Author name cluster

Yaowu Chen

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5 papers

2 author rows

NeurIPS Conference 2024 Conference Paper

Enhancing LLM’s Cognition via Structurization

Kai Liu
Zhihang Fu
Chao Chen
Wei Zhang
Rongxin Jiang
Fan Zhou
Yaowu Chen
Yue Wu

When reading long-form text, human cognition is complex and structurized. While large language models (LLMs) process input contexts through a causal and sequential perspective, this approach can potentially limit their ability to handle intricate and complex inputs effectively. To enhance LLM’s cognition capability, this paper presents a novel concept of context structurization. Specifically, we transform the plain, unordered contextual sentences into well-ordered and hierarchically structurized elements. By doing so, LLMs can better grasp intricate and extended contexts through precise attention and information-seeking along the organized structures. Extensive evaluations are conducted across various model architectures and sizes (including a series of auto-regressive LLMs as well as BERT-like masking models) on a diverse set of NLP tasks (e. g. , context-based question-answering, exhaustive hallucination evaluation, and passage-level dense retrieval). Empirical results show consistent and significant performance gains afforded by a single-round structurization. In particular, we boost the open-sourced LLaMA2-70B model to achieve comparable performance against GPT-3. 5-Turbo as the halluci- nation evaluator. Besides, we show the feasibility of distilling advanced LLMs’ language processing abilities to a smaller yet effective StruXGPT-7B to execute structurization, addressing the practicality of our approach. Code is available at https: //github. com/alibaba/struxgpt.

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NeurIPS Conference 2024 Conference Paper

Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution

Kai Liu
Zhihang Fu
Sheng Jin
Chao Chen
Ze Chen
Rongxin Jiang
Fan Zhou
Yaowu Chen

Detecting and rejecting unknown out-of-distribution (OOD) samples is critical for deployed neural networks to void unreliable predictions. In real-world scenarios, however, the efficacy of existing OOD detection methods is often impeded by the inherent imbalance of in-distribution (ID) data, which causes significant performance decline. Through statistical observations, we have identified two common challenges faced by different OOD detectors: misidentifying tail class ID samples as OOD, while erroneously predicting OOD samples as head class from ID. To explain this phenomenon, we introduce a generalized statistical framework, termed ImOOD, to formulate the OOD detection problem on imbalanced data distribution. Consequently, the theoretical analysis reveals that there exists a class-aware bias item between balanced and imbalanced OOD detection, which contributes to the performance gap. Building upon this finding, we present a unified training-time regularization technique to mitigate the bias and boost imbalanced OOD detectors across architecture designs. Our theoretically grounded method translates into consistent improvements on the representative CIFAR10-LT, CIFAR100-LT, and ImageNet-LT benchmarks against several state-of-the-art OOD detection ap- proaches. Code is available at https: //github. com/alibaba/imood.

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NeurIPS Conference 2022 Conference Paper

Boosting Out-of-distribution Detection with Typical Features

Yao Zhu
Yuefeng Chen
Chuanlong Xie
Xiaodan Li
Rong Zhang
Hui Xue'
Xiang Tian
Bolun Zheng

Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD detection methods that focus on designing OOD scores or introducing diverse outlier examples to retrain the model, we delve into the obstacle factors in OOD detection from the perspective of typicality and regard the feature's high-probability region of the deep model as the feature's typical set. We propose to rectify the feature into its typical set and calculate the OOD score with the typical features to achieve reliable uncertainty estimation. The feature rectification can be conducted as a plug-and-play module with various OOD scores. We evaluate the superiority of our method on both the commonly used benchmark (CIFAR) and the more challenging high-resolution benchmark with large label space (ImageNet). Notably, our approach outperforms state-of-the-art methods by up to 5. 11% in the average FPR95 on the ImageNet benchmark.

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JBHI Journal 2019 Journal Article

Identifying Brain Networks at Multiple Time Scales via Deep Recurrent Neural Network

Yan Cui
Shijie Zhao
Han Wang
Li Xie
Yaowu Chen
Junwei Han
Lei Guo
Fan Zhou

For decades, task functional magnetic resonance imaging has been a powerful noninvasive tool to explore the organizational architecture of human brain function. Researchers have developed a variety of brain network analysis methods for task fMRI data, including the general linear model, independent component analysis, and sparse representation methods. However, these shallow models are limited in faithful reconstruction and modeling of the hierarchical and temporal structures of brain networks, as demonstrated in more and more studies. Recently, recurrent neural networks (RNNs) exhibit great ability of modeling hierarchical and temporal dependence features in the machine learning field, which might be suitable for task fMRI data modeling. To explore such possible advantages of RNNs for task fMRI data, we propose a novel framework of a deep recurrent neural network (DRNN) to model the functional brain networks from task fMRI data. Experimental results on the motor task fMRI data of Human Connectome Project 900 subjects release demonstrated that the proposed DRNN can not only faithfully reconstruct functional brain networks, but also identify more meaningful brain networks with multiple time scales which are overlooked by traditional shallow models. In general, this work provides an effective and powerful approach to identifying functional brain networks at multiple time scales from task fMRI data.

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ICRA Conference 2017 Conference Paper

Illumination insensitive efficient second-order minimization for planar object tracking

Lin Chen 0030
Fan Zhou 0007
Yu Shen
Xiang Tian 0002
Haibin Ling
Yaowu Chen

Tracking for planar objects is an important issue to vision-based robotic applications. In direct visual tracking (DVT) methods, the similarity between two images is often measured through the sum of squared differences (SSD) especially with the efficient second-order minimization (ESM) due to its simplicity and efficiency. However, SSD-based ESM is not robust to illumination changes since it is usually built upon the brightness constancy assumption. Contrast to image brightness, gradient orientations (GO) are invariant to both linear and non-linear illumination changes as verified in practice. Based on GO, we propose an illumination insensitive ESM method for planar object tracking in this paper. In order to introduce GO into the ESM, we generalized the original ESM formulas for multi-dimensional features. In addition, a denoising method based on the Perona-Malik function and a mask image were suggested to improve GO's robustness against image noise and low texture. Our experimental results on dataset for planar objects with illumination changes and a benchmark dataset confirm the proposed method is robust to illumination variations and capable to deal with the general tracking challenges.

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