Author name cluster

Yu Gong

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7 papers

2 author rows

ECAI Conference 2024 Conference Paper

Dual Attention Encoder with Joint Preservation for Medical Image Segmentation

Shijie Li
Yunbin Tu
Yu Gong
Bowen Zhong
Zheng Li

Transformers have recently gained considerable popularity for capturing long-range dependencies in the medical image segmentation. However, most transformer-based segmentation methods primarily focus on modeling global dependencies and fail to fully explore the complementary nature of different dimensional dependencies within features. These methods simply treat the aggregation of multi-dimensional dependencies as auxiliary modules for incorporating context into the Transformer architecture, thereby limiting the model’s capability to learn rich feature representations. To address this issue, we introduce the Dual Attention Encoder with Joint Preservation (DANIE) for medical image segmentation, which synergistically aggregates spatial-channel dependencies across both local and global areas through attention learning. Additionally, we design a lightweight aggregation mechanism, termed Joint Preservation, which learns a composite feature representation, allowing different dependencies to complement each other. Without bells and whistles, our DANIE significantly improves the performance of previous state-of-the-art methods on five popular medical image segmentation benchmarks, including Synapse, ACDC, ISIC 2017, ISIC 2018 and GlaS.

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AAAI Conference 2023 Conference Paper

HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks

Jinqi Xiao
Chengming Zhang
Yu Gong
Miao Yin
Yang Sui
Lizhi Xiang
Dingwen Tao
Bo Yuan

Low-rank compression is an important model compression strategy for obtaining compact neural network models. In general, because the rank values directly determine the model complexity and model accuracy, proper selection of layer-wise rank is very critical and desired. To date, though many low-rank compression approaches, either selecting the ranks in a manual or automatic way, have been proposed, they suffer from costly manual trials or unsatisfied compression performance. In addition, all of the existing works are not designed in a hardware-aware way, limiting the practical performance of the compressed models on real-world hardware platforms. To address these challenges, in this paper we propose HALOC, a hardware-aware automatic low-rank compression framework. By interpreting automatic rank selection from an architecture search perspective, we develop an end-to-end solution to determine the suitable layer-wise ranks in a differentiable and hardware-aware way. We further propose design principles and mitigation strategy to efficiently explore the rank space and reduce the potential interference problem. Experimental results on different datasets and hardware platforms demonstrate the effectiveness of our proposed approach. On CIFAR-10 dataset, HALOC enables 0.07% and 0.38% accuracy increase over the uncompressed ResNet-20 and VGG-16 models with 72.20% and 86.44% fewer FLOPs, respectively. On ImageNet dataset, HALOC achieves 0.9% higher top-1 accuracy than the original ResNet-18 model with 66.16% fewer FLOPs. HALOC also shows 0.66% higher top-1 accuracy increase than the state-of-the-art automatic low-rank compression solution with fewer computational and memory costs. In addition, HALOC demonstrates the practical speedups on different hardware platforms, verified by the measurement results on desktop GPU, embedded GPU and ASIC accelerator.

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ICML Conference 2022 Conference Paper

RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression

Yu Gong
Greg Mori
Frederick Tung

Data imbalance, in which a plurality of the data samples come from a small proportion of labels, poses a challenge in training deep neural networks. Unlike classification, in regression the labels are continuous, potentially boundless, and form a natural ordering. These distinct features of regression call for new techniques that leverage the additional information encoded in label-space relationships. This paper presents the RankSim (ranking similarity) regularizer for deep imbalanced regression, which encodes an inductive bias that samples that are closer in label space should also be closer in feature space. In contrast to recent distribution smoothing based approaches, RankSim captures both nearby and distant relationships: for a given data sample, RankSim encourages the sorted list of its neighbors in label space to match the sorted list of its neighbors in feature space. RankSim is complementary to conventional imbalanced learning techniques, including re-weighting, two-stage training, and distribution smoothing, and lifts the state-of-the-art performance on three imbalanced regression benchmarks: IMDB-WIKI-DIR, AgeDB-DIR, and STS-B-DIR.

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AAAI Conference 2021 Conference Paper

Personalized Adaptive Meta Learning for Cold-start User Preference Prediction

Runsheng Yu
Yu Gong
Xu He
Yu Zhu
Qingwen Liu
Wenwu Ou
Bo An

A common challenge in personalized user preference prediction is the cold-start problem. Due to the lack of user-item interactions, directly learning from the new users’ log data causes serious over-fitting problem. Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot learning problem, where each user is the task and recommended items are the classes, and the gradient-based meta learning method (MAML) is leveraged to address this challenge. However, in real-world application, the users are not uniformly distributed (i. e. , different users may have different browsing history, recommended items, and user profiles. We define the major users as the users in the groups with large numbers of users sharing similar user information, and other users are the minor users), existing MAML approaches tend to fit the major users and ignore the minor users. To address this cold-start task-overfitting problem, we propose a novel personalized adaptive meta learning approach to consider both the major and the minor users with three key contributions: 1) We are the first to present a personalized adaptive learning rate meta-learning approach to improve the performance of MAML by focusing on both the major and minor users. 2) To provide better personalized learning rates for each user, we introduce a similarity-based method to find similar users as a reference and a tree-based method to store users’ features for fast search. 3) To reduce the memory usage, we design a memory agnostic regularizer to further reduce the space complexity to constant while maintain the performance. Experiments on MovieLens, BookCrossing, and real-world production datasets reveal that our method outperforms the state-of-the-art methods dramatically for both the minor and major users.

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AAMAS Conference 2019 Conference Paper

A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem

Lu Duan
Haoyuan Hu
Yu Qian
Yu Gong
Xiaodong Zhang
Jiangwen Wei
Yinghui Xu

A 3D flexible bin packing problem (3D-FBPP) arises from the process of warehouse packing in e-commerce. An online customer’s order usually contains several items and needs to be packed as a whole before shipping. In particular, 5% of tens of millions of packages are using plastic wrapping as outer packaging every day, which brings pressure on the plastic surface minimization to save traditional logistics costs. Because of the huge practical significance, we focus on the issue of packing cuboid-shaped items orthogonally into a least-surface-area bin. The existing heuristic methods for classic 3D bin packing don’t work well for this particular NP-hard problem and designing a good problem-specific heuristic is nontrivial. In this paper, rather than designing heuristics, we propose a novel multi-task framework based on Selected Learning to learn a heuristic-like policy that generates the sequence and orientations of items to be packed simultaneously. Through comprehensive experiments on a large scale real-world transaction order dataset and online AB tests, we show: 1) our selected learning method trades off the imbalance and correlation among the tasks and significantly outperforms the single task Pointer Network and the multi-task network without selected learning; 2) our method obtains an average 5. 47% cost reduction than the well-designed greedy algorithm which is previously used in our online production system.

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AAAI Conference 2019 Conference Paper

Deep Cascade Multi-Task Learning for Slot Filling in Online Shopping Assistant

Yu Gong
Xusheng Luo
Yu Zhu
Wenwu Ou
Zhao Li
Muhua Zhu
Kenny Q. Zhu
Lu Duan

Slot filling is a critical task in natural language understanding (NLU) for dialog systems. State-of-the-art approaches treat it as a sequence labeling problem and adopt such models as BiLSTM-CRF. While these models work relatively well on standard benchmark datasets, they face challenges in the context of E-commerce where the slot labels are more informative and carry richer expressions. In this work, inspired by the unique structure of E-commerce knowledge base, we propose a novel multi-task model with cascade and residual connections, which jointly learns segment tagging, named entity tagging and slot filling. Experiments show the effectiveness of the proposed cascade and residual structures. Our model has a 14. 6% advantage in F1 score over the strong baseline methods on a new Chinese E-commerce shopping assistant dataset, while achieving competitive accuracies on a standard dataset. Furthermore, online test deployed on such dominant E-commerce platform shows 130% improvement on accuracy of understanding user utterances. Our model has already gone into production in the E-commerce platform.

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AAAI Conference 2016 Conference Paper

Representing Verbs as Argument Concepts

Yu Gong
Kaiqi Zhao
Kenny Zhu

Verbs play an important role in the understanding of natural language text. This paper studies the problem of abstracting the subject and object arguments of a verb into a set of noun concepts, known as the “argument concepts”. This set of concepts, whose size is parameterized, represents the ﬁnegrained semantics of a verb. For example, the object of “enjoy” can be abstracted into time, hobby and event, etc. We present a novel framework to automatically infer human readable and machine computable action concepts with high accuracy.

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