Search Results for author: Ke Ye

Found 6 papers, 1 papers with code

SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection

no code implementations24 Jan 2024 Ke Ye, Heinrich Jiang, Afshin Rostamizadeh, Ayan Chakrabarti, Giulia Desalvo, Jean-François Kagy, Lazaros Karydas, Gui Citovsky, Sanjiv Kumar

In this paper, we present SpacTor, a new training procedure consisting of (1) a hybrid objective combining span corruption (SC) and token replacement detection (RTD), and (2) a two-stage curriculum that optimizes the hybrid objective over the initial $\tau$ iterations, then transitions to standard SC loss.

FFINet: Future Feedback Interaction Network for Motion Forecasting

no code implementations8 Nov 2023 Miao Kang, Shengqi Wang, Sanping Zhou, Ke Ye, Jingjing Jiang, Nanning Zheng

In this paper, we propose a novel Future Feedback Interaction Network (FFINet) to aggregate features the current observations and potential future interactions for trajectory prediction.

Motion Forecasting Position +1

The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers

no code implementations12 Oct 2022 Zonglin Li, Chong You, Srinadh Bhojanapalli, Daliang Li, Ankit Singh Rawat, Sashank J. Reddi, Ke Ye, Felix Chern, Felix Yu, Ruiqi Guo, Sanjiv Kumar

This paper studies the curious phenomenon for machine learning models with Transformer architectures that their activation maps are sparse.

Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry

no code implementations25 Jun 2020 Ke Ye, Mu Niu, Pokman Cheung, Zhenwen Dai, YuAn Liu

The introduction of our strip algorithm, tailored for manifolds with extra symmetries, and the ball algorithm, designed for arbitrary manifolds, constitutes our significant contribution.

Gaussian Processes regression

Semi-Riemannian Manifold Optimization

1 code implementation18 Dec 2018 Tingran Gao, Lek-Heng Lim, Ke Ye

We introduce in this paper a manifold optimization framework that utilizes semi-Riemannian structures on the underlying smooth manifolds.

Optimization and Control Numerical Analysis 90C30, 53C50, 53B30, 49M05, 49M15 F.2.1; G.1.6

Cohomology of Cryo-Electron Microscopy

no code implementations5 Apr 2016 Ke Ye, Lek-Heng Lim

The goal of cryo-electron microscopy (EM) is to reconstruct the 3-dimensional structure of a molecule from a collection of its 2-dimensional projected images.

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