198 citations · 1k across the 28 of their papers we have counts for
8 papers · 1 filter
Latent Group Structured Multi-task Learning
Xiangyu Niu, Yifan Sun, Jinyuan Sun
In multi-task learning (MTL), we improve the performance of key machine learning algorithms by training various tasks jointly. When the number of tasks is large, modeling task stru…
MGPU-TSM: A Multi-GPU System with Truly Shared Memory
Saiful A. Mojumder, Yifan Sun, Leila Delshadtehrani +6
The sizes of GPU applications are rapidly growing. They are exhausting the compute and memory resources of a single GPU, and are demanding the move to multiple GPUs. However, the p…
Memory-based Jitter: Improving Visual Recognition on Long-tailed Data with Diversity In Memory
Jialun Liu, Jingwei Zhang, Yi yang +3
This paper considers deep visual recognition on long-tailed data. To be general, we consider two applied scenarios, \ie, deep classification and deep metric learning. Under the lon…
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions
Zhongdao Wang, Jingwei Zhang, Liang Zheng +4
This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as t…
HALCONE : A Hardware-Level Timestamp-based Cache Coherence Scheme for Multi-GPU systems
Saiful A. Mojumder, Yifan Sun, Leila Delshadtehrani +6
While multi-GPU (MGPU) systems are extremely popular for compute-intensive workloads, several inefficiencies in the memory hierarchy and data movement result in a waste of GPU reso…
Embodied Self-supervised Learning by Coordinated Sampling and Training
Yifan Sun, Xihong Wu
Self-supervised learning can significantly improve the performance of downstream tasks, however, the dimensions of learned representations normally lack explicit physical meanings.…
Circle Loss: A Unified Perspective of Pair Similarity Optimization
Yifan Sun, Changmao Cheng, Yuhan Zhang +4
This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity and minimize the between-class similarit…
Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective
Jialun Liu, Yifan Sun, Chuchu Han +2
This paper considers learning deep features from long-tailed data. We observe that in the deep feature space, the head classes and the tail classes present different distribution p…