Search Results for author: Kai Zhu

Found 9 papers, 1 papers with code

Mitigating Greenhouse Gas Emissions Through Generative Adversarial Networks Based Wildfire Prediction

no code implementations20 Aug 2021 Sifat Chowdhury, Kai Zhu, Yu Zhang

Over the past decade, the number of wildfire has increased significantly around the world, especially in the State of California.

Data Augmentation

FusionStitching: Boosting Memory Intensive Computations for Deep Learning Workloads

no code implementations23 Sep 2020 Zhen Zheng, Pengzhan Zhao, Guoping Long, Feiwen Zhu, Kai Zhu, Wenyi Zhao, Lansong Diao, Jun Yang, Wei. Lin

We show in this work that memory intensive computations can result in severe performance problems due to off-chip memory access and CPU-GPU context switch overheads in a wide range of deep learning models.

Code Generation

Self-Supervised Tuning for Few-Shot Segmentation

no code implementations12 Apr 2020 Kai Zhu, Wei Zhai, Zheng-Jun Zha, Yang Cao

Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples.

Meta-Learning

One-Shot Texture Retrieval with Global Context Metric

no code implementations16 May 2019 Kai Zhu, Wei Zhai, Zheng-Jun Zha, Yang Cao

In this paper, we tackle one-shot texture retrieval: given an example of a new reference texture, detect and segment all the pixels of the same texture category within an arbitrary image.

FusionStitching: Deep Fusion and Code Generation for Tensorflow Computations on GPUs

no code implementations13 Nov 2018 Guoping Long, Jun Yang, Kai Zhu, Wei. Lin

In recent years, there is a surge on machine learning applications in industry.

Distributed, Parallel, and Cluster Computing Mathematical Software

Recognize Complex Events From Static Images by Fusing Deep Channels

no code implementations CVPR 2015 Yuanjun Xiong, Kai Zhu, Dahua Lin, Xiaoou Tang

A considerable portion of web images capture events that occur in our personal lives or social activities.

Collaborative Filtering with Information-Rich and Information-Sparse Entities

no code implementations6 Mar 2014 Kai Zhu, Rui Wu, Lei Ying, R. Srikant

In particular, we consider both the clustering model, where only users (or items) are clustered, and the co-clustering model, where both users and items are clustered, and further, we assume that some users rate many items (information-rich users) and some users rate only a few items (information-sparse users).

Collaborative Filtering Recommendation Systems

Jointly Clustering Rows and Columns of Binary Matrices: Algorithms and Trade-offs

no code implementations1 Oct 2013 Jiaming Xu, Rui Wu, Kai Zhu, Bruce Hajek, R. Srikant, Lei Ying

In standard clustering problems, data points are represented by vectors, and by stacking them together, one forms a data matrix with row or column cluster structure.

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