Search Results for author: SiQi Liu

Found 19 papers, 3 papers with code

EDIS: Entity-Driven Image Search over Multimodal Web Content

no code implementations23 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.

Image Retrieval Retrieval

Beyond Object Recognition: A New Benchmark towards Object Concept Learning

no code implementations6 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.

Object Recognition

Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments

no code implementations22 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.

reinforcement-learning Reinforcement Learning (RL)

DynaConF: Dynamic Forecasting of Non-Stationary Time-Series

no code implementations17 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.

Time Series Forecasting

Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games

no code implementations31 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.

Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach

no code implementations21 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.

Continuous Control reinforcement-learning +1

Code Smells in Machine Learning Systems

no code implementations2 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.

BIG-bench Machine Learning

Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity

no code implementations8 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.

Voxel-wise Cross-Volume Representation Learning for 3D Neuron Reconstruction

no code implementations14 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.

Representation Learning

Drug-Target Interaction Prediction with Graph Attention networks

1 code implementation10 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.

Graph Attention

Partial Graph Reasoning for Neural Network Regularization

no code implementations3 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.

From Motor Control to Team Play in Simulated Humanoid Football

1 code implementation25 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.

Imitation Learning Multi-agent Reinforcement Learning +1

On statistical inference when fixed points of belief propagation are unstable

no code implementations26 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

Single Neuron Segmentation using Graph-based Global Reasoning with Auxiliary Skeleton Loss from 3D Optical Microscope Images

no code implementations22 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.

Event-based Motion Segmentation with Spatio-Temporal Graph Cuts

1 code implementation16 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.

Motion Segmentation Scene Understanding

Griffon: Reasoning about Job Anomalies with Unlabeled Data in Cloud-based Platforms

no code implementations23 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.

Time Series Analysis

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