Author name cluster

Hang Gao

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23 papers

2 author rows

AAAI Conference 2026 Conference Paper

Learning Whom to Align With: Progressive Anomaly Combination Detection for Partially View-Aligned Clustering

Hang Gao
Zuosong Cai
Yuze Li
Cheng Liu
Gaoyang Li
Ying Li
Wei Du
You Zhou

Partially View-aligned Clustering (PVC) addresses the challenge of partial view alignment in multi-view learning by leveraging complementary and consistent information. While existing PVC methods show promise, most rely on distance-based strategies that are sensitive to view-specific details and noise, limiting their robustness. In this work, we propose a novel view alignment strategy that reformulates the alignment task as an anomaly detection problem. Rather than learning a view-alignment matrix that enforces strict one-to-one correspondences across views, we adopt a progressive approach to identify well-aligned samples. Specifically, we sample subsets of data by generating random view combinations from unaligned samples and propose an anomaly combination detection module to evaluate the alignment consistency of these combinations. In addition, our progressive training framework alternates between updating model parameters and selecting high-confidence view combinations for subsequent optimization. By reformulating view alignment as an anomaly detection task, our approach provides a more robust and effective solution to partial view alignment. Experiments on benchmark datasets demonstrate that our method outperforms state-of-the-art approaches in the PVC problem.

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NeurIPS Conference 2025 Conference Paper

A-Mem: Agentic Memory for LLM Agents

Wujiang Xu
Zujie Liang
Kai Mei
Hang Gao
Juntao Tan
Yongfeng Zhang

While large language model (LLM) agents can effectively use external tools for complex real-world tasks, they require memory systems to leverage historical experiences. Current memory systems enable basic storage and retrieval but lack sophisticated memory organization, despite recent attempts to incorporate graph databases. Moreover, these systems' fixed operations and structures limit their adaptability across diverse tasks. To address this limitation, this paper proposes a novel agentic memory system for LLM agents that can dynamically organize memories in an agentic way. Following the basic principles of the Zettelkasten method, we designed our memory system to create interconnected knowledge networks through dynamic indexing and linking. When a new memory is added, we generate a comprehensive note containing multiple structured attributes, including contextual descriptions, keywords, and tags. The system then analyzes historical memories to identify relevant connections, establishing links where meaningful similarities exist. Additionally, this process enables memory evolution -- as new memories are integrated, they can trigger updates to the contextual representations and attributes of existing historical memories, allowing the memory network to continuously refine its understanding. Our approach combines the structured organization principles of Zettelkasten with the flexibility of agent-driven decision making, allowing for more adaptive and context-aware memory management. Empirical experiments on six foundation models show superior improvement against existing SOTA baselines. The code is available at \url{https: //anonymous. 4open. science/r/AgenticMemory-76B4}.

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AAAI Conference 2025 Conference Paper

Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach

Hang Gao
Chenhao Zhang
Fengge Wu
Changwen Zheng
Junsuo Zhao
Huaping Liu

Graph representation learning methods are highly effective in handling complex non-Euclidean data by capturing intricate relationships and features within graph structures. However, traditional methods face challenges when dealing with heterogeneous graphs that contain various types of nodes and edges due to the diverse sources and complex nature of the data. Existing heterogeneous graph neural networks (HGNNs) have shown promising results but require prior knowledge of node and edge types and unified node feature formats, which limits their applicability. Recent advancements in graph representation learning using large language models (LLMs) offer new solutions by integrating LLMs' data processing capabilities, enabling the alignment of various graph representations. Nevertheless, these methods often overlook heterogeneous graph data and require extensive preprocessing. To address these limitations, we propose an LLM-enhanced Heterogeneous Graph Neural Network (LHGNN). LHGNN leverages the strengths of both LLM and GNN, allowing for the processing of graph data with any format and type of nodes and edges without the need for type information or special preprocessing. LHGNN employs LLM to automatically summarize and classify different data formats and types, aligns node features, and uses a specialized GNN for targeted learning, thus obtaining effective graph representations for downstream tasks. Theoretical analysis and experimental validation have demonstrated the effectiveness of our method.

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NeurIPS Conference 2025 Conference Paper

Cameras as Relative Positional Encoding

Ruilong Li
Brent Yi
Junchen Liu
Hang Gao
Yi Ma
Angjoo Kanazawa

Transformers are increasingly prevalent for multiview computer vision tasks, where geometric relationships between viewpoints are critical for 3D perception. To leverage these relationships, multiview transformers must use camera geometry to ground visual tokens in 3D space. In this work, we compare techniques for conditioning transformers on cameras: token-level raymap encodings, attention-level relative pose encodings, and a new relative encoding we propose—Projective Positional Encoding (PRoPE)—that captures complete camera frustums, both intrinsics and extrinsics, as a relative positional encoding. Our experiments begin by showing how relative conditioning methods improve performance in feedforward novel view synthesis, with further gains from PRoPE. This holds across settings: scenes with both shared and varying intrinsics, when combining token- and attention-level conditioning, and for generalization to inputs with out-of-distribution sequence lengths and camera intrinsics. We then verify that these benefits persist for different tasks, stereo depth estimation and discriminative spatial cognition, as well as larger model sizes.

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AAAI Conference 2025 Conference Paper

Contrastive Auxiliary Learning with Structure Transformation for Heterogeneous Graphs

Wei Du
Hongmin Sun
Hang Gao
Gaoyang Li
Ying Li

In recent years, methods based on heterogeneous graph neural networks (HGNNs) have been widely used for embedding heterogeneous graphs (HGs) due to their ability to effectively encode the rich information from HGs into low-dimensional node embeddings. Existing HGNNs focus on neighbor aggregation and semantic fusion while neglecting the HG structure and learning paradigms. However, the original HG data might lack node features, which existing models may not effectively account for. Additionally, exclusively relying on a single supervised learning approach may only partially leverage the invariant information in graph data. To address these challenges, we introduce the Contrastive Auxiliary Learning Model for Heterogeneous Graphs (CALHG). This model combines edge perturbation and graph diffusion to enhance graph data, allowing it to capture the inherent structural information within heterogeneous graphs fully. Additionally, we employ a category-guided multi-view contrastive learning approach, which does not rely on positive and negative samples for model training, enabling us to capture the intrinsic invariances in heterogeneous graph data. Extensive experiments and analyses on five benchmark datasets without node features and three benchmark datasets with node features demonstrate the effectiveness and efficiency of our novel method compared with several state-of-the-art methods.

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JBHI Journal 2025 Journal Article

Deep Self-Reinforced Multi-View Subspace Clustering for Cancer Subtyping

Cheng Liu
Baoyuan Zheng
Jiaojiao Wang
Xibiao Wang
Hang Gao
Fei Wang
Wenjun Shen
Si Wu

Identifying cancer subtypes is crucial for understanding disease progression and guiding precision medicine. With advances in high-throughput experimental technologies, the integration of multiple types of omics data for cancer subtype identification has become increasingly feasible. However, despite the promising performance of existing integrative cancer subtyping methods, efficiently integrating and clustering multi-omics datasets remains challenging due to the high levels of noise inherent in omics data, which impede the accurate characterization of relationships among samples. To address these challenges, we propose a novel deep multi-view subspace clustering model that incorporates a self-reinforced learning strategy. This strategy iteratively improves the quality of self-representation, which is critical for accurately capturing sample relationships and enabling effective clustering. Specifically, during model training, the proposed method learns a highly reliable self-representation through a good-neighbor learning mechanism, allowing it to model more accurate and robust inter-sample relationships. Building upon this reliable self-representation, we further develop a learnable view-graph fusion framework that integrates complementary information across multiple omics views to derive a consensus representation for clustering, thereby guiding the overall learning process. In addition, we introduce a local graph-guided learning mechanism based on an initial graph constructed from the raw data. This mechanism serves as an effective regularization strategy to prevent the model from converging to suboptimal solutions, thereby enhancing stability and robustness during training. Extensive experimental results demonstrate that the proposed method consistently outperforms several state-of-the-art approaches, validating its effectiveness and robustness for cancer subtype identification.

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IROS Conference 2025 Conference Paper

Design of a swimming microrobot powered by a single piezoelectric bender

Cameron Urban
Tyler King
Rafael Gottlieb
Hang Gao
E. Farrell Helbling

Countless underwater robots seek to monitor aquatic environments while minimizing their impact on fragile ecosystems. At mm-scales, these systems can be used in a range of waterways, from shallow streams and rivers, to larger ponds and lakes, and navigate around large obstacles or through tight spaces in coral reefs, mangroves, or pipe systems. They can also be more readily used as platforms for biological study, as small-scale robots can more easily be integrated into bench-top characterization systems to verify hydrodynamic performance. Here, we present a new robotic platform, the Daniobot, a 16. 5mm body length (BL) microrobotic fish that is capable of achieving top speeds of 2. 84BLs −1. At 23. 8mm total length (TL), Daniobot is, to the best of our knowledge, the smallest fish-inspired robot propelled by onboard actuators. We present the design, fabrication, and assembly of this robot as well as detailed position and velocity results at varying tail amplitudes and frequencies, and compare their trends to a simple analytical model. This design uses a single PZT bimorph actuator operating at 175V, enabling future untethered experiments.

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IJCAI Conference 2025 Conference Paper

Learn to Think: Bootstrapping LLM Logic Through Graph Representation Learning

Hang Gao
Chenhao Zhang
Tie Wang
Junsuo Zhao
Fengge Wu
Changwen Zheng
Huaping Liu

Large Language Models (LLMs) have achieved remarkable success across various domains. However, they still face significant challenges, including high computational costs for training and limitations in solving complex reasoning problems. Although existing methods have extended the reasoning capabilities of LLMs through structured paradigms, these approaches often rely on task-specific prompts and predefined reasoning processes, which constrain their flexibility and generalizability. To address these limitations, we propose a novel framework that leverages graph learning to enable more flexible and adaptive reasoning capabilities for LLMs. Specifically, this approach models the reasoning process of a problem as a graph and employs LLM-based graph learning to guide the adaptive generation of each reasoning step. To further enhance the adaptability of the model, we introduce a Graph Neural Network (GNN) module to perform representation learning on the generated reasoning process, enabling real-time adjustments to both the model and the prompt. Experimental results demonstrate that this method significantly improves reasoning performance across multiple tasks without requiring additional training or task-specific prompt design. Code can be found in https: //github. com/zch65458525/L2T.

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AAAI Conference 2024 Conference Paper

Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning

Jiangmeng Li
Yifan Jin
Hang Gao
Wenwen Qiang
Changwen Zheng
Fuchun Sun

Graph contrastive learning (GCL) aims to align the positive features while differentiating the negative features in the latent space by minimizing a pair-wise contrastive loss. As the embodiment of an outstanding discriminative unsupervised graph representation learning approach, GCL achieves impressive successes in various graph benchmarks. However, such an approach falls short of recognizing the topology isomorphism of graphs, resulting in that graphs with relatively homogeneous node features cannot be sufficiently discriminated. By revisiting classic graph topology recognition works, we disclose that the corresponding expertise intuitively complements GCL methods. To this end, we propose a novel hierarchical topology isomorphism expertise embedded graph contrastive learning, which introduces knowledge distillations to empower GCL models to learn the hierarchical topology isomorphism expertise, including the graph-tier and subgraph-tier. On top of this, the proposed method holds the feature of plug-and-play, and we empirically demonstrate that the proposed method is universal to multiple state-of-the-art GCL models. The solid theoretical analyses are further provided to prove that compared with conventional GCL methods, our method acquires the tighter upper bound of Bayes classification error. We conduct extensive experiments on real-world benchmarks to exhibit the performance superiority of our method over candidate GCL methods, e.g., for the real-world graph representation learning experiments, the proposed method beats the state-of-the-art method by 0.23% on unsupervised representation learning setting, 0.43% on transfer learning setting. Our code is available at https://github.com/jyf123/HTML.

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ICRA Conference 2024 Conference Paper

High-speed interfacial flight of an insect-scale robot

Hang Gao
Sunghwan Jung
E. Farrell Helbling

Several insect species are able to locomote across the air-water interface by leveraging surface tension to remain above the water surface. A subset of these insects, such as the stonefly and waterlily beetle, flap their wings to actively move around the two dimensional surface — a locomotion strategy referred to as interfacial flight. Here, we present an insect-scale robot, the γ-bot, inspired by these interfacial fliers. The robot is comprised of a flapping-wing vehicle that generates a thrust force parallel to the water surface, and three passive legs utilize surface tension to support the body mass and maintain contact with the air-water interface. We developed and validated a simple model to characterize the drag forces acting on the vehicle and estimate the robot’s velocity. This 112 mg robot can reach maximum velocities of 0. 9 ms −1 (corresponding to 15 BLs −1 ) and can initiate both left and right turns, demonstrating high maneuverability along the air-water interface. In addition, the robot can carry an additional 419 mg, enabling future sensing, control, and power autonomous operation.

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EAAI Journal 2024 Journal Article

Improving real-time driver distraction detection via constrained attention mechanism

Hang Gao
Yi Liu

Real-time driving distraction detection has garnered significant attention due to its potential to build various driving safety protections such as distraction warnings and driver assistance systems. Recent studies have focused on the development of neural networks for vision-based detection, though achieving a balance between performance and efficiency has proven challenging. In this paper, we propose a novel constrained attention (CA) mechanism for real-time driver distraction detection, which aims to achieve better performance meanwhile ensuring the computation efficiency. Specifically, we conduct some case studies by generating class activation maps to check the model attention, and three potential factors affecting performance are mined, which are ambiguous attention signal, excessive attention region, and similar attention between different classes. Two regularization terms are designed to optimize the three obstacles. Firstly, a concentrative regularization is introduced to limit the size of the attention region, meanwhile, pixels in the region have clear attention degrees. Secondly, an orthogonal regularization is proposed to optimize the attention of different classes to be discriminative. To further inspiring the model, we design an intersample constraint, which optimizes the attention of images with the same ground truth to be similar. Experiments are conducted on two driver distraction detection datasets, and the experimental results showed that our CA mechanism can bring significant performance improvement. More importantly, there will be no additional computational burden when the trained model is deployed in actual scenarios. Codes are released at https: //github. com/gaohangcodes/CAN4DDD.

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AAAI Conference 2024 Conference Paper

Rethinking Causal Relationships Learning in Graph Neural Networks

Hang Gao
Chengyu Yao
Jiangmeng Li
Lingyu Si
Yifan Jin
Fengge Wu
Changwen Zheng
Huaping Liu

Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustness of GNNs, it becomes exceptionally crucial to bolster their ability to capture causal relationships. However, despite recent advancements that have indeed strengthened GNNs from a causal learning perspective, conducting an in-depth analysis specifically targeting the causal modeling prowess of GNNs remains an unresolved issue. In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels. The rationality of the generated data is further ensured through theoretical foundations. Drawing insights from analyses conducted using our dataset, we introduce a lightweight and highly adaptable GNN module designed to strengthen GNNs' causal learning capabilities across a diverse range of tasks. Through a series of experiments conducted on both synthetic datasets and other real-world datasets, we empirically validate the effectiveness of the proposed module. The codes are available at https://github.com/yaoyao-yaoyao-cell/CRCG.

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IROS Conference 2023 Conference Paper

Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation

Zhen Liu 0054
Hang Gao
Hao Ma
Shuo Cai
Yunfeng Hu 0003
Ting Qu 0001
Hong Chen 0003
Xun Gong 0007

Autonomous vehicle (AV) evaluation has been the subject of increased interest in recent years both in industry and in academia. This paper focuses on the development of a novel framework for generating adversarial driving behavior of background vehicle interfering against the AV to expose effective and rational risky events. Specifically, the adversarial behavior is learned by a reinforcement learning (RL) approach incorporated with the cumulative prospect theory (CPT) which allows representation of human risk cognition. Then, the extended version of deep deterministic policy gradient (DDPG) technique is proposed for training the adversarial policy while ensuring training stability as the CPT action-value function is leveraged. A comparative case study regarding the cut-in scenario is conducted on a high fidelity Hardware-in-the-Loop (HiL) platform and the results demonstrate the adversarial effectiveness to infer the weakness of the tested AV.

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AAAI Conference 2023 Conference Paper

Robust Causal Graph Representation Learning against Confounding Effects

Hang Gao
Jiangmeng Li
Wenwen Qiang
Lingyu Si
Bing Xu
Changwen Zheng
Fuchun Sun

The prevailing graph neural network models have achieved significant progress in graph representation learning. However, in this paper, we uncover an ever-overlooked phenomenon: the pre-trained graph representation learning model tested with full graphs underperforms the model tested with well-pruned graphs. This observation reveals that there exist confounders in graphs, which may interfere with the model learning semantic information, and current graph representation learning methods have not eliminated their influence. To tackle this issue, we propose Robust Causal Graph Representation Learning (RCGRL) to learn robust graph representations against confounding effects. RCGRL introduces an active approach to generate instrumental variables under unconditional moment restrictions, which empowers the graph representation learning model to eliminate confounders, thereby capturing discriminative information that is causally related to downstream predictions. We offer theorems and proofs to guarantee the theoretical effectiveness of the proposed approach. Empirically, we conduct extensive experiments on a synthetic dataset and multiple benchmark datasets. Experimental results demonstrate the effectiveness and generalization ability of RCGRL. Our codes are available at https://github.com/hang53/RCGRL.

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IROS Conference 2022 Conference Paper

A compliant thorax design for robustness and elastic energy exchange in flapping-wing robots

Hang Gao
James Lynch
Nick Gravish

Flapping wing insects benefit from a compliant thorax that provides elastic energy exchange and resiliency to wing collisions. In this paper, we present a flapping wing robot that uses an underactuated compliant transmission inspired by the insect thorax. We developed a novel fabrication method that combines carbon fiber (CF) laminate and soft robotics fabrication techniques for transmission construction. The transmission design is optimized to achieve desired wingstroke requirements and to allow for independent motion of each wing. We validate these design choices in bench-top tests measuring transmission compliance and kinematics. We integrate the transmission with laminate wings and two types of actuation, demonstrating elastic energy exchange and limited lift-off capabilities Lastly, we tested collision mitigation through flapping wing experiments that obstructed the motion of a wing. These experiments demonstrate that an underactuated compliant, transmission can provide resilience and robustness to flapping wing robots.

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IJCAI Conference 2022 Conference Paper

Bootstrapping Informative Graph Augmentation via A Meta Learning Approach

Hang Gao
Jiangmeng Li
Wenwen Qiang
Lingyu Si
Fuchun Sun
Changwen Zheng

Recent works explore learning graph representations in a self-supervised manner. In graph contrastive learning, benchmark methods apply various graph augmentation approaches. However, most of the augmentation methods are non-learnable, which causes the issue of generating unbeneficial augmented graphs. Such augmentation may degenerate the representation ability of graph contrastive learning methods. Therefore, we motivate our method to generate augmented graph with a learnable graph augmenter, called MEta Graph Augmentation (MEGA). We then clarify that a "good" graph augmentation must have uniformity at the instance-level and informativeness at the feature-level. To this end, we propose a novel approach to learning a graph augmenter that can generate an augmentation with uniformity and informativeness. The objective of the graph augmenter is to promote our feature extraction network to learn a more discriminative feature representation, which motivates us to propose a meta-learning paradigm. Empirically, the experiments across multiple benchmark datasets demonstrate that MEGA outperforms the state-of-the-art methods in graph self-supervised learning tasks. Further experimental studies prove the effectiveness of different terms of MEGA. Our codes are available at https: //github. com/hang53/MEGA.

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NeurIPS Conference 2022 Conference Paper

Monocular Dynamic View Synthesis: A Reality Check

Hang Gao
Ruilong Li
Shubham Tulsiani
Bryan Russell
Angjoo Kanazawa

We study the recent progress on dynamic view synthesis (DVS) from monocular video. Though existing approaches have demonstrated impressive results, we show a discrepancy between the practical capture process and the existing experimental protocols, which effectively leaks in multi-view signals during training. We define effective multi-view factors (EMFs) to quantify the amount of multi-view signal present in the input capture sequence based on the relative camera-scene motion. We introduce two new metrics: co-visibility masked image metrics and correspondence accuracy, which overcome the issue in existing protocols. We also propose a new iPhone dataset that includes more diverse real-life deformation sequences. Using our proposed experimental protocol, we show that the state-of-the-art approaches observe a 1-2 dB drop in masked PSNR in the absence of multi-view cues and 4-5 dB drop when modeling complex motion. Code and data can be found at http: //hangg7. com/dycheck.

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NeurIPS Conference 2021 Conference Paper

Learning with Holographic Reduced Representations

Ashwinkumar Ganesan
Hang Gao
Sunil Gandhi
Edward Raff
Tim Oates
James Holt
Mark McLean

Holographic Reduced Representations (HRR) are a method for performing symbolic AI on top of real-valued vectors by associating each vector with an abstract concept, and providing mathematical operations to manipulate vectors as if they were classic symbolic objects. This method has seen little use outside of older symbolic AI work and cognitive science. Our goal is to revisit this approach to understand if it is viable for enabling a hybrid neural-symbolic approach to learning as a differential component of a deep learning architecture. HRRs today are not effective in a differential solution due to numerical instability, a problem we solve by introducing a projection step that forces the vectors to exist in a well behaved point in space. In doing so we improve the concept retrieval efficacy of HRRs by over $100\times$. Using multi-label classification we demonstrate how to leverage the symbolic HRR properties to develop a output layer and loss function that is able to learn effectively, and allows us to investigate some of the pros and cons of an HRR neuro-symbolic learning approach.

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ICLR Conference 2020 Conference Paper

Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation

Hang Gao
Xizhou Zhu
Stephen Lin 0001
Jifeng Dai

Convolutional networks are not aware of an object's geometric variations, which leads to inefficient utilization of model and data capacity. To overcome this issue, recent works on deformation modeling seek to spatially reconfigure the data towards a common arrangement such that semantic recognition suffers less from deformation. This is typically done by augmenting static operators with learned free-form sampling grids in the image space, dynamically tuned to the data and task for adapting the receptive field. Yet adapting the receptive field does not quite reach the actual goal -- what really matters to the network is the *effective* receptive field (ERF), which reflects how much each pixel contributes. It is thus natural to design other approaches to adapt the ERF directly during runtime. In this work, we instantiate one possible solution as Deformable Kernels (DKs), a family of novel and generic convolutional operators for handling object deformations by directly adapting the ERF while leaving the receptive field untouched. At the heart of our method is the ability to resample the original kernel space towards recovering the deformation of objects. This approach is justified with theoretical insights that the ERF is strictly determined by data sampling locations and kernel values. We implement DKs as generic drop-in replacements of rigid kernels and conduct a series of empirical studies whose results conform with our theories. Over several tasks and standard base models, our approach compares favorably against prior works that adapt during runtime. In addition, further experiments suggest a working mechanism orthogonal and complementary to previous works.

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ICRA Conference 2020 Conference Paper

LiDAR Inertial Odometry Aided Robust LiDAR Localization System in Changing City Scenes

Wendong Ding
Shenhua Hou
Hang Gao
Guowei Wan
Shiyu Song

Environmental fluctuations pose crucial challenges to a localization system in autonomous driving. We present a robust LiDAR localization system that maintains its kinematic estimation in changing urban scenarios by using a dead reckoning solution implemented through a LiDAR inertial odometry. Our localization framework jointly uses information from complementary modalities such as global matching and LiDAR inertial odometry to achieve accurate and smooth localization estimation. To improve the performance of the LiDAR odometry, we incorporate inertial and LiDAR intensity cues into an occupancy grid based LiDAR odometry to enhance frame-to-frame motion and matching estimation. Multi-resolution occupancy grid is implemented yielding a coarse-to-fine approach to balance the odometry's precision and computational requirement. To fuse both the odometry and global matching results, we formulate a MAP estimation problem in a pose graph fusion framework that can be efficiently solved. An effective environmental change detection method is proposed that allows us to know exactly when and what portion of the map requires an update. We comprehensively validate the effectiveness of the proposed approaches using both the Apollo-SouthBay dataset and our internal dataset. The results confirm that our efforts lead to a more robust and accurate localization system, especially in dynamically changing urban scenarios.

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AAAI Conference 2019 Conference Paper

Evolutionarily Learning Multi-Aspect Interactions and Influences from Network Structure and Node Content

Songlei Jian
Liang Hu
Longbing Cao
Kai Lu
Hang Gao

The formation of a complex network is highly driven by multi-aspect node influences and interactions, reflected on network structures and the content embodied in network nodes. Limited work has jointly modeled all these aspects, which typically focuses on topological structures but overlooks the heterogeneous interactions behind node linkage and contributions of node content to the interactive heterogeneities. Here, we propose a multi-aspect interaction and influence-unified evolutionary coupled system (MAI-ECS) for network representation by involving node content and linkage-based network structure. MAI-ECS jointly and iteratively learns two systems: a multi-aspect interaction learning system to capture heterogeneous hidden interactions between nodes and an influence propagation system to capture multiaspect node influences and their propagation between nodes. MAI-ECS couples, unifies and optimizes the two systems toward an effective representation of explicit node content and network structure, and implicit node interactions and influences. MAI-ECS shows superior performance in node classification and link prediction in comparison with the stateof-the-art methods on two real-world datasets. Further, we demonstrate the semantic interpretability of the results generated by MAI-ECS.

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NeurIPS Conference 2018 Conference Paper

Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks

Hang Gao
Zheng Shou
Alireza Zareian
Hanwang Zhang
Shih-Fu Chang

Deep neural networks suffer from over-fitting and catastrophic forgetting when trained with small data. One natural remedy for this problem is data augmentation, which has been recently shown to be effective. However, previous works either assume that intra-class variances can always be generalized to new classes, or employ naive generation methods to hallucinate finite examples without modeling their latent distributions. In this work, we propose Covariance-Preserving Adversarial Augmentation Networks to overcome existing limits of low-shot learning. Specifically, a novel Generative Adversarial Network is designed to model the latent distribution of each novel class given its related base counterparts. Since direct estimation on novel classes can be inductively biased, we explicitly preserve covariance information as the ``variability'' of base examples during the generation process. Empirical results show that our model can generate realistic yet diverse examples, leading to substantial improvements on the ImageNet benchmark over the state of the art.

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IJCAI Conference 2017 Conference Paper

Embedding-based Representation of Categorical Data by Hierarchical Value Coupling Learning

Songlei Jian
Longbing Cao
Guansong Pang
Kai Lu
Hang Gao

Learning the representation of categorical data with hierarchical value coupling relationships is very challenging but critical for the effective analysis and learning of such data. This paper proposes a novel coupled unsupervised categorical data representation (CURE) framework and its instantiation, i. e. , a coupled data embedding (CDE) method, for representing categorical data by hierarchical value-to-value cluster coupling learning. Unlike existing embedding- and similarity-based representation methods which can capture only a part or none of these complex couplings, CDE explicitly incorporates the hierarchical couplings into its embedding representation. CDE first learns two complementary feature value couplings which are then used to cluster values with different granularities. It further models the couplings in value clusters within the same granularity and with different granularities to embed feature values into a new numerical space with independent dimensions. Substantial experiments show that CDE significantly outperforms three popular unsupervised embedding methods and three state-of-the-art similarity-based representation methods.

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