Azabou1, Jingyun Xiao1, Christopher Liding1, Matthew $\mathbf{Jin^{1}}$ , Carolina Urzay1, William Gray-Roncal2, Erik ... |
2301.00346 | 2023-01 | An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects | # An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
Thanh Vinh Vo1 Arnab Bhattacharyya1 Young Lee2 Tze-Yun Leong1 1School of Computing, National University of Singapore 2Roche AG and Harvard University {votv,arnabb,leongty}@nus.edu.sg
# Abstract
We propose a new causal inference... |
2301.00351 | 2023-01 | Skew Class-Balanced Re-Weighting for Unbiased Scene Graph Generation | Article
# Skew Class-Balanced Re-Weighting for Unbiased Scene Graph Generation
Haeyong Kang and Chang D. Yoo \*
Citation: Kang, H.; Yoo, C.D. Skew Class-Balanced Re-Weighting for Unbiased Scene Graph Generation. Mach. Learn. Knowl. Extr. 2023, 1, 1–18. https://doi.org/
Academic Editor: Andreas Holzinger
... |
2301.00357 | 2023-01 | A Functional approach for Two Way Dimension Reduction in Time Series | # A Functional approach for Two Way Dimension Reduction in Time Series
Aniruddha Rajendra Rao Haiyan Wang Industrial AI Lab, Hitachi America, Ltd. R&D Industrial AI Lab, Hitachi America, Ltd. R&D Santa Clara, CA Santa Clara, CA
Chetan Gupta Industrial AI Lab, Hitachi America, Ltd. R&D Santa Clara, CA
{Aniruddh... |
2301.00362 | 2023-01 | Goal-guided Transformer-enabled Reinforcement Learning for Efficient Autonomous Navigation | # Goal-guided Transformer-enabled Reinforcement Learning for Efficient Autonomous Navigation
Wenhui Huang, Student Member, IEEE, Yanxin Zhou, Xiangkun He, Member, IEEE, and Chen Lv, Senior Member, IEEE
Abstract—Despite some successful applications of goal-driven navigation, existing deep reinforcement learning (D... |
2301.00363 | 2023-01 | Abstract | Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin
Leikun $\mathrm{Yin}^{1}$ , Rahul Ghosh2, Chenxi Lin1, David Hale3, Christoph Weigl3,4, James Obarowski5,
Junxiong Zhou1, Jessica Till1, Xiaowei Jia6, Troy Mao7, Vipin Kumar2, Zhenong Jin1\*
1 Department of Bioproducts an... |
2301.00364 | 2023-01 | Generalizable Black-Box Adversarial Attack with Meta Learning | # Generalizable Black-Box Adversarial Attack with Meta Learning
Fei Yin∗ , Yong Zhang∗ , Baoyuan Wu∗† , Member, IEEE, Yan Feng, Jingyi Zhang, Yanbo Fan $\textcircled{1}$ , Yujiu Yang† , Member, IEEE
Abstract—In the scenario of black-box adversarial attack, the target model’s parameters are unknown, and the attack... |
2301.00366 | 2023-01 | SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation | # SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation
Kunal Chaturvedi, Ali Braytee, Jun Li, Mukesh Prasad
aSchool of Computer Science, University of Technology Sydney, Ultimo, 2007, NSW, Australia
# Abstract
This paper proposes a novel self-supervised based Cut-... |
2301.00383 | 2023-01 | Discriminative Radial Domain Adaptation | # Discriminative Radial Domain Adaptation
Zenan Huang, Jun Wen, Member, IEEE, Siheng Chen, Member, IEEE, Linchao Zhu, Member, IEEE, and Nenggan Zheng, Senior Member, IEEE
Abstract—Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on dist... |
2301.00384 | 2023-01 | Correlation Clustering Algorithm for Dynamic Complete Signed Graphs: An Index-based Approach | # Correlation Clustering Algorithm for Dynamic Complete Signed Graphs: An Index-based Approach
Ali Shakiba
Department of Computer Science, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran. ali.shakiba@vru.ac.ir;a.shakiba.iran@gmail.com
# Abstract
In this paper, we reduce the complexity of approximating t... |
2301.00389 | 2023-01 | FedICT: Federated Multi-task Distillation for Multi-access Edge Computing | # FedICT: Federated Multi-task Distillation for Multi-access Edge Computing
Zhiyuan Wu, Member, IEEE, Sheng Sun, Yuwei Wang, Member, IEEE, Min Liu, Senior Member, IEEE, Quyang Pan, Xuefeng Jiang, and Bo Gao, Member, IEEE
Abstract—The growing interest in intelligent services and privacy protection for mobile devic... |
2301.00391 | 2023-01 | PiPAD: Pipelined and Parallel Dynamic GNN Training on GPUs | # PiPAD: Pipelined and Parallel Dynamic GNN Training on GPUs
Chunyang Wang
Beihang University
Beijing, China
wangchunyang@buaa.edu.cn
Desen Sun∗
Beihang University
Beijing, China
sy2006344@buaa.edu.cn
Yuebin Bai
Beihang University
Beijing, China
byb@buaa.edu.cn
# Abstract
1 Dyn... |
2301.00393 | 2023-01 | A principled distributional approach to trajectory similarity measurement | # A principled distributional approach to trajectory similarity measurement
Yufan Wang, Kai Ming Ting, Yuanyi Shang Nanjing University Nanjing, China {wangyf,tingkm,shangyy}@lamda.nju.edu.cn
# ABSTRACT
Existing measures and representations for trajectories have two longstanding fundamental shortcomings, i.e., ... |
2301.00395 | 2023-01 | CORGI-PM $\clubsuit$ : A Chinese Corpus For Gender Bias Probing and Mitigation | # CORGI-PM $\clubsuit$ : A Chinese Corpus For Gender Bias Probing and Mitigation
Ge Zhang1 3 4 ∗, Yizhi Li2 ∗, Yaoyao Wu5, Linyuan Zhang 6, Chenghua Lin 2 † , Jiayi Geng7, Shi Wang 3 †, Jie Fu 1
1 Beijing Academy of Artificial Intelligence, China 2 Department of Computer Science, The University of Sheffield, UK ... |
2301.00407 | 2023-01 | MIGPERF: A COMPREHENSIVE BENCHMARK FOR DEEP LEARNING TRAINING AND INFERENCE WORKLOADS ON MULTI-INSTANCE GPUS | # MIGPERF: A COMPREHENSIVE BENCHMARK FOR DEEP LEARNING TRAINING AND INFERENCE WORKLOADS ON MULTI-INSTANCE GPUS
Huaizheng Zhang 1 Yuanming Li 1 Wencong Xiao 1 Yizheng Huang 2 Xing Di 3 Jianxiong Yin 4 Simon See 4 Yong Luo 3 Chiew Tong Lau 5 Yang You 6
# ABSTRACT
New architecture GPUs like A100 are now equipped ... |
2301.00427 | 2023-01 | Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation | # Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation
Han Huang, Leilei Sun, Bowen Du, Weifeng Lv SKLSDE, Beihang University, Beijing, China {h-huang, leileisun, dubowen, lwf}@buaa.edu.cn
# Abstract
Learning the underlying distribution of molecular graphs and generating high... |
2301.00436 | 2023-01 | Hierarchical Explanations for Video Action Recognition | # Hierarchical Explanations for Video Action Recognition
Sadaf Gulshad, Teng Long, Nanne van Noord University of Amsterdam {s.gulshad, t.long, n.j.e.vannoord}@uva.nl
# Abstract
To interpret deep neural networks, one main approach is to dissect the visual input and find the prototypical parts responsible for th... |
2301.00437 | 2023-01 | Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data | # Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data
Hien Dang \* 1 Tho Tran \* 1 Stanley Osher 2 Hung Tran-The 3 Nhat Ho \*\* 4 Tan Nguyen \*\* 5
# Abstract
# 1. Introduction
Modern deep neural networks have achieved impressive performance on tasks from image classification to natura... |
2301.00447 | 2023-01 | Image To Tree with Recursive Prompting | # Image To Tree with Recursive Prompting
James Batten $^{1,2}$ , Matthew Sinclair $^{1,2}$ , Ben Glocker $^{1,2}$ , and Michiel Schaap $^{1,2}$
$^{\mathrm{~1~}}{}$ Imperial College London 2 HeartFlow, Inc.
Abstract. Extracting complex structures from grid-based data is a common key step in automated medical im... |
2301.00448 | 2023-01 | UNSUPERVISED ACOUSTIC SCENE MAPPING BASED ON ACOUSTIC FEATURES AND DIMENSIONALITY REDUCTION | # UNSUPERVISED ACOUSTIC SCENE MAPPING BASED ON ACOUSTIC FEATURES AND DIMENSIONALITY REDUCTION
Idan Cohen, Sharon Gannot and Ofir Lindenbaum
Faculty of Engineering, Bar-Ilan University, Ramat-Gan, 5290002, Israel {Idan.Cohen, Sharon.Gannot, Ofir.Lindenbaum}@biu.ac.il
# ABSTRACT
Classical methods for acoustic... |
2301.00452 | 2023-01 | Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learning | # Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learning
Yonghao Long, Wang Wei, Tao Huang, Yuehao Wang and Qi Dou The Chinese University of Hong Kong
Abstract— Surgical robot automation has attracted increasing research interest over the past decade, expecting... |
2301.00457 | 2023-01 | ReSQueing Parallel and Private Stochastic Convex Optimization | # ReSQueing Parallel and Private Stochastic Convex Optimization
Yair Carmon∗ Arun Jambulapati† Yujia Jin‡ Yin Tat Lee§ Daogao Liu†
Aaron Sidford‡ Kevin Tian§
# Abstract
We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a fu... |
2301.00462 | 2023-01 | A Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data | # A Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data
Padmaksha Roy1[0000−0002−9571−1117], Himanshu Singhal1[0000−0002−0474−8126], Timothy J O’Shea1[0000−0003−2467−220X], and Ming Jin1[0000−0001−7909−4545]
Virginia Tech, VA, USA {padmaksha,himanshusinghal,oshea,jinming}@vt.edu
Abs... |
2301.00489 | 2023-01 | Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework | # Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework
Jiayun Zhang University of California, San Diego jiz069@ucsd.edu
Xiyuan Zhang University of California, San Diego xiyuanzh@ucsd.edu
Xinyang Zhang University of Illinois at Urbana-Champaign xz43@illino... |
2301.00493 | 2023-01 | Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting | # Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
Benjamin Wilson∗†,1, William $\mathbf{Q}\mathbf{i}^{*\dagger}$ , Tanmay Agarwal∗†, John Lambert†, Jagjeet Singh†, Siddhesh Khandelwal2, Bowen Pan†,3, Ratnesh Kumar†, Andrew Hartnett†, Jhony Kaesemodel Pontes†, Deva Ramanan†,4, Peter ... |
2301.00497 | 2023-01 | Efficient Online Learning with Memory via Frank-Wolfe Optimization: Algorithms with Bounded Dynamic Regret and Applications to Control | # Efficient Online Learning with Memory via Frank-Wolfe Optimization: Algorithms with Bounded Dynamic Regret and Applications to Control
Hongyu Zhou 1 Zirui $\mathbf{X}\mathbf{u}^{1}$ Vasileios Tzoumas 1
# Abstract
Projection operations are a typical computation bottleneck in online learning. In this paper, we... |
2301.00503 | 2023-01 | A Concept Knowledge Graph for User Next Intent Prediction at Alipay | # A Concept Knowledge Graph for User Next Intent Prediction at Alipay
Yacheng He
Ant Group
Hangzhou, China
heyachen.hyc@antgroup.com
Qianghuai Jia∗
Ant Group
Hangzhou, China
qianghuai.jqh@antgroup.com
Lin Yuan
Ant Group
Hangzhou, China
huiwai.yl@antgroup.com
Ruopeng Li
Ant Grou... |
2301.00508 | 2023-01 | EMOGATOR: A NEW OPEN SOURCE VOCAL BURST DATASETWITH BASELINE MACHINE LEARNING CLASSIFICATIONMETHODOLOGIES | # EMOGATOR: A NEW OPEN SOURCE VOCAL BURST DATASETWITH BASELINE MACHINE LEARNING CLASSIFICATIONMETHODOLOGIES
Fred W. Buhl University of Florida fredbuhl@ufl.edu
April 7, 2023
# ABSTRACT
Vocal Bursts – short, non-speech vocalizations that convey emotions, such as laughter, cries, sighs, moans, and groans – ar... |
2301.00512 | 2023-01 | On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects | # On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects
Sumana Basu,2, Marc-Andre Legault 2, Adriana Romero-Soriano'.2.3, Doina Precup'.2
1 McGill University, 2 Mila, 3 Meta AI umana.basu@mail.mcgill.ca, marc-andre.legault@mcgill.ca, adriana.romsor@g... |
2301.00516 | 2023-01 | Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications | # Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications
Zhe Ma, Wen Wu, Senior Member, IEEE, Feifei Gao, Fellow, IEEE, and Xuemin (Sherman) Shen, Fellow, IEEE
# Abstract
In this paper, we investigate the joint device activity and data detection in massive machine-type communications (... |
2301.00521 | 2023-01 | A Policy Optimization Method Towards Optimal-time Stability | # A Policy Optimization Method Towards Optimal-time Stability
Shengjie Wang1,2,3 Fengbo Lan1 Xiang Zheng4 Yuxue Cao1 Oluwatosin Oseni5 Haotian $\mathbf{X}\mathbf{u}^{1}$ Tao Zhang1,† Yang Gao1,2,3,†
1Tsinghua University 2Shanghai Artificial Intelligence Laboratory 3Shanghai Qi Zhi Institute City University of Hon... |
2301.00524 | 2023-01 | Learning Confident Classifiers in the Presence of Label Noise | # Learning Confident Classifiers in the Presence of Label Noise
Asma Ahmed Hashmi Institute of Informatics, LMU Munich Konrad Zuse School of Excellence in Reliable AI asmah17@gmail.com
Aigerim Zhumabayeva MBZUAI Abu Dhabi, UAE Aigerim.Zhumabayeva@mbzuai.ac.ae
Nikita Kotelevskii Skoltech, MBZUAI Russia, UAE Nik... |
2301.00537 | 2023-01 | Posterior Collapse and Latent Variable Non-identifiability | # Posterior Collapse and Latent Variable Non-identifiability
Yixin Wang University of Michigan yixinw@umich.edu
David M. Blei Columbia University david.blei@columbia.edu
John P. Cunningham Columbia University jpc2181@columbia.edu
# Abstract
Variational autoencoders model high-dimensional data by positing... |
2301.00545 | 2023-01 | Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels | # Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels
Yikai Wang, Yanwei Fu, and Xinwei Sun.
Abstract—A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this paper, we propose a novel theoretically guaranteed clean sample selection fra... |
2301.00557 | 2023-01 | Learning to Maximize Mutual Information for Dynamic Feature Selection | # Learning to Maximize Mutual Information for Dynamic Feature Selection
Ian Covert 1 Wei Qiu 1 Mingyu Lu 1 Nayoon $\mathbf{Kim}^{1}$ Nathan White 2 Su-In Lee 1
# Abstract
Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here,... |
2301.00561 | 2023-01 | Local Differential Privacy for Sequential Decision Making in a Changing Environment | # Local Differential Privacy for Sequential Decision Making in a Changing Environment
Pratik Gajane
Eindhoven University of Technology pratik.gajane $@$ gmail.com
# Abstract
We study the problem of preserving privacy while still providing high utility in sequential decision making scenarios in a changing en... |
2301.00582 | 2023-01 | Sparse neural networks with skip-connections for identification of aluminum electrolysis cell | # Sparse neural networks with skip-connections for identification of aluminum electrolysis cell
Erlend Torje Berg Lundby, Haakon Robinson, Adil Rasheed, Ivar Johan Halvorsen, Jan Tommy Gravdahl
Abstract— Neural networks are rapidly gaining interest in nonlinear system identification due to the model’s ability to ... |
2301.00595 | 2023-01 | Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets | # Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets
Ekaterina Antonenko $^{1,2}$ and Jesse Read1
1 LIX, École Polytechnique, Institut Polytechnique de Paris, France 2 Digitalent lab (Moteur Intelligence Artificielle), Paris, France {ekaterina.antonenko,jesse.read}@... |
2301.00596 | 2023-01 | A contrastive learning approach for individual re-identification in a wild fish population | # A contrastive learning approach for individual re-identification in a wild fish population
Ørjan Langøy Olsen $^{1}$ , Tonje Knutsen Sørdalen $^2$ , Morten Goodwin $^{1}$ , Ketil Malde $^{3}$ , Kristian Muri Knausgard $^{*4}$ , and Kim Tallaksen Halvorsen $\vdots$
$^{1}$ Centre for Artificial Intelligence Resea... |
2301.00620 | 2023-01 | Dynamically Modular and Sparse General Continual Learning | # Dynamically Modular and Sparse General Continual Learning
Arnav Varma1, Elahe Arani†1,2 and Bahram Zonooz $^{\dag1,2}$ 1Advanced Research Lab, NavInfo Europe, Eindhoven, The Netherlands $^2$ Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands arnav.varma@navinfo.eu, ... |
2301.00621 | 2023-01 | Data-Driven Optimization of Directed Information over Discrete Alphabets | # Data-Driven Optimization of Directed Information over Discrete Alphabets
Dor Tsur\* Ziv Aharoni\* Ziv Goldfeld, and Haim Permuter $^*$
# Abstract
Directed information (DI) is a fundamental measure for the study and analysis of sequential stochastic models. In particular, when optimized over input distributio... |
2301.00631 | 2023-01 | Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization | # Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization
Gersende Fort1\* and Eric Moulines2 1\*Institut de Mathematiques de Toulouse, CNRS& Universite de Toulouse 118 route de Narbonne, Toulouse, 31400, France. $^2$ CMAP, Ecole Polytechnique, Route de Saclay, Pala... |
2301.00636 | 2023-01 | New Designed Loss Functions to Solve Ordinary Differential Equations with Artificial Neural Network | # New Designed Loss Functions to Solve Ordinary Differential Equations with Artificial Neural Network
Xiao Xiong Imperial College London xx1119@ic.ac.uk
# Abstract
This paper investigates the use of artificial neural networks (ANNs) to solve differential equations (DEs) and the construction of the loss functio... |
2301.00637 | 2023-01 | Large-Scale Traffic Signal Control by a Nash Deep Q-network Approach | # Large-Scale Traffic Signal Control by a Nash Deep Q-network Approach
Yuli Zhang, Shangbo Wang, Ruiyuan Jiang
Abstract—Reinforcement Learning (RL) is currently one of the most commonly used techniques for traffic signal control (TSC), which can adaptively adjusted traffic signal phase and duration according to r... |
2301.00641 | 2023-01 | Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management | # Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management
Yuanzheng Li, Member IEEE, Shangyang He, Yang Li, Senior Member IEEE, Yang Shi, Fellow IEEE, and Zhigang Zeng, Fellow IEEE
Abstract—The utilization of large-scale distributed renewable en... |
2301.00656 | 2023-01 | TRINET: STABILIZING SELF-SUPERVISED LEARNING FROM COMPLETE OR SLOW COLLAPSE ON ASR | # TRINET: STABILIZING SELF-SUPERVISED LEARNING FROM COMPLETE OR SLOW COLLAPSE ON ASR
Lixin Cao 1† Jun Wang 1† Ben Yang 1,2‡ Dan Su1 Dong Yu3
1Tencent AI Lab, China 2 Peking University 3Tencent AI Lab, USA
# ABSTRACT
Self-supervised learning (SSL) models confront challenges of abrupt informational collapse o... |
2301.00665 | 2023-01 | Targeted Phishing Campaigns using Large Scale Language Models | # Targeted Phishing Campaigns using Large Scale Language Models
Rabimba Karanjai
Department of Computer Science
University Of Houston
Houston, United States
rkaranjai@uh.edu
Abstract—Natural language models (NLMs) such as GPT-3, GPT-2, and other large language models have achieved impressive results i... |
2301.00675 | 2023-01 | FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep Neural Networks | # FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep Neural Networks
Akul Malhotra Purdue University West Lafayette, Indiana malho ${\mathrm{t}}23\textcircled{a}$ purdue.edu
Sumeet Kumar Gupta Purdue University West Lafayette, Indiana guptask@purdue.edu
Abstract—Model compression via quantizati... |
2301.00676 | 2023-01 | Multimodal Sequential Generative Models for Semi-Supervised Language Instruction Following | # Multimodal Sequential Generative Models for Semi-Supervised Language Instruction Following
Kei Akuzawa1 , Yusuke Iwasawa1 , Yutaka Matsuo1 1The University of Tokyo, Japan {akuzawa-kei,iwasawa,matsuo}@weblab.t.u-tokyo.ac.jp
# Abstract
Agents that can follow language instructions are expected to be useful in a... |
2301.00691 | 2023-01 | Reinforcement Learning with Success Induced Task Prioritization | # Reinforcement Learning with Success Induced Task Prioritization
Maria Nesterova $\cdot^{1}$ , Alexey Skrynnik $^{1,2,3}$ , and Aleksandr Panov $^{2,3}$
$^{\mathrm{~1~}}{}$ Moscow Institute of Physics and Technology, Moscow, Russia 2 AIRI, Moscow, Russia $^{3}$ Federal Research Center “Computer Science and Contr... |
2301.00704 | 2023-01 | Muse: Text-To-Image Generation via Masked Generative Transformers | # Muse: Text-To-Image Generation via Masked Generative Transformers
Huiwen Chang \* Han Zhang\* Jarred Barber AJ Maschinot + Jose Lezama Lu Jiang Ming-Hsuan Yang Kevin Murphy William T. Freeman Michael Rubinstein † Yuanzhen Li † Dilip Krishnan †
Google Research
# Abstract
We present Muse, a text-to-image T... |
2301.00709 | 2023-01 | Tsetlin Machine Embedding: Representing Words Using Logical Expressions | # Tsetlin Machine Embedding: Representing Words Using Logical Expressions
Bimal Bhattarai , Ole-Christoffer Granmo , Lei Jiao , Rohan Yadav and Jivitesh Sharma Centre for AI Research (CAIR), University of Agder, Norway {bimal.bhattarai, ole.granmo, lei.jiao, rohan.yadav, jivitesh.sharma}@uia.no
# Abstract
Embe... |
2301.00712 | 2023-01 | On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis | # On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis
Lesi Chen∗ IIIS, Tsinghua University Shanghai Qizhi Institute
CHENLC23 $@$ MAILS.TSINGHUA.EDU.CN
Jing $\mathbf{X}\mathbf{u}^{*}$ IIIS, Tsinghua University
XUJING21 $@$ MAILS.TSINGHUA.EDU.CN
Jingzhao Zhang† ... |
2301.00716 | 2023-01 | IRT2: Inductive Linking and Ranking in Knowledge Graphs of Varying Scale | # IRT2: Inductive Linking and Ranking in Knowledge Graphs of Varying Scale
Felix Hamann} Adrian Ulges, Maurice Falk
Abstract: We address the challenge of building domain-specific knowledge models for industrial use cases, where labelled data and taxonomic information is initially scarce. Our focus is on inductive... |
2301.00717 | 2023-01 | Robust Consensus Clustering and its Applications for Advertising Forecasting | # Robust Consensus Clustering and its Applications for Advertising Forecasting
Deguang Kong\*, Miao Lu, Konstantin Shmakov and Jian Yang
Yahoo Research, San Jose, California, U.S.A, 94089 doogkong@gmail.com, ml4ey@virginia.edu, kshmakov $@$ yahooinc.com, jianyang@yahooinc.com
# Abstract
Consensus clustering... |
2301.00719 | 2023-01 | Detection of Groups with Biased Representation in Ranking | # Detection of Groups with Biased Representation in Ranking
Jinyang Li University of Michigan jinyli@umich.edu
Yuval Moskovitch Ben Gurion University of the Negev yuvalmos@bgu.ac.il
H. V. Jagadish University of Michigan jag@umich.edu
Abstract—Real-life tools for decision-making in many critical domains are ... |
2301.00723 | 2023-01 | TEMPORALLY LAYERED ARCHITECTURE FOR ADAPTIVE, DISTRIBUTED AND CONTINUOUS CONTROL | # TEMPORALLY LAYERED ARCHITECTURE FOR ADAPTIVE, DISTRIBUTED AND CONTINUOUS CONTROL
Devdhar Patel University of Massachusetts Amherst Amherst, MA 01003, USA devdharpatel@cs.umass.edu
Joshua Russell College of Computer and Information Sciences, University of Massachusetts Amherst Amherst, MA 01003, USA jgrussell@cs... |
2301.00736 | 2023-01 | Mixed moving average field guided learning for spatio-temporal data | # Mixed moving average field guided learning for spatio-temporal data
Imma Valentina Curato∗ , Orkun Furat $^\dagger$ , Lorenzo Proietti $^\ddag$ and Bennet Stroh \$
August 5, 2024
# Abstract
Influenced mixed moving average fields are a versatile modeling class for spatio-temporal data. However, their predi... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.