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arXiv 2024年

作者： Ni, Xuanfan Li, Piji College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China

Recent efforts have evaluated large language models (LLMs) in areas such as commonsense reasoning, mathematical reasoning, and code generation. However, to the best of our knowledge, no work has specifically investigated the performance of LLMs in natural language generation (NLG) tasks, a pivotal criterion for determining model excellence. Thus, this paper conducts a comprehensive evaluation of well-known and high-performing LLMs, namely ChatGPT, ChatGLM, T5-based models, LLaMA-based models, and Pythia-based models, in the context of NLG tasks. We select English and Chinese datasets encompassing Dialogue Generation and Text Summarization. Moreover, we propose a common evaluation setting that incorporates input templates and post-processing strategies. Our study reports both automatic results, accompanied by a detailed analysis. © 2024, CC BY.

关键词： Natural language processing systems

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An Improved Algorithm for Spiking Neural Networks with Multi-Scale Attention Coding

An Improved Algorithm for Spiking Neural Networks with Multi...

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Cyber-Physical Social intelligence (ICCSI), International Conference on

作者： Sisi Chen Xiaofeng Chen Weikai Li Department of Mathematics Chongqing Jiaotong University Chongqing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

Spiking Neural Networks (SNNs), driven by spike-based mechanisms, are known for their high efficiency and low energy consumption, which makes them ideal for applications like image classification, object detection, and medical diagnostics. However, challenges include training difficulties due to the non-differentiability of spiking activities and limitations in encoding schemes, which struggle to capture complex spatio-temporal dynamics. In this paper, we propose a novel algorithm, termed the Spatio-Temporal Backpropagation algorithm with Multi-Scale Attention Coding (STBP-MSC). STBP-MSC dynamically allocates attention across spatial and temporal domains, which improves coding efficiency and enhances the classification accuracy of SNNs. Furthermore, STBP-MSC incorporates the Complementary Leaky Integrate-and-Fire (CLIF) neuron model, which enhances temporal gradient computation. Experimental results on the MNIST and Fashion-MNIST datasets demonstrate that STBP-MSC achieves high coding efficiency and superior performance within minimal time windows.

关键词： Training Visualization Heuristic algorithms Computational modeling Neurons Spiking neural networks Backpropagation algorithms Encoding Classification algorithms Medical diagnosis

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A comprehensive perspective of contrastive self-supervised learning

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Frontiers of Computer Science 2021年第4期15卷 1-3页

作者： Songcan CHEN Chuanxing GENG College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing 211106China

Self-supervised learning(SSL),as a new unsupervised representation learning paradigm in machine learning,recently has received extensive attention,which is also regarded as the future of machine learning by the Turing Award winner,LeCunn[1].SSL learns representation from unlabeled data using"pretext"tasks that provide free supervision,with the aim of performing well on semantic relation agnostic downstream(supervised)*** usually divides two stages:first learning as general/invariant representation/feature as possible with the auto-annotation pretext tasks(the core),then transferring the learned knowledge to downstream tasks(the ultimate goal)[2].

关键词： SSL representation Turing

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TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series analysis

arXiv

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arXiv 2024年

作者： Liu, Jiexi Cao, Meng Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China

Irregularly sampled multivariate time series (ISMTS) are prevalent in reality. Due to their non-uniform intervals between successive observations and varying sampling rates among series, the channel-independent (CI) strategy, which has been demonstrated more desirable for complete multivariate time series forecasting in recent studies, has failed. This failure can be further attributed to the sampling sparsity, which provides insufficient information for effective CI learning, thereby reducing its capacity. When we resort to the channel-dependent (CD) strategy, even higher capacity cannot mitigate the potential loss of diversity in learning similar embedding patterns across different channels. We find that existing work considers CI and CD strategies to be mutually exclusive, primarily because they apply these strategies to the global channel. However, we hold the view that channel strategies do not necessarily have to be used globally. Instead, by appropriately applying them locally and globally, we can create an opportunity to take full advantage of both strategies. This leads us to introduce the Channel Harmony ISMTS Transformer (TimeCHEAT), which utilizes the CD strategy locally and the CI strategy globally. Specifically, we segment the ISMTS into sub-series level patches. Locally, the CD strategy aggregates information within each patch for time embedding learning, maximizing the use of relevant observations while reducing long-range irrelevant interference. Here, we enhance generality by transforming embedding learning into an edge weight prediction task using bipartite graphs, eliminating the need for special prior knowledge. Globally, the CI strategy is applied across patches, allowing the Transformer to learn individualized attention patterns for each channel. Experimental results indicate our proposed TimeCHEAT demonstrates competitive state-of-the-art performance across three mainstream tasks including classification, forecasting and interpolation. Copyri

关键词： Time series analysis

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A Double Regularization Loss Based on Long-tailed Noisy Labels

A Double Regularization Loss Based on Long-tailed Noisy Labe...

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Lei Wang Shaoyuan Li MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China

Extensive research has been conducted in recent years to solve the long-tailed distribution and achieved excellent results. However, in contrast to well-designed data, datasets with label noise are common in the real world, even in long-tailed datasets. Then, loss functions that rely on prior knowledge of correct labels for long-tailed distributions will fail. To solve the above problems, the robustness of different loss functions in long-tailed data containing noise is first analyzed. Then algorithmic improvements are made to the LDAM loss, which is focused on dealing with long-tailed problems. We propose a robust loss function DRL, which solves the noisy long-tailed problem with two regularisation terms: label regularisation and sample regularisation. Experiments on several datasets validate the effectiveness of the proposed loss function.

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All Beings Are Equal in Open Set Recognition

arXiv

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arXiv 2024年

作者： Li, Chaohua Zhang, Enhao Geng, Chuanxing Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China

In open-set recognition (OSR), a promising strategy is exploiting pseudo-unknown data outside given K known classes as an additional K+1-th class to explicitly model potential open space. However, treating unknown classes without distinction is unequal for them relative to known classes due to the category-agnostic and scale-agnostic of the unknowns. This inevitably not only disrupts the inherent distributions of unknown classes but also incurs both class-wise and instance-wise imbalances between known and unknown classes. Ideally, the OSR problem should model the whole class space as K+∞, but enumerating all unknowns is impractical. Since the core of OSR is to effectively model the boundaries of known classes, this means just focusing on the unknowns nearing the boundaries of targeted known classes seems sufficient. Thus, as a compromise, we convert the open classes from infinite to K, with a novel concept Target-Aware Universum (TAU) and propose a simple yet effective framework Dual Contrastive Learning with Target-Aware Universum (DCTAU). In details, guided by the targeted known classes, TAU automatically expands the unknown classes from the previous 1 to K, effectively alleviating the distribution disruption and the imbalance issues mentioned above. Then, a novel Dual Contrastive (DC) loss is designed, where all instances irrespective of known or TAU are considered as positives to contrast with their respective negatives. Experimental results indicate DCTAU sets a new state-of-the-art. Copyright © 2024, The Authors. All rights reserved.

关键词： machine learning

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Class-aware Learning for Imbalanced Multi-Label Classification

Class-aware Learning for Imbalanced Multi-Label Classificati...

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Jiayao Chen Shaoyuan Li MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China

Imbalanced multi-label image classification has gained increasing attention recently, in which each sample has multiple class labels, but the number of each category is unevenly distributed. It’s common in practical applications but traditional multi-label learning methods can hardly deal with imbalance problems. In this paper, we propose an effective method to tackle imbalanced multi-label learning. The class-aware embedding network is proposed to learn robust class-based representation. Additionally, by using the distribution-balanced loss to weigh different samples, our model can improve the feature learning ability of minority classes. Extensive experiments on widely used long-tailed manual multi-label datasets like VOC-LT and COCO-LT explicitly validate the proposed good method.

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Tumor Micro-Environment Interactions Guided Graph Learning for Survival analysis of Human Cancers from Whole-Slide Pathological Images

Tumor Micro-Environment Interactions Guided Graph Learning f...

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Conference on Computer Vision and pattern Recognition (CVPR)

作者： Wei Shao YangYang Shi Daoqiang Zhang JunJie Zhou Peng Wan Ministry of Education College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Brain-Machine Intelligence Technology Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The recent advance of deep learning technology brings the possibility of assisting the pathologist to predict the patients' survival from whole-slide pathological images (WSIs). However, most of the prevalent methods only worked on the sampled patches in specifically or randomly selected tumor areas of WSIs, which has very limited capability to capture the complex interactions between tumor and its surrounding micro-environment components. As a matter of fact, tumor is supported and nurtured in the heterogeneous tumor micro-environment(TME), and the detailed analysis of TME and their correlation with tumors are important to in-depth analyze the mechanism of cancer development. In this paper, we considered the spatial interactions among tumor and its two major TME components (i.e., lymphocytes and stromal fibrosis) and presented a Tumor Micro-environment Interactions Guided Graph Learning (TMEGL) algorithm for the prognosis prediction of human cancers. Specifically, we firstly selected different types of patches as nodes to build graph for each WSI. Then, a novel TME neighborhood organization guided graph embedding algorithm was proposed to learn node representations that can preserve their topological structure information. Finally, a Gated Graph Attention Network is applied to capture the survival-associated intersections among tumor and different TME components for clinical outcome prediction. We tested TMEGL on three cancer cohorts derived from The Cancer Genome Atlas (TCGA), and the experimental results indicated that TMEGL not only outperforms the existing WSI-based survival analysis models, but also has good explainable ability for survival prediction.

关键词： Pathology Analytical models Organizations Predictive models Semisupervised learning Prediction algorithms pattern recognition

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MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled Multivariate Time Series analysis

arXiv

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arXiv 2024年

作者： Liu, Jiexi Cao, Meng Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautic China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China

Irregularly sampled multivariate time series (ISMTS) are prevalent in reality. Most existing methods treat ISMTS as synchronized regularly sampled time series with missing values, neglecting that the irregularities are primarily attributed to variations in sampling rates. In this paper, we introduce a novel perspective that irregularity is essentially relative in some senses. With sampling rates artificially determined from low to high, an irregularly sampled time series can be transformed into a hierarchical set of relatively regular time series from coarse to fine. We observe that additional coarse-grained relatively regular series not only mitigate the irregularly sampled challenges to some extent but also incorporate broad-view temporal information, thereby serving as a valuable asset for representation learning. Therefore, following the philosophy of learning that Seeing the big picture first, then delving into the details, we present the Multi-Scale and Multi-Correlation Attention Network (MuSiCNet) combining multiple scales to iteratively refine the ISMTS representation. Specifically, within each scale, we explore time attention and frequency correlation matrices to aggregate intra- and inter-series information, naturally enhancing the representation quality with richer and more intrinsic details. While across adjacent scales, we employ a representation rectification method containing contrastive learning and reconstruction results adjustment to further improve representation consistency. MuSiCNet is an ISMTS analysis framework that competitive with SOTA in three mainstream tasks consistently, including classification, interpolation, and forecasting. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Faster Double Adaptive Gradient Methods 39

Faster Double Adaptive Gradient Methods

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39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Huang, Feihu Luo, Yuning College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

ISBN: (纸本)157735897X

In this paper, we propose a class of faster double adaptive gradient methods to solve nonconvex finite-sum optimization problems possibly with nonsmooth regularization by simultaneously using adaptive learning rate and adaptive mini-batch size. Specifically, we first propose a double adaptive stochastic gradient method (i.e., 2AdaSGD), and prove that our 2AdaSGD obtains a low stochastic first-order oracle (SFO) complexity for finding a stationary solution under the population smoothness condition. Furthermore, we propose a variance reduced double adaptive stochastic gradient method (i.e., 2AdaSPIDER), and prove that our 2AdaSPIDER obtains an optimal SFO complexity under the average smoothness condition, which is lower than the SFO complexity of the existing double adaptive gradient algorithms. In particular, we introduce a new stochastic gradient mapping to adaptively adjust mini-batch size in our stochastic gradient methods. We conduct some numerical experiments to verify efficiency of our proposed methods. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Numerical methods

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