no code implementations • 23 May 2023 • SiQi Liu, Weixi Feng, Wenhu Chen, William Yang Wang
Making image retrieval methods practical for real-world search applications requires significant progress in dataset scales, entity comprehension, and multimodal information fusion.
no code implementations • 6 Dec 2022 • Yong-Lu Li, Yue Xu, Xinyu Xu, Xiaohan Mao, Yuan YAO, SiQi Liu, Cewu Lu
To support OCL, we build a densely annotated knowledge base including extensive labels for three levels of object concept (category, attribute, affordance), and the causal relations of three levels.
no code implementations • 17 Oct 2022 • Luke Marris, Ian Gemp, Thomas Anthony, Andrea Tacchetti, SiQi Liu, Karl Tuyls
We argue that such a network is a powerful component for many possible multiagent algorithms.
no code implementations • 22 Sep 2022 • Ian Gemp, Thomas Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome Connor, Vibhavari Dasagi, Bart De Vylder, Edgar Duenez-Guzman, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, SiQi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Perolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.
no code implementations • 17 Sep 2022 • SiQi Liu, Andreas Lehrmann
Deep learning models have shown impressive results in a variety of time series forecasting tasks, where modeling the conditional distribution of the future given the past is the essence.
no code implementations • 31 May 2022 • SiQi Liu, Marc Lanctot, Luke Marris, Nicolas Heess
Learning to play optimally against any mixture over a diverse set of strategies is of important practical interests in competitive games.
no code implementations • 21 Apr 2022 • Bobak Shahriari, Abbas Abdolmaleki, Arunkumar Byravan, Abe Friesen, SiQi Liu, Jost Tobias Springenberg, Nicolas Heess, Matt Hoffman, Martin Riedmiller
Actor-critic algorithms that make use of distributional policy evaluation have frequently been shown to outperform their non-distributional counterparts on many challenging control tasks.
no code implementations • 2 Mar 2022 • Jiri Gesi, SiQi Liu, Jiawei Li, Iftekhar Ahmed, Nachiappan Nagappan, David Lo, Eduardo Santana de Almeida, Pavneet Singh Kochhar, Lingfeng Bao
We found that our newly identified code smells are prevalent and impactful on the maintenance of DL systems from the developer's perspective.
no code implementations • ICLR 2022 • SiQi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel
Learning in strategy games (e. g. StarCraft, poker) requires the discovery of diverse policies.
no code implementations • 8 Oct 2021 • Marta Garnelo, Wojciech Marian Czarnecki, SiQi Liu, Dhruva Tirumala, Junhyuk Oh, Gauthier Gidel, Hado van Hasselt, David Balduzzi
Strategic diversity is often essential in games: in multi-player games, for example, evaluating a player against a diverse set of strategies will yield a more accurate estimate of its performance.
no code implementations • 14 Aug 2021 • Heng Wang, Chaoyi Zhang, Jianhui Yu, Yang song, SiQi Liu, Wojciech Chrzanowski, Weidong Cai
Recently, a series of deep learning based segmentation methods have been proposed to improve the quality of raw 3D optical image stacks by removing noises and restoring neuronal structures from low-contrast background.
1 code implementation • 10 Jul 2021 • Haiyang Wang, Guangyu Zhou, SiQi Liu, Jyun-Yu Jiang, Wei Wang
For better learning and interpreting the DTI topological structure and the similarity, it is desirable to have methods specifically for predicting interactions from the graph structure.
no code implementations • 3 Jun 2021 • Tiange Xiang, Chaoyi Zhang, Yang song, SiQi Liu, Hongliang Yuan, Weidong Cai
This add-on graph regularizes the network during training and can be completely skipped during inference.
1 code implementation • 25 May 2021 • SiQi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls, Thore Graepel, Nicolas Heess
In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds.
no code implementations • 26 Jan 2021 • SiQi Liu, Sidhanth Mohanty, Prasad Raghavendra
For instance, in a planted constraint satisfaction problem such as planted 3-SAT, the clauses are sparse observations from which the hidden assignment is to be recovered.
Community Detection Data Structures and Algorithms Probability
no code implementations • 22 Jan 2021 • Heng Wang, Yang song, Chaoyi Zhang, Jianhui Yu, SiQi Liu, Hanchuan Peng, Weidong Cai
One of the critical steps in improving accurate single neuron reconstruction from three-dimensional (3D) optical microscope images is the neuronal structure segmentation.
1 code implementation • 16 Dec 2020 • Yi Zhou, Guillermo Gallego, Xiuyuan Lu, SiQi Liu, Shaojie Shen
We develop a method to identify independently moving objects acquired with an event-based camera, i. e., to solve the event-based motion segmentation problem.
no code implementations • 12 Dec 2020 • Zhaowei Zhu, Xiang Lan, Tingting Zhao, Yangming Guo, Pipin Kojodjojo, Zhuoyang Xu, Zhuo Liu, SiQi Liu, Han Wang, Xingzhi Sun, Mengling Feng
Cardiovascular disease is a major threat to health and one of the primary causes of death globally.
no code implementations • 23 Aug 2019 • Liqun Shao, Yiwen Zhu, Abhiram Eswaran, Kristin Lieber, Janhavi Mahajan, Minsoo Thigpen, Sudhir Darbha, SiQi Liu, Subru Krishnan, Soundar Srinivasan, Carlo Curino, Konstantinos Karanasos
In contrast, in Griffin we cast the problem to a corresponding regression one that predicts the runtime of a job, and show how the relative contributions of the features used to train our interpretable model can be exploited to rank the potential causes of job slowdowns.