1 code implementation • 14 Jun 2024 • Dongjie Yu, Hang Xu, Yizhou Chen, Yi Ren, Jia Pan
The predicted keyposes provide guidance for trajectory generation and also mark the completion of one sub-stage task.
1 code implementation • 23 Apr 2024 • Chenxing Hong, Yan Jin, Zhiqi Kang, Yizhou Chen, Mengke Li, Yang Lu, Hanzi Wang
We find that imbalanced tasks significantly challenge the capability of models to control the trade-off between stability and plasticity from the perspective of recent prompt-based continual learning methods.
no code implementations • 3 Feb 2023 • Yizhou Chen, Guangda Huzhang, AnXiang Zeng, Qingtao Yu, Hui Sun, Heng-yi Li, Jingyi Li, Yabo Ni, Han Yu, Zhiming Zhou
However, such a method has two important limitations in real-world applications: 1) it is hard to learn embeddings that generalize well for users and items with rare interactions on their own; and 2) it may incur unbearably high memory costs when the number of users and items scales up.
1 code implementation • 17 Oct 2022 • Yuying Hao, Yi Liu, Yizhou Chen, Lin Han, Juncai Peng, Shiyu Tang, Guowei Chen, Zewu Wu, Zeyu Chen, Baohua Lai
In recent years, the rapid development of deep learning has brought great advancements to image and video segmentation methods based on neural networks.
1 code implementation • 30 Sep 2022 • Yizhou Chen, Andrea Sipos, Mark Van der Merwe, Nima Fazeli
Learning representations in the joint domain of vision and touch can improve manipulation dexterity, robustness, and sample-complexity by exploiting mutual information and complementary cues.
1 code implementation • 14 Jun 2022 • Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet
We prove that both algorithms are asymptotically no-regret even when some or all previous tasks are dissimilar to the current task, and show that RM-GP-UCB enjoys a better theoretical robustness than RM-GP-TS.
1 code implementation • 14 Dec 2021 • Raquel Aoki, Yizhou Chen, Martin Ester
This work proposes the M3E2, a multi-task learning neural network model to estimate the effect of multiple treatments.
3 code implementations • 20 Sep 2021 • Yuying Hao, Yi Liu, Zewu Wu, Lin Han, Yizhou Chen, Guowei Chen, Lutao Chu, Shiyu Tang, Zhiliang Yu, Zeyu Chen, Baohua Lai
In addition, with the proposed method, we develop an efficient interactive segmentation tool for practical data annotation tasks.
Ranked #2 on Interactive Segmentation on PASCAL VOC (NoC@85 metric)
no code implementations • 6 Sep 2021 • Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low
Unfortunately, these NAS algorithms aim to select only one single well-performing architecture from their search spaces and thus have overlooked the capability of neural network ensemble (i. e., an ensemble of neural networks with diverse architectures) in achieving improved performance over a single final selected architecture.
no code implementations • 17 Apr 2021 • Haibin Yu, Dapeng Liu, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet
Deep Gaussian processes (DGPs), a hierarchical composition of GP models, have successfully boosted the expressive power of their single-layer counterpart.
2 code implementations • 26 Mar 2021 • Minghao Liu, Shengqi Ren, Siyuan Ma, Jiahui Jiao, Yizhou Chen, Zhiguang Wang, Wei Song
In this work, we explored a simple extension of the current Transformer Networks with gating, named Gated Transformer Networks (GTN) for the multivariate time series classification problem.
no code implementations • 1 Jan 2021 • Yizhou Chen, Dong Li, Na Li, TONG LIANG, Shizhuo Zhang, Bryan Kian Hsiang Low
This paper presents a novel implicit process-based meta-learning (IPML) algorithm that, in contrast to existing works, explicitly represents each task as a continuous latent vector and models its probabilistic belief within the highly expressive IP framework.
no code implementations • ICML 2020 • Zhongxiang Dai, Yizhou Chen, Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho
This paper presents a recursive reasoning formalism of Bayesian optimization (BO) to model the reasoning process in the interactions between boundedly rational, self-interested agents with unknown, complex, and costly-to-evaluate payoff functions in repeated games, which we call Recursive Reasoning-Based BO (R2-B2).
1 code implementation • NeurIPS 2019 • Haibin Yu, Yizhou Chen, Zhongxiang Dai, Kian Hsiang Low, Patrick Jaillet
This paper presents an implicit posterior variational inference (IPVI) framework for DGPs that can ideally recover an unbiased posterior belief and still preserve time efficiency.