Search Results for author: Ziyang Wu

Found 10 papers, 6 papers with code

Efficient Maximal Coding Rate Reduction by Variational Forms

no code implementations31 Mar 2022 Christina Baek, Ziyang Wu, Kwan Ho Ryan Chan, Tianjiao Ding, Yi Ma, Benjamin D. Haeffele

The principle of Maximal Coding Rate Reduction (MCR$^2$) has recently been proposed as a training objective for learning discriminative low-dimensional structures intrinsic to high-dimensional data to allow for more robust training than standard approaches, such as cross-entropy minimization.

Image Classification

Incremental Learning of Structured Memory via Closed-Loop Transcription

no code implementations11 Feb 2022 Shengbang Tong, Xili Dai, Ziyang Wu, Mingyang Li, Brent Yi, Yi Ma

This work proposes a minimal computational model for learning a structured memory of multiple object classes in an incremental setting.

Incremental Learning

Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction

1 code implementation12 Nov 2021 Xili Dai, Shengbang Tong, Mingyang Li, Ziyang Wu, Michael Psenka, Kwan Ho Ryan Chan, Pengyuan Zhai, Yaodong Yu, Xiaojun Yuan, Heung Yeung Shum, Yi Ma

In particular, we propose to learn a closed-loop transcription between a multi-class multi-dimensional data distribution and a linear discriminative representation (LDR) in the feature space that consists of multiple independent multi-dimensional linear subspaces.

Can We Characterize Tasks Without Labels or Features?

1 code implementation CVPR 2021 Bram Wallace, Ziyang Wu, Bharath Hariharan

The problem of expert model selection deals with choosing the appropriate pretrained network ("expert") to transfer to a target task.

Model Selection

Incremental Learning via Rate Reduction

no code implementations CVPR 2021 Ziyang Wu, Christina Baek, Chong You, Yi Ma

Current deep learning architectures suffer from catastrophic forgetting, a failure to retain knowledge of previously learned classes when incrementally trained on new classes.

Incremental Learning

Efficient AutoML Pipeline Search with Matrix and Tensor Factorization

1 code implementation7 Jun 2020 Chengrun Yang, Jicong Fan, Ziyang Wu, Madeleine Udell

Data scientists seeking a good supervised learning model on a new dataset have many choices to make: they must preprocess the data, select features, possibly reduce the dimension, select an estimation algorithm, and choose hyperparameters for each of these pipeline components.

AutoML

PARN: Position-Aware Relation Networks for Few-Shot Learning

1 code implementation ICCV 2019 Ziyang Wu, Yuwei Li, Lihua Guo, Kui Jia

However, due to the inherent local connectivity of CNN, the CNN-based relation network (RN) can be sensitive to the spatial position relationship of semantic objects in two compared images.

Few-Shot Learning Relational Reasoning

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