Search Results for author: Chang Li

Found 25 papers, 5 papers with code

Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey

no code implementations8 Dec 2021 Xun Chen, Chang Li, Aiping Liu, Martin J. McKeown, Ruobing Qian, Z. Jane Wang

Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on non-invasively measured brain activity.

EEG Eeg Decoding

Optimizing Ranking Systems Online as Bandits

no code implementations12 Oct 2021 Chang Li

Optimizing ranking systems online means that the deployed system can serve user requests, e. g., queries in the web search, and optimize the ranking policy by learning from user interactions, e. g., clicks.

Learning-To-Rank Online Ranker Evaluation +1

Elastic Tactile Simulation Towards Tactile-Visual Perception

1 code implementation11 Aug 2021 Yikai Wang, Wenbing Huang, Bin Fang, Fuchun Sun, Chang Li

By contrast, EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact.

Federated Unbiased Learning to Rank

no code implementations11 May 2021 Chang Li, Hua Ouyang

Unbiased Learning to Rank (ULTR) studies the problem of learning a ranking function based on biased user interactions.

Federated Learning Learning-To-Rank

Experimental demonstration of memory-enhanced scaling for entanglement connection of quantum repeater segments

no code implementations21 Jan 2021 Yunfei Pu, Sheng Zhang, Yukai Wu, Nan Jiang, Wei Chang, Chang Li, Luming Duan

The experimental realization of entanglement connection of two quantum repeater segments with an efficient memory-enhanced scaling demonstrates a key advantage of the quantum repeater protocol, which makes a cornerstone towards future large-scale quantum networks.

Quantum Physics

Human Action Recognition Based on Multi-scale Feature Maps from Depth Video Sequences

no code implementations19 Jan 2021 Chang Li, Qian Huang, Xing Li, Qianhan Wu

We employ depth motion images (DMI) as the templates to generate the multi-scale static representation of actions.

Action Classification Action Recognition

The Skill-Action Architecture: Learning Abstract Action Embeddings for Reinforcement Learning

no code implementations1 Jan 2021 Chang Li, Dongjin Song, DaCheng Tao

Derived from a novel discovery that the SMDP option framework has an MDP equivalence, SA hierarchically extracts skills (abstract actions) from primary actions and explicitly encodes these knowledge into skill context vectors (embedding vectors).

Hierarchical Reinforcement Learning

Intrinsic motivation in virtual assistant interaction for fostering spontaneous interactions

no code implementations13 Oct 2020 Chang Li, Hideyoshi Yanagisawa

A novel motivation model is proposed, in which intrinsic motivation is affected by two factors that derive from user interactions with virtual assistants: expectation of capability and uncertainty.

Human robot interaction

Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity

no code implementations1 Dec 2019 Chang Li, Haoyun Feng, Maarten de Rijke

Relevance ranking aims at building a ranked list sorted in decreasing order of item relevance, while result diversification focuses on generating a ranked list of items that covers a broad range of topics.

Learning-To-Rank Recommendation Systems

Quantum Communication between Multiplexed Atomic Quantum Memories

no code implementations5 Sep 2019 Chang Li, Nan Jiang, Yukai Wu, Wei Chang, Yunfei Pu, Sheng Zhang, Lu-Ming Duan

The use of multiplexed atomic quantum memories (MAQM) can significantly enhance the efficiency to establish entanglement in a quantum network.

Quantum Physics

Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media

no code implementations ACL 2019 Chang Li, Dan Goldwasser

Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task.

Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model

no code implementations29 May 2019 Chang Li, Maarten de Rijke

We consider the problem of identifying the K most attractive items and propose cascading non-stationary bandits, an online learning variant of the cascading model, where a user browses a ranked list from top to bottom and clicks on the first attractive item.

Learning-To-Rank

MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation

1 code implementation11 Dec 2018 Chang Li, Ilya Markov, Maarten de Rijke, Masrour Zoghi

Our main finding is that for large-scale Condorcet ranker evaluation problems, MergeDTS outperforms the state-of-the-art dueling bandit algorithms.

Information Retrieval Online Ranker Evaluation

Incremental Sparse Bayesian Ordinal Regression

1 code implementation18 Jun 2018 Chang Li, Maarten de Rijke

Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning.

Multi-Label Learning

BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback

no code implementations15 Jun 2018 Chang Li, Branislav Kveton, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvari, Masrour Zoghi

In this paper, we study the problem of safe online learning to re-rank, where user feedback is used to improve the quality of displayed lists.

Learning-To-Rank Re-Ranking +1

Probabilistic Feature Selection and Classification Vector Machine

no code implementations18 Sep 2016 Bingbing Jiang, Chang Li, Maarten de Rijke, Xin Yao, Huanhuan Chen

The proposed method, called probabilistic feature selection and classification vector machine (PFCVMLP ), is able to simultaneously select relevant features and samples for classification tasks.

Feature Selection General Classification

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