Search Results for author: Yichen Zhou

Found 18 papers, 8 papers with code

Generalized Damping Torque Analysis of Ultra-Low Frequency Oscillation in the Jerk Space

no code implementations7 Dec 2023 Yichen Zhou, Yang Yang, Tao Zhou, Yonggang Li

A multi-information variable is constructed to transform the system into a new state space, where it is found that the jerk dynamics of the turbine-generator cascaded system is a second-order differential equation.

A decoder-only foundation model for time-series forecasting

1 code implementation14 Oct 2023 Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou

Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset.

Decoder Time Series +1

MetaFormer Baselines for Vision

7 code implementations24 Oct 2022 Weihao Yu, Chenyang Si, Pan Zhou, Mi Luo, Yichen Zhou, Jiashi Feng, Shuicheng Yan, Xinchao Wang

By simply applying depthwise separable convolutions as token mixer in the bottom stages and vanilla self-attention in the top stages, the resulting model CAFormer sets a new record on ImageNet-1K: it achieves an accuracy of 85. 5% at 224x224 resolution, under normal supervised training without external data or distillation.

Ranked #2 on Domain Generalization on ImageNet-C (using extra training data)

Domain Generalization Image Classification

Robust Distillation for Worst-class Performance

no code implementations13 Jun 2022 Serena Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, Michal Lukasik, Aditya Krishna Menon

We show empirically that our robust distillation techniques not only achieve better worst-class performance, but also lead to Pareto improvement in the tradeoff between overall performance and worst-class performance compared to other baseline methods.

Knowledge Distillation

Inception Transformer

3 code implementations25 May 2022 Chenyang Si, Weihao Yu, Pan Zhou, Yichen Zhou, Xinchao Wang, Shuicheng Yan

Recent studies show that Transformer has strong capability of building long-range dependencies, yet is incompetent in capturing high frequencies that predominantly convey local information.

Image Classification

Mugs: A Multi-Granular Self-Supervised Learning Framework

1 code implementation27 Mar 2022 Pan Zhou, Yichen Zhou, Chenyang Si, Weihao Yu, Teck Khim Ng, Shuicheng Yan

It provides complementary instance supervision to IDS via an extra alignment on local neighbors, and scatters different local-groups separately to increase discriminability.

Contrastive Learning Self-Supervised Image Classification +3

MetaFormer Is Actually What You Need for Vision

14 code implementations CVPR 2022 Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, Shuicheng Yan

Based on this observation, we hypothesize that the general architecture of the Transformers, instead of the specific token mixer module, is more essential to the model's performance.

Image Classification Object Detection +1

Malicious Mode Attack on EV Coordinated Charging Load and MIADRC Defense Strategy

no code implementations26 Oct 2021 Yichen Zhou, Weidong Liu, Jing Ma, Xinghao Zhen, Yonggang Li

Further, to mitigate the impact of MMA, a defense strategy based on multi-index information active disturbance rejection control is proposed to improve the stability and anti-disturbance ability of the power system, which considers the impact factors of both mode damping and disturbance compensation.

Distilling Double Descent

no code implementations13 Feb 2021 Andrew Cotter, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sashank J. Reddi, Yichen Zhou

Distillation is the technique of training a "student" model based on examples that are labeled by a separate "teacher" model, which itself is trained on a labeled dataset.

Approximate Heavily-Constrained Learning with Lagrange Multiplier Models

no code implementations NeurIPS 2020 Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Wang, Wenshuo Guo

In machine learning applications such as ranking fairness or fairness over intersectional groups, one often encounters optimization problems with an extremely large number of constraints.


A Simple Baseline for Pose Tracking in Videos of Crowded Scenes

no code implementations16 Oct 2020 Li Yuan, Shuning Chang, Ziyuan Huang, Yichen Zhou, Yunpeng Chen, Xuecheng Nie, Francis E. H. Tay, Jiashi Feng, Shuicheng Yan

This paper presents our solution to ACM MM challenge: Large-scale Human-centric Video Analysis in Complex Events\cite{lin2020human}; specifically, here we focus on Track3: Crowd Pose Tracking in Complex Events.

Multi-Object Tracking Optical Flow Estimation +1

Towards Accurate Human Pose Estimation in Videos of Crowded Scenes

no code implementations16 Oct 2020 Li Yuan, Shuning Chang, Xuecheng Nie, Ziyuan Huang, Yichen Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan

In this paper, we focus on improving human pose estimation in videos of crowded scenes from the perspectives of exploiting temporal context and collecting new data.

Optical Flow Estimation Pose Estimation

Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes

no code implementations16 Oct 2020 Li Yuan, Yichen Zhou, Shuning Chang, Ziyuan Huang, Yunpeng Chen, Xuecheng Nie, Tao Wang, Jiashi Feng, Shuicheng Yan

Prior works always fail to deal with this problem in two aspects: (1) lacking utilizing information of the scenes; (2) lacking training data in the crowd and complex scenes.

Action Recognition In Videos Semantic Segmentation

Tree Boosted Varying Coefficient Models

1 code implementation1 Apr 2019 Yichen Zhou, Giles Hooker

This paper investigates the integration of gradient boosted decision trees and varying coefficient models.


A Deep-Learning-Based Fashion Attributes Detection Model

1 code implementation24 Oct 2018 Menglin Jia, Yichen Zhou, Mengyun Shi, Bharath Hariharan

Such information analyzing process is called abstracting, which recognize similarities or differences across all the garments and collections.


Approximation Trees: Statistical Stability in Model Distillation

no code implementations22 Aug 2018 Yichen Zhou, Zhengze Zhou, Giles Hooker

Here, we consider the use of regression trees as a student model, in which nodes of the tree can be used as `explanations' for particular predictions, and the whole structure of the tree can be used as a global representation of the resulting function.

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