Search Results for author: Yueming Wang

Found 11 papers, 2 papers with code

Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations

no code implementations27 Feb 2024 Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Michael He, Yinghai Lu, Yu Shi

Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a daily basis.

Recommendation Systems

MindGPT: Interpreting What You See with Non-invasive Brain Recordings

1 code implementation27 Sep 2023 Jiaxuan Chen, Yu Qi, Yueming Wang, Gang Pan

By doing so, we found that the neural representations of the MindGPT are explainable, which can be used to evaluate the contributions of visual properties to language semantics.

Language Modelling Large Language Model

A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training

no code implementations25 Aug 2023 Hanwen Wang, Yu Qi, Lin Yao, Yueming Wang, Dario Farina, Gang Pan

Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel ``copy/new'' feedback paradigm to help shape the signal generation of the subject toward the optimal distribution; 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process.

EEG Electroencephalogram (EEG)

Revisiting Neural Retrieval on Accelerators

no code implementations6 Jun 2023 Jiaqi Zhai, Zhaojie Gong, Yueming Wang, Xiao Sun, Zheng Yan, Fu Li, Xing Liu

A key component of retrieval is to model (user, item) similarity, which is commonly represented as the dot product of two learned embeddings.

Information Retrieval Retrieval

ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks

no code implementations6 Jun 2023 Jiangrong Shen, Qi Xu, Jian K. Liu, Yueming Wang, Gang Pan, Huajin Tang

To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse SNNs without redundancy connections after training.

LaSNN: Layer-wise ANN-to-SNN Distillation for Effective and Efficient Training in Deep Spiking Neural Networks

no code implementations17 Apr 2023 Di Hong, Jiangrong Shen, Yu Qi, Yueming Wang

A conversion scheme is proposed to obtain competitive accuracy by mapping trained ANNs' parameters to SNNs with the same structures.

Knowledge Distillation

Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-machine Interface

no code implementations22 Apr 2022 Yu Qi, Xinyun Zhu, Kedi Xu, Feixiao Ren, Hongjie Jiang, Junming Zhu, Jianmin Zhang, Gang Pan, Yueming Wang

In this way, DyEnsemble copes with variability in signals and improves the robustness of online control.

Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces

1 code implementation NeurIPS 2019 Yu Qi, Bin Liu, Yueming Wang, Gang Pan

Brain-computer interfaces (BCIs) have enabled prosthetic device control by decoding motor movements from neural activities.

Robust Face Recognition by Constrained Part-based Alignment

no code implementations20 Jan 2015 Yuting Zhang, Kui Jia, Yueming Wang, Gang Pan, Tsung-Han Chan, Yi Ma

By assuming a human face as piece-wise planar surfaces, where each surface corresponds to a facial part, we develop in this paper a Constrained Part-based Alignment (CPA) algorithm for face recognition across pose and/or expression.

Face Alignment Face Recognition +1

Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut

no code implementations25 Mar 2014 Zhenfang Hu, Gang Pan, Yueming Wang, Zhaohui Wu

The methods include PCA, K-means, Laplacian eigenmap (LE), ratio cut (Rcut), and a new sparse representation method developed by us, called spectral sparse representation (SSR).

Clustering Dimensionality Reduction

Sparse Principal Component Analysis via Rotation and Truncation

no code implementations6 Mar 2014 Zhenfang Hu, Gang Pan, Yueming Wang, Zhaohui Wu

In contrast to most of existing work which deal with the problem by adding some sparsity penalties on various objectives of PCA, in this paper, we propose a new method SPCArt, whose motivation is to find a rotation matrix and a sparse basis such that the sparse basis approximates the basis of PCA after the rotation.

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