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Timezone: America/Chicago

Competition: Weather4cast - Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts Thu 8 Dec 05:00 a.m.  

Aleksandra Gruca · Pedro Herruzo · Pilar Rípodas · Xavier Calbet · Llorenç Lliso Valverde · Federico Serva · Bertrand Le Saux · Michael Kopp · David Kreil · Sepp Hochreiter

The Weather4cast NeurIPS Competition has high practical impact for society: Unusual weather is increasing all over the world, reflecting ongoing climate change, and affecting communities in agriculture, transport, public health and safety, etc.Can you predict future rain patterns with modern machine learning algorithms? Apply spatio-temporal modelling to complex dynamic systems. Get access to unique large-scale data and demonstrate temporal and spatial transfer learning under strong distributional shifts.We provide a super-resolution challenge of high relevance to local events: Predict future weather as measured by ground-based hi-res rain radar weather stations.In addition to movies comprising rain radar maps you get large-scale multi-band satellite sensor images for exploiting data fusion.Winning models will advance key areas of methods research in machine learning, of relevance beyond the application domain.


Social: How to negotiate industry offers Thu 8 Dec 11:00 a.m.  

Nicole Bannon

Join the team at Rora and 81cents, to get the tools, information, and data you need to negotiate your next offer in AI more confidently. 

Some of the topics we'll cover in a 1.5 hr. period (with 1/2 an hour for Q&A) are:

- Understanding the fundamentals of compensation in tech (particularly around equity, bonus structures, etc.)
- How to get over your fears of negotiating
- How to decide which company / offer is right for you 
- How to negotiate without counter offers and without knowing ""market value""
- How to respond to pushback from recruiters and other guilt tripping / lowballing /pressure tactics
- How to avoid having an offer rescinded
- How to negotiate deadline of an offer
- Walking through a timeline of the negotiation process for a new offer


Spotlight: Featured Papers Panels 5A Thu 8 Dec 11:00 a.m.  

Each panel session is split into four 30-minute blocks composed of a set of lightning talks and a deep dive session related to similar topics. The deep dive will begin immediately after lightning talks and the related Q&A (it might be before the 15 min are over). We will not take any questions via microphone but ask you to use slido (see embedding below or go to https://slido.com and use keyword #neurips22). If you are a presenter or moderator, you should see a zoom link that you can use to join the session for Q&A.

Finally some important don'ts: DO NOT share any zoom or slido information publicly. DO NOT join zoom if you are not presenting or moderating.

Lightning Talk
Yao Mu · Jin Zhang · Haoyi Niu · Rui Yang · Mingdong Wu · Ze Gong · shubham sharma · Chenjia Bai · Yu ("Tony") Zhang · Siyuan Li · Yuzheng Zhuang · Fangwei Zhong · Yiwen Qiu · Xiaoteng Ma · Fei Ni · Yulong Xia · Chongjie Zhang · Hao Dong · Ming Li · Zhaoran Wang · Bin Wang · Chongjie Zhang · Jianyu Chen · Guyue Zhou · Lei Han · Jianming HU · Jianye Hao · Xianyuan Zhan · Ping Luo
Lightning Talk
Qiang LI · Zhiwei Xu · Jiaqi Yang · Thai Hung Le · Haoxuan Qu · Yang Li · Artyom Sorokin · Peirong Zhang · Mira Finkelstein · Nitsan levy · Chung-Yiu Yau · dapeng li · Thommen Karimpanal George · De-Chuan Zhan · Nazar Buzun · Jiajia Jiang · Li Xu · Yichuan Mo · Yujun Cai · Yuliang Liu · Leonid Pugachev · Bin Zhang · Lucy Liu · Hoi-To Wai · Liangliang Shi · Majid Abdolshah · Yoav Kolumbus · Lin Geng Foo · Junchi Yan · Mikhail Burtsev · Lianwen Jin · Yuan Zhan · Dung Nguyen · David Parkes · Yunpeng Baiia · Jun Liu · Kien Do · Guoliang Fan · Jeffrey S Rosenschein · Sunil Gupta · Sarah Keren · Svetha Venkatesh
Lightning Talk
Minting Pan · Xiang Chen · Wenhan Huang · Can Chang · Zhecheng Yuan · Jianzhun Shao · Yushi Cao · Peihao Chen · Ke Xue · Zhengrong Xue · Zhiqiang Lou · Xiangming Zhu · Lei Li · Zhiming Li · Kai Li · Jiacheng Xu · Dongyu Ji · Ni Mu · Kun Shao · Tianpei Yang · Kunyang Lin · Ningyu Zhang · Yunbo Wang · Lei Yuan · Bo Yuan · Hongchang Zhang · Jiajun Wu · Tianze Zhou · Xueqian Wang · Ling Pan · Yuhang Jiang · Xiaokang Yang · Xiaozhuan Liang · Hao Zhang · Weiwen Hu · Miqing Li · YAN ZHENG · Matthew Taylor · Huazhe Xu · Shumin Deng · Chao Qian · YI WU · Shuncheng He · Wenbing Huang · Chuanqi Tan · Zongzhang Zhang · Yang Gao · Jun Luo · Yi Li · Xiangyang Ji · Thomas Li · Mingkui Tan · Fei Huang · Yang Yu · Huazhe Xu · Dongge Wang · Jianye Hao · Chuang Gan · Yang Liu · Luo Si · Hangyu Mao · Huajun Chen · Jianye Hao · Jun Wang · Xiaotie Deng
Lightning Talk
Yangrui Chen · Zhiyang Chen · Liang Zhang · Hanqing Wang · Jiaqi Han · Shuchen Wu · shaohui peng · Ganqu Cui · Yoav Kolumbus · Noemi Elteto · Xing Hu · Anwen Hu · Wei Liang · Cong Xie · Lifan Yuan · Noam Nisan · Wenbing Huang · Yousong Zhu · Ishita Dasgupta · Luc V Gool · Tingyang Xu · Rui Zhang · Qin Jin · Zhaowen Li · Meng Ma · Bingxiang He · Yangyi Chen · Juncheng Gu · Wenguan Wang · Ke Tang · Yu Rong · Eric Schulz · Fan Yang · Wei Li · Zhiyuan Liu · Jiaming Guo · Yanghua Peng · Haibin Lin · Haixin Wang · Qi Yi · Maosong Sun · Ruizhi Chen · Chuan Wu · Chaoyang Zhao · Yibo Zhu · Liwei Wu · xishan zhang · Zidong Du · Rui Zhao · Jinqiao Wang · Ling Li · Qi Guo · Ming Tang · Yunji Chen

Spotlight: Featured Papers Panels 5B Thu 8 Dec 11:00 a.m.  

Each panel session is split into four 30-minute blocks composed of a set of lightning talks and a deep dive session related to similar topics. The deep dive will begin immediately after lightning talks and the related Q&A (it might be before the 15 min are over). We will not take any questions via microphone but ask you to use slido (see embedding below or go to https://slido.com and use keyword #neurips22). If you are a presenter or moderator, you should see a zoom link that you can use to join the session for Q&A.

Finally some important don'ts: DO NOT share any zoom or slido information publicly. DO NOT join zoom if you are not presenting or moderating.

Lightning Talk
Devansh Arpit · Xiaojun Xu · Zifan Shi · Ivan Skorokhodov · Shayan Shekarforoush · Zhan Tong · Yiqun Wang · Shichong Peng · Linyi Li · Ivan Skorokhodov · Huan Wang · Yibing Song · David Lindell · Yinghao Xu · Seyed Alireza Moazenipourasil · Sergey Tulyakov · Peter Wonka · Yiqun Wang · Ke Li · David Fleet · Yujun Shen · Yingbo Zhou · Bo Li · Jue Wang · Peter Wonka · Marcus Brubaker · Caiming Xiong · Limin Wang · Deli Zhao · Qifeng Chen · Dit-Yan Yeung
Lightning Talk
Conglong Li · Mohammad Azizmalayeri · Mojan Javaheripi · Pratik Vaishnavi · Jon Hasselgren · Hao Lu · Kevin Eykholt · Arshia Soltani Moakhar · Wenze Liu · Gustavo de Rosa · Nikolai Hofmann · Minjia Zhang · Zixuan Ye · Jacob Munkberg · Amir Rahmati · Arman Zarei · Subhabrata Mukherjee · Yuxiong He · Shital Shah · Reihaneh Zohrabi · Hongtao Fu · Tomasz Religa · Yuliang Liu · Mohammad Manzuri · Mohammad Hossein Rohban · Zhiguo Cao · Caio Cesar Teodoro Mendes · Sebastien Bubeck · Farinaz Koushanfar · Debadeepta Dey
Lightning Talk
Yanze Wu · Jie Xiao · Nianzu Yang · Jieyi Bi · Jian Yao · Yiting Chen · Qizhou Wang · Yangru Huang · Yongqiang Chen · Peixi Peng · Yuxin Hong · Xintao Wang · Feng Liu · Yining Ma · Qibing Ren · Xueyang Fu · Yonggang Zhang · Kaipeng Zeng · Jiahai Wang · GEN LI · Yonggang Zhang · Qitian Wu · Yifan Zhao · Chiyu Wang · Junchi Yan · Feng Wu · Yatao Bian · Xiaosong Jia · Ying Shan · Zhiguang Cao · Zheng-Jun Zha · Guangyao Chen · Tianjun Xiao · Han Yang · Jing Zhang · Jinbiao Chen · MA Kaili · Yonghong Tian · Junchi Yan · Chen Gong · Tong He · Binghui Xie · Yuan Sun · Francesco Locatello · Tongliang Liu · Yeow Meng Chee · David P Wipf · Tongliang Liu · Bo Han · Bo Han · Yanwei Fu · James Cheng · Zheng Zhang
Lightning Talk
Yuezhi Yang · Zeyu Yang · Yong Lin · Yi.shi Xu · Linan Yue · Tao Yang · Weixin Chen · Qi Liu · Jiaqi Chen · Dongsheng Wang · Baoyuan Wu · Yuwang Wang · Hao Pan · Shengyu Zhu · Zhenwei Miao · Yan Lu · Lu Tan · Bo Chen · Yichao Du · Haoqian Wang · Wei Li · Yanqing An · Ruiying Lu · Peng Cui · Nanning Zheng · Li Wang · Zhibin Duan · Xiatian Zhu · Mingyuan Zhou · Enhong Chen · Li Zhang

Competition: VisDA 2022 Challenge: Sim2Real Domain Adaptation for Industrial Recycling Thu 8 Dec 03:00 p.m.  

Dina Bashkirova · Samarth Mishra · Piotr Teterwak · Donghyun Kim · Rachel Lai · Fadi Alladkani · James Akl · Vitaly Ablavsky · Sarah Bargal · Berk Calli · Kate Saenko

Efficient post-consumer waste recycling is one of the key challenges of modern society, as countries struggle to find sustainable solutions to rapidly rising waste levels and avoid increased soil and sea pollution. The US is one of the leading countries in waste generation by volume but recycles less than 35% of its recyclable waste. Recyclable waste is sorted according to material type (paper, plastic, etc.) in material recovery facilities (MRFs) which still heavily rely on manual sorting. Computer vision solutions are an essential component in automating waste sorting and ultimately solving the pollution problem.In this sixth iteration of the VisDA challenge, we introduce a simulation-to-real (Sim2Real) semantic image segmentation competition for industrial waste sorting. We aim to answer the question: can synthetic data augmentation improve performance on this task and help adapt to changing data distributions? Label-efficient and reliable semantic segmentation is essential for this setting, but differs significantly from existing semantic segmentation datasets: waste objects are typically severely deformed and randomly located, which limits the efficacy of both shape and context priors, and have long tailed distributions and high clutter. Synthetic data augmentation can benefit such applications due to the difficulty in obtaining labels and rare categories. However, new solutions are needed to overcome the large domain gap between simulated and real images. Natural domain shift due to factors such as MRF location, season, machinery in use, etc., also needs to be handled in this application.Competitors will have access to two sources of training data: a novel procedurally generated synthetic waste sorting dataset, SynthWaste, as well as fully-annotated waste sorting data collected from a real material recovery facility. The target test set will be real data from a different MRF.


Competition: The Third Neural MMO Challenge: Learning to Specialize in Massively Multiagent Open Worlds Thu 8 Dec 03:00 p.m.  

Joseph Suarez · Hanmo Chen · Arbin Chen · Bo Wu · Xiaolong Zhu · enhong liu · JUN HU · Chenghui Yu · Phillip Isola

Neural MMO is an open-source environment for agent-based intelligence research featuring large maps with large populations, long time horizons, and open-ended multi-task objectives. We propose a benchmark on this platform wherein participants train and submit agents to accomplish loosely specified goals -- both as individuals and as part of a team. The submitted agents are evaluated against thousands of other such user submitted agents. Participants get started with a publicly available code base for Neural MMO, scripted and learned baseline models, and training/evaluation/visualization packages. Our objective is to foster the design and implementation of algorithms and methods for adapting modern agent-based learning methods (particularly reinforcement learning) to a more general setting not limited to few agents, narrowly defined tasks, or short time horizons. Neural MMO provides a convenient setting for exploring these ideas without the computational inefficiency typically associated with larger environments.


Competition: OGB-LSC 2022: A Large-Scale Challenge for ML on Graphs Thu 8 Dec 03:00 p.m.  

Weihua Hu · Matthias Fey · Hongyu Ren · Maho Nakata · Yuxiao Dong · Jure Leskovec

Enabling effective and efficient machine learning (ML) over large-scale graph data (e.g., graphs with billions of edges) can have a huge impact on both industrial and scientific applications. At KDD Cup 2021, we organized the OGB Large-Scale Challenge (OGB-LSC), where we provided large and realistic graph ML tasks. Our KDD Cup attracted huge attention from graph ML community (more than 500 team registrations across the globe), facilitating innovative methods being developed to yield significant performance breakthrough. However, the problem of machine learning over large graphs is not solved yet and it is important for the community to engage in a focused multi-year effort in this area (like ImageNet and MS-COCO). Here we propose an annual ML challenge around large-scale graph datasets, which will drive forward method development and allow for tracking progress. We propose the 2nd OGB-LSC (referred to as OGB-LSC 2022) around the OGB-LSC datasets. Our proposed challenge consists of three tracks, covering core graph ML tasks of node-level prediction (academic paper classification with 240 million nodes), link-level prediction (knowledge graph completion with 90 million entities), and graph-level prediction (molecular property prediction with 4 million graphs). Importantly, we have updated two out of the three datasets based on the lessons learned from our KDD Cup, so that the resulting datasets are more challenging and realistic. Our datasets are extensively validated through our baseline analyses and last year’s KDD Cup. We also provide the baseline code as well as Python package to easily load the datasets and evaluate the model performance.


Competition: Open Catalyst Challenge Thu 8 Dec 03:00 p.m.  

Abhishek Das · Muhammed Shuaibi · Aini Palizhati · Siddharth Goyal · Adeesh Kolluru · Janice Lan · Ammar Rizvi · Nima Shoghi · Anuroop Sriram · Brook Wander · Brandon Wood · Zachary Ulissi · Larry Zitnick

Advancements to renewable energy processes are needed urgently to address climate change and energy scarcity around the world. Many of these processes, including the generation of electricity through fuel cells or fuel generation from renewable resources are driven through chemical reactions. The use of catalysts in these chemical reactions plays a key role in developing cost-effective solutions by enabling new reactions and improving their efficiency. Unfortunately, the discovery of new catalyst materials is limited due to the high cost of computational atomic simulations and experimental studies. Machine learning has the potential to significantly reduce the cost of computational simulations by orders of magnitude. By filtering potential catalyst materials based on these simulations, candidates of higher promise may be selected for experimental testing and the rate at which new catalysts are discovered could be greatly accelerated.The 2nd edition of the Open Catalyst Challenge invites participants to submit results of machine learning models that simulate the interaction of a molecule on a catalyst's surface. Specifically, the task is to predict the energy of an adsorbate-catalyst system in its relaxed state starting from an arbitrary initial state. From these values, the catalyst's impact on the overall rate of a chemical reaction may be estimated; a key factor in filtering potential catalysis materials. Competition participants are provided training and validation datasets containing over 6 million data samples from a wide variety of catalyst materials, and a new testing dataset specific to the competition. Results will be evaluated and winners determined by comparing against the computationally expensive approach of Density Functional Theory to verify the relaxed energies predicted. Baseline models and helper code are available on Github: https://github.com/open-catalyst-project/ocp.


Competition: Habitat Rearrangement Challenge Thu 8 Dec 03:00 p.m.  

Andrew Szot · Karmesh Yadav · Alexander Clegg · Vincent-Pierre Berges · Aaron Gokaslan · Angel Chang · Manolis Savva · Zsolt Kira · Dhruv Batra

We propose the Habitat Rearrangement Challenge. Specifically, a virtual robot (Fetch mobile manipulator) is spawned in a previously unseen simulation environment and asked to rearrange objects from initial to desired positions -- picking/placing objects from receptacles (counter, sink, sofa, table), opening/closing containers (drawers, fridges) as necessary. The robot operates entirely from onboard sensing -- head- and arm-mounted RGB-D cameras, proprioceptive joint-position sensors (for the arm), and egomotion sensors (for the mobile base) -- and may not access any privileged state information (no prebuilt maps, no 3D models of rooms or objects, no physically-implausible sensors providing knowledge of mass, friction, articulation of containers). This is a challenging embodied AI task involving embodied perception, mobile manipulation, sequential decision making in long-horizon tasks, and (potentially) deep reinforcement and imitation learning. Developing such embodied intelligent systems is a goal of deep scientific and societal value, including practical applications in home assistant robots.


Competition: The Trojan Detection Challenge Thu 8 Dec 03:00 p.m.  

Mantas Mazeika · Dan Hendrycks · Huichen Li · Xiaojun Xu · Andy Zou · Sidney Hough · Arezoo Rajabi · Dawn Song · Radha Poovendran · Bo Li · David Forsyth

A growing concern for the security of ML systems is the possibility for Trojan attacks on neural networks. There is now considerable literature for methods detecting these attacks. We propose the Trojan Detection Challenge to further the community's understanding of methods to construct and detect Trojans. This competition will consist of complimentary tracks on detecting/analyzing Trojans and creating evasive Trojans. Participants will be tasked with devising methods to better detect Trojans using a new dataset containing over 6,000 neural networks. Code and evaluations from three established baseline detectors will provide a starting point, and a novel Minimal Trojan attack will challenge participants to push the state-of-the-art in Trojan detection. At the end of the day, we hope our competition spurs practical innovations and clarifies deep questions surrounding the offense-defense balance of Trojan attacks.


Spotlight: Featured Papers Panels 6A Thu 8 Dec 07:00 p.m.  

Each panel session is split into four 30-minute blocks composed of a set of lightning talks and a deep dive session related to similar topics. The deep dive will begin immediately after lightning talks and the related Q&A (it might be before the 15 min are over). We will not take any questions via microphone but ask you to use slido (see embedding below or go to https://slido.com and use keyword #neurips22). If you are a presenter or moderator, you should see a zoom link that you can use to join the session for Q&A.

Finally some important don'ts: DO NOT share any zoom or slido information publicly. DO NOT join zoom if you are not presenting or moderating.

Lightning Talk
Ziyi Wang · Nian Liu · Yaming Yang · Qilong Wang · Yuanxin Liu · Zongxin Yang · Yizhao Gao · Yanchen Deng · Dongze Lian · Nanyi Fei · Ziyu Guan · Xiao Wang · Shufeng Kong · Xumin Yu · Daquan Zhou · Yi Yang · Fandong Meng · Mingze Gao · Caihua Liu · Yongming Rao · Zheng Lin · Haoyu Lu · Zhe Wang · Jiashi Feng · Zhaolin Zhang · Deyu Bo · Xinchao Wang · Chuan Shi · Jiangnan Li · Jiangtao Xie · Jie Zhou · Zhiwu Lu · Wei Zhao · Bo An · Jiwen Lu · Peihua Li · Jian Pei · Hao Jiang · Cai Xu · Peng Fu · Qinghua Hu · Yijie Li · Weigang Lu · Yanan Cao · Jianbin Huang · Weiping Wang · Zhao Cao · Jie Zhou
Lightning Talk
Yichuan Mo · Botao Yu · Gang Li · Zezhong Xu · Haoran Wei · Arsene Fansi Tchango · Raef Bassily · Haoyu Lu · Qi Zhang · Songming Liu · Mingyu Ding · Peiling Lu · Yifei Wang · Xiang Li · Dongxian Wu · Ping Guo · Wen Zhang · Hao Zhongkai · Mehryar Mohri · Rishab Goel · Yisen Wang · Yifei Wang · Yangguang Zhu · Zhi Wen · Ananda Theertha Suresh · Chengyang Ying · Yujie Wang · Peng Ye · Rui Wang · Nanyi Fei · Hui Chen · Yiwen Guo · Wei Hu · Chenglong Liu · Julien Martel · Yuqi Huo · Wu Yichao · Hang Su · Yisen Wang · Peng Wang · Huajun Chen · Xu Tan · Jun Zhu · Ding Liang · Zhiwu Lu · Joumana Ghosn · Shanshan Zhang · Wei Ye · Ze Cheng · Shikun Zhang · Tao Qin · Tie-Yan Liu
Lightning Talk
Junyu Xie · Chengliang Zhong · Ali Ayub · Sravanti Addepalli · Harsh Rangwani · Jiapeng Tang · Yuchen Rao · Zhiying Jiang · Yuqi Wang · Xingzhe He · Gene Chou · Ilya Chugunov · Samyak Jain · Yuntao Chen · Weidi Xie · Sumukh K Aithal · Carter Fendley · Lev Markhasin · Yiqin Dai · Peixing You · Bastian Wandt · Yinyu Nie · Helge Rhodin · Felix Heide · Ji Xin · Angela Dai · Andrew Zisserman · Bi Wang · Xiaoxue Chen · Mayank Mishra · ZHAO-XIANG ZHANG · Venkatesh Babu R · Justus Thies · Ming Li · Hao Zhao · Venkatesh Babu R · Jimmy Lin · Fuchun Sun · Matthias Niessner · Guyue Zhou · Xiaodong Mu · Chuang Gan · Wenbing Huang
Lightning Talk
Xiu-Shen Wei · Konstantina Dritsa · Guillaume Huguet · ABHRA CHAUDHURI · Zhenbin Wang · Kevin Qinghong Lin · Yutong Chen · Jianan Zhou · Yongsen Mao · Junwei Liang · Jinpeng Wang · Mao Ye · Yiming Zhang · Aikaterini Thoma · H.-Y. Xu · Daniel Sumner Magruder · Enwei Zhang · Jianing Zhu · Ronglai Zuo · Massimiliano Mancini · Hanxiao Jiang · Jun Zhang · Fangyun Wei · Faen Zhang · Ioannis Pavlopoulos · Zeynep Akata · Xiatian Zhu · Jingfeng ZHANG · Alexander Tong · Mattia Soldan · Chunhua Shen · Yuxin Peng · Liuhan Peng · Michael Wray · Tongliang Liu · Anjan Dutta · Yu Wu · Oluwadamilola Fasina · Panos Louridas · Angel Chang · Manik Kuchroo · Manolis Savva · Shujie LIU · Wei Zhou · Rui Yan · Gang Niu · Liang Tian · Bo Han · Eric Z. XU · Guy Wolf · Yingying Zhu · Brian Mak · Difei Gao · Masashi Sugiyama · Smita Krishnaswamy · Rong-Cheng Tu · Wenzhe Zhao · Weijie Kong · Chengfei Cai · WANG HongFa · Dima Damen · Bernard Ghanem · Wei Liu · Mike Zheng Shou

Spotlight: Featured Papers Panels 6B Thu 8 Dec 07:00 p.m.  

Each panel session is split into four 30-minute blocks composed of a set of lightning talks and a deep dive session related to similar topics. The deep dive will begin immediately after lightning talks and the related Q&A (it might be before the 15 min are over). We will not take any questions via microphone but ask you to use slido (see embedding below or go to https://slido.com and use keyword #neurips22). If you are a presenter or moderator, you should see a zoom link that you can use to join the session for Q&A.

Finally some important don'ts: DO NOT share any zoom or slido information publicly. DO NOT join zoom if you are not presenting or moderating.

Lightning Talk
Yushun Zhang · Duc Nguyen · Jiancong Xiao · Wei Jiang · Yaohua Wang · Yilun Xu · Zhen LI · Anderson Ye Zhang · Ziming Liu · Fangyi Zhang · Gilles Stoltz · Congliang Chen · Gang Li · Yanbo Fan · Ruoyu Sun · Naichen Shi · Yibo Wang · Ming Lin · Max Tegmark · Lijun Zhang · Jue Wang · Ruoyu Sun · Tommi Jaakkola · Senzhang Wang · Zhi-Quan Luo · Xiuyu Sun · Zhi-Quan Luo · Tianbao Yang · Rong Jin
Lightning Talk
Alexander Korotin · Jinyuan Jia · Weijian Deng · Shi Feng · Maying Shen · Denizalp Goktas · Fang-Yi Yu · Alexander Kolesov · Sadie Zhao · Stephen Gould · Hongxu Yin · Wenjie Qu · Liang Zheng · Evgeny Burnaev · Amy Greenwald · Neil Gong · Pavlo Molchanov · Yiling Chen · Lei Mao · Jianna Liu · Jose M. Alvarez
Lightning Talk
Lingfeng Yang · Yao Lai · Zizheng Pan · Zhenyu Wang · WEICONG LIANG · Chuanyang Zheng · Jian-Wei Zhang · Peng Jin · Jing Liu · Xiuying Wei · Yao Mu · Xiang Li · YUHUI YUAN · Zizheng Pan · Yifan Sun · Yunchen Zhang · Jianfei Cai · Hao Luo · zheyang li · Jinfa Huang · Haoyu He · Yi Yang · Ping Luo · Fenglin Liu · Henghui Ding · Borui Zhao · Xiangguo Zhang · Kai Zhang · Pichao WANG · Bohan Zhuang · Wei Chen · Ruihao Gong · Zhi Yang · Xian Wu · Feng Ding · Jianfei Cai · Xiao Luo · Renjie Song · Weihong Lin · Jian Yang · Wenming Tan · Bohan Zhuang · Shanghang Zhang · Shen Ge · Fan Wang · Qi Zhang · Guoli Song · Jun Xiao · Hao Li · Ding Jia · David Clifton · Ye Ren · Fengwei Yu · Zheng Zhang · Jie Chen · Shiliang Pu · Xianglong Liu · Chao Zhang · Han Hu
Lightning Talk
Junjie Chen · Chuanxia Zheng · JINLONG LI · Yu Shi · Shichao Kan · Yu Wang · Fermín Travi · Ninh Pham · Lei Chai · Guobing Gan · Tung-Long Vuong · Gonzalo Ruarte · Tao Liu · Li Niu · Jingjing Zou · Zequn Jie · Peng Zhang · Ming LI · Yixiong Liang · Guolin Ke · Jianfei Cai · Gaston Bujia · Sunzhu Li · Siyuan Zhou · Jingyang Lin · Xu Wang · Min Li · Zhuoming Chen · Qing Ling · Xiaolin Wei · Xiuqing Lu · Shuxin Zheng · Dinh Phung · Yigang Cen · Jianlou Si · Juan Esteban Kamienkowski · Jianxin Wang · Chen Qian · Lin Ma · Benyou Wang · Yingwei Pan · Tie-Yan Liu · Liqing Zhang · Zhihai He · Ting Yao · Tao Mei