Hint Marginalization for Improved Reasoning in Large Language Models
Soumyasundar Pal
Didier Ch'etelat
Yingxue Zhang
Large Language Models (LLMs) have exhibited an impressive capability to perform reasoning tasks, especially if they are encouraged to genera… (see more)te a sequence of intermediate steps. Reasoning performance can be improved by suitably combining multiple LLM responses, generated either in parallel in a single query, or via sequential interactions with LLMs throughout the reasoning process. Existing strategies for combination, such as self-consistency and progressive-hint-prompting, make inefficient usage of the LLM responses. We present Hint Marginalization, a novel and principled algorithmic framework to enhance the reasoning capabilities of LLMs. Our approach can be viewed as an iterative sampling strategy for forming a Monte Carlo approximation of an underlying distribution of answers, with the goal of identifying the mode the most likely answer. Empirical evaluation on several benchmark datasets for arithmetic reasoning demonstrates the superiority of the proposed approach.
Constant step-size stochastic approximation with delayed updates
Silviu-Iulian Niculescu
Mathukumalli Vidyasagar
In this paper, we consider constant step-size stochastic approximation with delayed updates. For the non-delayed case, it is well known that… (see more) under appropriate conditions, the discrete-time iterates of stochastic approximation track the trajectory of a continuous-time ordinary differential equation (ODE). For the delayed case, we show in this paper that, under appropriate conditions, the discrete-time iterates track the trajectory of a delay-differential equation (DDE) rather than an ODE. Thus, delayed updates lead to a qualitative change in the behavior of constant step-size stochastic approximation. We present multiple examples to illustrate the qualitative affect of delay and show that increasing the delay is generally destabilizing but, for some systems, it can be stabilizing as well.
Correction: CEPC Technical Design Report: Accelerator
Waleed Abdallah
Tiago CarlosAdorno de Freitas
Konstantin Afanaciev
Shakeel Ahmad
Ijaz Ahmed
Xiaocong Ai
Abid Aleem
Wolfgang Altmannshofer
Fabio Alves
Weiming An
Rui An
Daniele Paolo Anderle
D. Anderle
Stefan Antusch
Yasuo Arai
Andrej Arbuzov
Abdesslam Arhrib
A. Arhrib
Mustafa Ashry
Sha Bai … (see 1080 more)
Yu Bai
Yang Bai
Vipul Bairathi
Csaba Balazs
Philip Bambade
Yong Ban
Triparno Bandyopadhyay
Shou-Shan Bao
Desmond P. Barber
Ays¸e Bat
Varvara Batozskaya
Subash Chandra Behera
Alexander Belyaev
Michele Bertucci
Xiao-Jun Bi
Yuanjie Bi
Tianjian Bian
T. Bian
Fabrizio Bianchi
Thomas Bieko¨tter
Michela Biglietti
Shalva Bilanishvili
Deng Binglin
Lingling Men
Denis Bodrov
Anton Bogomyagkov
Serge Bondarenko
Stewart Boogert
Maarten Boonekamp
Marcello Borri
M. Borri
Angelo Bosotti
Vincent Boudry
Mohammed Boukidi
Igor Boyko
Ivanka Bozovic
Giuseppe Bozzi
Jean-Claude Brient
J. Brient
Anastasiia Budzinskaya
Masroor Bukhari
Vladimir Bytev
Giacomo Cacciapaglia
Hua Cai
Wenyong Cai
Wujun Cai
Yijian Cai
Yizhou Cai
Yuchen Cai
Haiying Cai
Huacheng Cai
Lorenzo Calibbi
Junsong Cang
Guofu Cao
Jianshe Cao
Antoine Chance
Xuejun Chang
Yue Chang
Zhe Chang
Xinyuan Chang
Wei Chao
Auttakit Chatrabhuti
Yimin Che
Yuzhi Che
Bin Chen
Danping Chen
Fuqing Chen
Fusan Chen
Gang Chen
Guoming Chen
Hua-Xing Chen
Huirun Chen
Jinhui Chen
Ji-Yuan Chen
Kai Chen
Mali Chen
Mingjun Chen
Mingshui Chen
Ning Chen
Shanhong Chen
Shanzhen Chen
Shao-Long Chen
Shaomin Chen
Shiqiang Chen
Tianlu Chen
Wei Chen
Xiang Chen
Xiaoyu Chen
Xin Chen
Xun Chen
Xurong Chen
Ye Chen
Ying Chen
Yukai Chen
Zelin Chen
Zilin Chen
Boping Chen
Chunhui Chen
H. Cheng
Huajie Cheng
Hok Chuen Cheng
Shan Cheng
Tongguang Cheng
Yunlong Chi
Pietro Chimenti
Wen Han Chiu
Guk Cho
M. Chu
Ming-Chung Chu
X. Chu
Xiaotong Chu
Ziliang Chu
Guglielmo Coloretti
Andreas Crivellin
Hanhua Cui
Xiaohao Cui
Zhaoyuan Cui
B. D’Anzi
Brunella D’Anzi
Ling-Yun Dai
Xinchen Dai
Xuwen Dai
Antonio De Maria
Nicola De Filippis
Christophe De La Taille
Francesca De Mori
Chiara De Sio
Elisa Del Core
Shuangxue Deng
W. Deng
Wei-Tian Deng
Zhi Deng
Ziyan Deng
Bhupal Dev
Tang Dewen
Biagio Di Micco
Ran Ding
Siqin Ding
Yadong Ding
Haiyi Dong
Jianing Dong
Jing Dong
Lan Dong
Mingyi Dong
Xu Dong
Yipei Dong
Yubing Dong
Milos Dordevic
Marco Drewes
Mingxuan Du
Qianqian Du
Xiaokang Du
Yanyan Du
Yong Du
Yunfei Du
Chun-Gui Duan
Zhe Duan
Yahor Dydyshka
Ulrik Egede
Walaa Elmetenawee
Yun Eo
Ka Yan Fan
Kuanjun Fan
Yunyun Fan
Bo Fang
Shuangshi Fang
Yuquan Fang
Ada Farilla
Riccardo Farinelli
Muhammad Farooq
A. F. Golfe
Almaz Fazliakhmetov
Angeles Faus Golfe
Rujun Fei
Bo Feng
Chong Feng
Junhua Feng
Xu Feng
Zhuoran Feng
ZhuoranFeng
Luis Roberto Flores Castillo
Etienne Forest
Andrew Fowlie
H. Fox
Harald Fox
Hai-Bing Fu
Jinyu Fu
Benjamin Fuks
Yoshihiro Funakoshi
Emidio Gabrielli
Nan Gan
Li Gang
Jie Gao
Meisen Gao
Wenbin Gao
Wenchun Gao
Yu Gao
Yuanning Gao
Zhanxiang Gao
Yanyan Gao
Kun Ge
Shao-Feng Ge
Zhenwu Ge
Li-Sheng Geng
Qinglin Geng
Hao Zeng
Chao-Qiang Geng
Swagata Ghosh
Antonio Gioiosa
Leonid Gladilin
Ti Gong
Stefania Gori
Quanbu Gou
Sebastian Grinstein
Chenxi Gu
Gerardo Guillermo
Joao Guimaraes da Costa
Dizhou Guo
Fangyi Guo
Jiacheng Guo
Jun Guo
Lei Guo
Xia Guo
Xinyang Guo
Xin-Heng Guo
Yunqiang Guo
Yuping Guo
Yun Guo
Zhi-Hui Guo
Alejandro Gutie´rrez-Rodríguez
Seungkyu Ha
Noman Habib
Jan Hajer
Francois Hammer
Chengcheng Han
Huayong Han
Jifeng Han
Liangliang Han
Liang Han
Rao Zhang
Yang Han
Ruixiong Han
Yezi Han
Yuanying Han
Tao Han
Jiankui Hao
Xiqing Hao
Qiang Zhao
Chuanqi He
Dayong He
Dongbing He
Guangyuan He
Hong-Jian He
Jibo He
Jun He
Longyan He
Xiang He
Xiao-Gang He
Zhenqiang He
Klaus Heinemann
Sven Heinemeyer
Yuekun Heng
María A. Herna´ndez-Ruíz
Jiamin Hong
Y. Hor
YuenKeung Hor
George W. S. Hou
Xiantao Hou
Xiaonan Hou
Zhilong Hou
Suen Hou
Caishi Hu
Chen Hu
Dake Hu
Haiming Hu
Jiagen Hu
Jun Hu
Kun Hu
Shouyang Hu
Yongcai Hu
Yu Hu
Zhen Hu
Z. Hua
Zhehao Hua
Jianfei Hua
Chao-Shang Huang
Fa Peng Huang
Guangshun Huang
Jinshu Huang
Ke Huang
Liangsheng Huang
Shuhui Huang
Xingtao Huang
Xu-Guang Huang
Yanping Huang
Yonggang Huang
Yongsheng Huang
Zimiao Huang
Yuanyuan Wei
Chen Huanyuan
Changgi Huh
Jiaqi Hui
Lihua Huo
Talab Hussain
Kyuyeong Hwang
Ara Ioannisian
Munawar Iqbal
Paul Jackson
Shahriyar Jafarzade
Haeun Jang
Seoyun Jang
Daheng Ji
Q. Ji
Qingping Ji
Quan Ji
Xiaolu Ji
Jingguang Jia
Jinsheng Jia
X. Q. Jia
Xuewei Jia
Zihang Jia
Cailian Jiang
Han Ren Jiang
Houbing Jiang
Jun Jiang
Xiaowei Jiang
Xin Jiang
Xuhui Jiang
Yongcheng Jiang
Zhongjian Jiang
Cheng Jiang
Ruiqi Jiao
Dapeng Jin
Shan Jin
Song Jin
Yi Jin
Junji Jis
Sunghoon Jung
Goran Kacarevic
Eric Kajfasz
Lidia Kalinovskaya
Aleksei Kampf
Wen Kang
Xian-Wei Kang
Xiaolin Kang
Biswajit Karmakar
Zhiyong Ke
Rijeesh Keloth
Alamgir Khan
Hamzeh Khanpour
Khanchai Khosonthongkee
KhanchaiKhosonthongkee
Bobae Kim
Dongwoon Kim
Mi Ran Kim
Minsuk Kim
Sungwon Kim
On Kim
Michael Klasen
Sanghyun Ko
S. Ko
Ivan Koop
Vitaliy Kornienko
Bryan Kortman
Gennady Kozlov
Shiqing Kuang
Mukesh Kumar
Chia Ming Kuo
Tsz Hong Kwok
Franc¸ois Sylvain Ren Lagarde
F. Lagarde
Pei-Zhu Lai
Imad Laktineh
Xiaofei Lan
Zuxiu Lan
Lia Lavezzi
Justin Lee
Junghyun Lee
Sehwook Lee
Ge Lei
Roy Lemmon
Yongxiang Leng
Sze Ching Leung
Hai Tao Li
Bingzhi Li
Bo Li
Changhong Li
Chao Li
Cheng Li
Chunhua Li
Cui Li
Dazhang Li
Dikai Li
Yi Wang
Gang Li
Gaosong Li
Haibo Li
Haifeng Li
Hai-Jun Li
Haotian Li
Hengne Li
Honglei Li
Huijing Li
Jialin Li
Jingyi Li
J. Li
Jun Li
Leyi Li
Liang Li
Jinmian Li
Mei Li
Meng Li
Minxian Li
Ling Li
Pei-Rong Li
Qiang Li
Shaopeng Li
Shenghe Li
Shu Li
Shuo Li
Teng Li
Tiange Li
Tong Li
Weichang Li
Weidong Li
Wenjun Li
Xiaoling Li
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Xiaoping Li
Xiaoting Li
Xin Li
Xinqiang Li
Xuekang Li
Yang Li
Yanwei Li
Yiming Li
Ying Li
Ying-Ying Li
Yonggang Li
Yonglin Li
Yufeng Li
Yuhui Li
Zhan Li
Zhao Li
Zhiji Li
Lingfeng Li
Jing Liang
Jinhan Liang
Zhijun Liang
Guangrui Liao
Hean Liao
Jiaming Yan
Fei Li
Libo Liao
Longzhou Liao
Yipu Liao
Ayut Limphirat
AyutLimphirat
Jiajun Liao
Tao Lin
Weiping Lin
Yi Liao
Yufu Lin
Yugen Lin
Beijiang Liu
Bo Liu
Danning Liu
Dong Liu
Fu-Hu Liu
Hongbang Liu
Huangcheng Liu
Hui Liu
Huiling Liu
Jia Liu
Jiaming Liu
Jianbei Liu
Jianyi Liu
Jingdong Liu
Jinhua Liu
Kai Liu
Kang Liu
Kun Liu
Mengyao Liu
Pengcheng Liu
Qibin Liu
Shan Liu
Shidong Liu
Shuang Liu
Shubin Liu
Peng Liu
Tao Liu
Tong Liu
Wei Liu
Xiang Liu
Xiaohui Liu
Xiaoyu Liu
Xin Liu
Xinglin Liu
Xingquan Liu
Yang Liu
Xiao-Hai Liu
Yanlin Liu
Yao-Bei Liu
Yi Liu
Yiming Liu
Yong Liu
Yonglu Liu
Yu Liu
Yubin Liu
Yudong Liu
Yulong Liu
Zhaofeng Liu
Zhenchao Liu
Zhi Liu
Zhi-Feng Liu
Zhiqing Liu
Zhongfu Liu
Zuowei Liu
Mia Liu
Zhen Liu
Xiaoyang Liu
Xinchou Lou
Cai-Dian Lu
Jun-Xu Lu
Qiu Zhen Lu
Shang Lu
Wenxi Lu
Xiaohan Lu
Yunpeng Lu
Zhiyong Lu
Xianguo Lu
Wei Lu
Bayarto Lubsandorzhiev
Sultim Lubsandorzhiev
Arslan Lukanov
Jinliang Luo
Tao Luo
xiaoan Luo
Xiaofeng Luo
Xiaolan Luo
Jindong Lv
Feng Lyu
Xiao-Rui Lyu
Kun-Feng Lyu
Ande Ma
Hong-Hao Ma
Jun-Li Ma
Kai Ma
Lishuang Ma
Na Ma
Renjie Ma
Weihu Ma
Xinpeng Ma
Yanling Ma
Yan-Qing Ma
Yongsheng Ma
Zhonghui Ma
Zhongjian Ma
Yang Ma
Mousam Maity
Lining Mao
Yanmin Mao
Yaxian Mao
Aure´lien Martens
Caccia Massimo Luigi Maria
Shigeki Matsumoto
Bruce Mellado
Davide Meloni
Cai Meng
Lingxin Meng
Zhenghui Mi
Yuhui Miao
Mauro Migliorati
Lei Ming
Vasiliki A. Mitsou
Laura Monaco
Arthur Moraes
Karabo Mosala
Ahmad Moursy
Lichao Mu
Zhihui Mu
Nickolai Muchnoi
Daniel Muenstermann
Pankaj Munbodh
William John Murray
Jérôme Nanni
Dmitry Nanzanov
Changshan Nie
Sergei Nikitin
Feipeng Ning
Guozhu Ning
Jia-Shu Niu
Juan-Juan Niu
Yan Niu
Edward Khomotso Nkadimeng
Kazuhito Ohmi
Katsunobu Oide
Hideki Okawa
Mohamed Ouchemhou
Qun Ouyang
Daniele Paesani
Carlo Pagani
Stathes Paganis
Collette Pakuza
Jiangyang Pan
Juntong Pan
Tong Pan
Xiang Pan
Papia Panda
Saraswati Pandey
Mila Pandurovic
Rocco Paparella
Roman Pasechnik
Emilie Passemar
Hua Pei
Xiaohua Peng
Xinye Peng
Yuemei Peng
Jialun Ping
Ronggang Ping
Souvik Priyam Adhya
Baohua Qi
Hang Qi
Huirong Qi
Ming Qi
Sen Qian
Zhuoni Qian
Congfeng Qiao
Guangyou Qin
Jiajia Qin
Laishun Qin
Liqing Qin
Qin Qin
Xiaoshuai Qin
Zhonghua Qin
Guofeng Qu
Antonio Racioppi
Michael Ramsey-Musolf
Shabbar Raza
Vladimir Rekovic
Jing Ren
Ju¨rgen Reuter
Tania Robens
Giancarlo Rossi
Manqi Ruan
Leonid Rumyantsev
Min Sang Ryu
Renat Sadykov
Minjing Sang
Juan Jose´ Sanz-Cillero
Miroslav Saur
Nishil Savla
Michael A. Schmidt
Daniele Sertore
Ron Settles
Peng Sha
Ding-Yu Shao
Ligang Shao
Hua-Sheng Shao
Xin She
Chuang Shen
Hong-Fei Shen
Jian-Ming Shen
Peixun Shen
Qiuping Shen
Zhongtao Shen
Shuqi Sheng
Haoyu Shi
Hua Shi
Qi Shi
Shusu Shi
Xiaolei Shi
Xin Shi
Yukun Shi
Zhan Shi
Ian Shipsey
Gary Shiu
Chang Shu
Zong-Guo Si
Andrei Sidorenkov
Ivan Smiljanić
Aodong Song
Huayang Song
Jiaojiao Song
Jinxing Song
Siyuan Song
Weimin Song
Weizheng Song
Zhi Song
Shashwat Sourav
Paolo Spruzzola
Feng Su
Shengsen Su
Wei Su
Shufang Su
Yanfeng Sui
Zexuan Sui
Michael Sullivan
Baiyang Sun
Guoqiang Sun
Hao Sun
Hao-Kai Sun
Junfeng Sun
Liang Sun
Mengcheng Sun
Pengfei Sun
Sichun Sun
Xianjing Sun
Xiaohu Sun
Xilei Sun
Xingyang Sun
Xin-Yuan Sun
Yanjun Sun
Yongzhao Sun
Yue Sun
Zheng Sun
Narumon Suwonjandee
Elsayed Tag Eldin
Biao Tan
Bo Tang
Chuanxiang Tang
Gao Tang
Guangyi Tang
Jingyu Tang
Liang Tang
Ying’Ao Tang
Junquan Tao
Abdel Nasser Tawfik
Geoffrey Taylor
Valery Telnov
Saike Tian
Riccardo Torre
Wladyslaw Henryk Trzaska
Dmitri Tsybychev
Yanjun Tu
Shengquan Tuo
Michael Tytgat
Ghalib Ul Islam
Nikita Ushakov
German Valencia
Jaap Velthuis
Alessandro Vicini
Trevor Vickey
Ivana Vidakovic
Henri Videau
Raymond Volkas
Dmitry Voronin
Natasa Vukasinovic
Xia Wan
Xuying Wan
Xiao Wang
Anqing Wang
Bin Wang
Chengtao Wang
Chuanye Wang
Ci Wang
Dayong Wang
Dou Wang
En Wang
Guanwen Wang
Guo-Li Wang
Haijing Wang
Haolin Wang
Jianchun Wang
JianLi Wang
Jiawei Wang
Jin Wang
Jin-Wei Wang
Joseph Wang
Kechen Wang
Lechun Wang
Wei Wang
Liguo Wang
Lijiao Wang
Lu Wang
Meng Wang
Na Wang
Pengcheng Wang
Qian Wang
Qun Wang
Shu Lin Wang
Shudong Wang
Taofeng Wang
Tianhong Wang
Tianyang Wang
Xiaolong Wang
Xiaoning Wang
Xiao-Ping Wang
Xiongfei Wang
Xujian Wang
Yaping Wang
Yaqian Wang
Yiao Wang
Yifang Wang
Yilun Wang
Yiwei Wang
You-Kai Wang
Yuanping Wang
Yuexin Wang
Yuhao Wang
Yu-Ming Wang
Yuting Wang
Zhen Wang
Zhigang Wang
Weiping Wang
Zeren Simon Wang
Biao Wang
Hao Wang
Lian-Tao Wang
Zihui Wang
Zirui Wang
Jia Wang
Tong Wang
Daihui Wei
Shujun Wei
Wei Wei
Xiaomin Wei
Yingjie Wei
Liangjian Wen
Xuejun Wen
Yufeng Wen
Martin White
Peter Williams
Zef Wolffs
William John Womersley
Baona Wu
Bobing Wu
Guanjian Wu
Jinfei Wu
Lei Wu
Lina Wu
Linghui Wu
Minlin Wu
Peiwen Wu
Qi Wu
Qun Wu
Tianya Wu
Xiang Wu
Xiaohong Wu
Xing-Gang Wu
Xuehui Wu
Yaru Wu
Yongcheng Wu
Yuwen Wu
Zhi Wu
Xin Wu
Lei Xia
Ligang Xia
Shang Xia
Benhou Xiang
Dao Xiang
Zhiyu Xiang
Bo-Wen Xiao
Chu-Wen Xiao
Dong Xiao
Guangyan Xiao
Han Xiao
Meng Xiao
Ouzheng Xiao
Rui-Qing Xiao
Xiang Xiao
Yichen Xiao
Ying Xiao
Yu Xiao
Yunlong Xiao
Zhenjun Xiao
Hengyuan Xiao
Nian Xie
Yuehong Xie
Tianmu Xin
Ye Xing
Zhizhong Xing
Da Xu
Fang Xu
Fanrong Xu
Haisheng Xu
Haocheng Xu
Ji Xu
Miaofu Xu
Qingjin Xu
Qingnian Xu
Wei Xu
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Xinping Xu
Zhen Xu
Zijun Xu
Zehua Xu
Yaoyuan Xu
Feifei Xue
Baojun Yan
Bin Yan
Fen Yan
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Liang Yan
Qi-Shu Yan
Wenbiao Yan
Yupeng Yan
Luping Yan
Haoyue Yan
Dong Yang
Fengying Yang
Guicheng Yang
Haijun Yang
Jin Min Yang
Jing Yang
Lan Yang
Li Yang
Li Lin Yang
Lili Yang
Litao Yang
Mei Yang
Qiaoli Yang
Tiansen Yang
Xiaochen Yang
Yingjun Yang
Yueling Yang
Zhengyong Yang
Zhenwei Yang
Youhua Yang
Xiancong Yang
De-Liang Yao
Shi Yao
Lei Ye
Lingxi Ye
Mei Ye
Rui Ye
Yecheng Ye
Vitaly Yermolchyk
Kai Yi
Li Yi
Yang Yi
Di Yin
Peng-Fei Yin
Shenghua Yin
Ze Yin
Zhongbao Yin
Zhang Yinhong
Hwi Dong Yoo
Zhengyun You
Charles Young
Boxiang Yu
Chenghui Yu
Fusheng Yu
Jie-Sheng Yu
Jinqing Yu
Lingda Yu
Zhao-Huan Yu
Felix Yu
Bingrong Yu
Changzheng Yuan
Li Yuan
Xing-Bo Yuan
Youjin Yuan
Junhui Yue
Qian Yue
Baobiao Yue
Un Nisa Zaib
Riccardo Zanzottera
Ming Zeng
Jian Zhai
Jiyuan Zhai
Xin Zhe Zhai
Xi-Jie Zhan
Ben-Wei Zhang
Bolun Zhang
Di Zhang
Guangyi Zhang
Hao Zhang
Hong-Hao Zhang
Huaqiao Zhang
Hui Zhang
Jian Wang
Jianzhong Zhang
Jiehao Zhang
Jielei Zhang
Jingru Zhang
Jinxian Zhang
Junsong Zhang
Junxing Zhang
Lei Zhang
Liang Zhang
Licheng Zhang
Liming Zhang
Linhao Zhang
Mengchao Zhang
Shulei Zhang
Wan Zhang
Wenchao Zhang
Xiangzhen Zhang
Xiaomei Zhang
Xiaoming Zhang
Xiaoxu Zhang
Xiaoyu Zhang
Xuantong Zhang
Xueyao Zhang
Yang Zhang
Yanxi Zhang
Yao Zhang
Ying Zhang
Yixiang Zhang
Yizhou Zhang
Yongchao Zhang
Yu Zhang
Yuan Zhang
Yujie Zhang
Yulei Zhang
Yumei Zhang
Yunlong Zhang
Zhandong Zhang
Zhaoru Zhang
Zhen-Hua Zhang
Zhenyu Zhang
Zhichao Zhang
Zhi-Qing Zhang
Zhuo Zhang
Zhiqing Zhang
Cong Zhang
Tianliang Zhang
Luyan Zhang
Guang Zhao
Hongyun Zhao
Jie Zhao
Jingxia Zhao
Jingyi Zhao
Ling Zhao
Luyang Zhao
Mei Zhao
Minggang Zhao
Mingrui Zhao
Ruiguang Zhao
Tongxian Zhao
Yaliang Zhao
Ying Zhao
Yue Zhao
Zhiyu Zhao
Zhuo Zhao
Alexey Zhemchugov
Hongjuan Zheng
Jinchao Zheng
Liang Zheng
Ran Zheng
shanxi zheng
Xu-Chang Zheng
Wang Zhile
Weicai Zhong
Yi-Ming Zhong
Chen Zhou
Daicui Zhou
Jianxin Zhou
Jing Zhou
Ning Zhou
Qi-Dong Zhou
Shiyu Zhou
Shun Zhou
Sihong Zhou
Xiang Zhou
Xingyu Zhou
Yang Zhou
Yong Zhou
Yu-Feng Zhou
Zusheng Zhou
Demin Zhou
Dechong Zhu
Hongbo Zhu
Huaxing Zhu
Jingya Zhu
Kai Zhu
Pengxuan Zhu
Ruilin Zhu
Xianglei Zhu
Yingshun Zhu
Yongfeng Zhu
Xiao Zhuang
Xuai Zhuang
Mikhail Zobov
Zhanguo Zong
Cong Zou
Hongying Zou
Extrapolatable Transformer Pre-training for Ultra Long Time-Series Forecasting
Ziyang Song
Qincheng Lu
Hao Xu
Mike He Zhu
Leveraging Data Characteristics for Bug Localization in Deep Learning Programs
Ruchira Manke
Mohammad Wardat
Hridesh Rajan
Deep Learning (DL) is a class of machine learning algorithms that are used in a wide variety of applications. Like any software system, DL p… (see more)rograms can have bugs. To support bug localization in DL programs, several tools have been proposed in the past. As most of the bugs that occur due to improper model structure known as structural bugs lead to inadequate performance during training, it is challenging for developers to identify the root cause and address these bugs. To support bug detection and localization in DL programs, in this paper, we propose Theia, which detects and localizes structural bugs in DL programs. Unlike the previous works, Theia considers the training dataset characteristics to automatically detect bugs in DL programs developed using two deep learning libraries, Keras and PyTorch . Since training the DL models is a time-consuming process, Theia detects these bugs at the beginning of the training process and alerts the developer with informative messages containing the bug's location and actionable fixes which will help them to improve the structure of the model. We evaluated Theia on a benchmark of 40 real-world buggy DL programs obtained from Stack Overflow . Our results show that Theia successfully localizes 57/75 structural bugs in 40 buggy programs, whereas NeuraLint, a state-of-the-art approach capable of localizing structural bugs before training localizes 17/75 bugs.
scMoE: single-cell mixture of experts for learning hierarchical, cell-type-specific, and interpretable representations from heterogeneous scRNA-seq data
Michael Huang
A vector almost-supermartingale convergence theorem and its applications
Silviu-Iulian Niculescu
Mathukumalli Vidyasagar
The almost-supermartingale convergence theorem of Robbins and Siegmund (1971) is a fundamental tool for establishing the convergence of vari… (see more)ous stochastic iterative algorithms including system identification, adaptive control, and reinforcement learning. The theorem is stated for non-negative scalar valued stochastic processes. In this paper, we generalize the theorem to non-negative vector valued stochastic processes and provide two set of sufficient conditions for such processes to converge almost surely. We present several applications of vector almost-supermartingale convergence theorem, including convergence of autoregressive supermartingales, delayed supermartingales, and stochastic approximation with delayed updates.
Bounded optimality of time investments in rats, mice, and humans
Torben Ott
Marion Bosc
Joshua I. Sanders
Adam Kepecs
Continuously Learning Bug Locations
Paulina Stevia Nouwou Mindom
Léuson M. P. Da Silva
Amin Nikanjam
Automatically locating buggy changesets associated with bug reports is crucial in the software development process. Deep Learning (DL)-based… (see more) techniques show promising results by leveraging structural information from the code and learning links between changesets and bug reports. However, since source code associated with changesets evolves, the performance of such models tends to degrade over time due to concept drift. Aiming to address this challenge, in this paper, we evaluate the potential of using Continual Learning (CL) techniques in multiple sub-tasks setting for bug localization (each of which operates on either stationary or non-stationary data), comparing it against a bug localization technique that leverages the BERT model, a deep reinforcement learning-based technique that leverages the A2C algorithm, and a DL-based function-level interaction model for semantic bug localization. Additionally, we enhanced the CL techniques by using logistic regression to identify and integrate the most significant bug-inducing factors. Our empirical evaluation across seven widely used software projects shows that CL techniques perform better than DL-based techniques by up to 61% in terms of Mean Reciprocal Rank (MRR), 44% in terms of Mean Average Precision (MAP), 83% in terms of top@1, 56% in terms of top@5, and 66% in terms of top@10 metrics in non-stationary setting. Further, we show that the CL techniques we studied are effective at localizing changesets relevant to a bug report while being able to mitigate catastrophic forgetting across the studied tasks and require up to 5x less computational effort during training. Our findings demonstrate the potential of adopting CL for bug localization in non-stationary settings, and we hope it helps to improve bug localization activities in Software Engineering using CL techniques.
Continuously Learning Bug Locations
Paulina Stevia Nouwou Mindom
Leuson Da Silva
Amin Nikanjam
Automatically locating buggy changesets associated with bug reports is crucial in the software development process. Deep Learning (DL)-based… (see more) techniques show promising results by leveraging structural information from the code and learning links between changesets and bug reports. However, since source code associated with changesets evolves, the performance of such models tends to degrade over time due to concept drift. Aiming to address this challenge, in this paper, we evaluate the potential of using Continual Learning (CL) techniques in multiple sub-tasks setting for bug localization (each of which operates on either stationary or non-stationary data), comparing it against a bug localization technique that leverages the BERT model, a deep reinforcement learning-based technique that leverages the A2C algorithm, and a DL-based function-level interaction model for semantic bug localization. Additionally, we enhanced the CL techniques by using logistic regression to identify and integrate the most significant bug-inducing factors. Our empirical evaluation across seven widely used software projects shows that CL techniques perform better than DL-based techniques by up to 61% in terms of Mean Reciprocal Rank (MRR), 44% in terms of Mean Average Precision (MAP), 83% in terms of top@1, 56% in terms of top@5, and 66% in terms of top@10 metrics in non-stationary setting. Further, we show that the CL techniques we studied are effective at localizing changesets relevant to a bug report while being able to mitigate catastrophic forgetting across the studied tasks and require up to 5x less computational effort during training. Our findings demonstrate the potential of adopting CL for bug localization in non-stationary settings, and we hope it helps to improve bug localization activities in Software Engineering using CL techniques.
Continuously Learning Bug Locations
Paulina Stevia Nouwou Mindom
Léuson M. P. Da Silva
Amin Nikanjam
Automatically locating buggy changesets associated with bug reports is crucial in the software development process. Deep Learning (DL)-based… (see more) techniques show promising results by leveraging structural information from the code and learning links between changesets and bug reports. However, since source code associated with changesets evolves, the performance of such models tends to degrade over time due to concept drift. Aiming to address this challenge, in this paper, we evaluate the potential of using Continual Learning (CL) techniques in multiple sub-tasks setting for bug localization (each of which operates on either stationary or non-stationary data), comparing it against a bug localization technique that leverages the BERT model, a deep reinforcement learning-based technique that leverages the A2C algorithm, and a DL-based function-level interaction model for semantic bug localization. Additionally, we enhanced the CL techniques by using logistic regression to identify and integrate the most significant bug-inducing factors. Our empirical evaluation across seven widely used software projects shows that CL techniques perform better than DL-based techniques by up to 61% in terms of Mean Reciprocal Rank (MRR), 44% in terms of Mean Average Precision (MAP), 83% in terms of top@1, 56% in terms of top@5, and 66% in terms of top@10 metrics in non-stationary setting. Further, we show that the CL techniques we studied are effective at localizing changesets relevant to a bug report while being able to mitigate catastrophic forgetting across the studied tasks and require up to 5x less computational effort during training. Our findings demonstrate the potential of adopting CL for bug localization in non-stationary settings, and we hope it helps to improve bug localization activities in Software Engineering using CL techniques.
LitLLMs, LLMs for Literature Review: Are we there yet?
Shubham Agarwal
Gaurav Sahu
Abhay Puri
Issam Hadj Laradji
Krishnamurthy Dj Dvijotham
Jason Stanley