GenAI4ELab/papercli-papers huggingface dataset:
Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models
Authors: Lingzhi Wang; Xingshan Zeng; Jinsong Guo; Kam-Fai Wong; Georg Gottlob
Venue: AAAI 2025 (main)

Abstract: This paper explores Machine Unlearning (MU), an emerging field that is gaining increased attention due to concerns about neural models unintentionally remembering personal or sensitive information. We present SeUL, a novel method that enables selective and fine-grained unlearning for language models. Unlike previous work that employs a fully reversed training objective in unlearning, SeUL minimizes the negative impact on the capability of language models, particularly in terms of generation. Furthermore, we introduce two innovative evaluation metrics, sensitive extraction likelihood (S-EL) and sensitive memorization accuracy (S-MA), specifically designed to assess the effectiveness of forgetting sensitive information. In support of the unlearning framework, we propose efficient automatic online and offline sensitive span annotation methods. The online selection method, based on language probability scores, ensures computational efficiency, while the offline annotation involves a two-stage LLM-based process for robust verification. In summary, this paper contributes a novel selective unlearning method (SeUL), introduces specialized evaluation metrics (S-EL and S-MA) for assessing sensitive information forgetting, and proposes automatic online and offline sensitive span annotation methods to support the overall unlearning framework and evaluation.
VisRec: A Semi-Supervised Approach to Visibility Data Reconstruction in Radio Astronomy
Authors: Ruoqi Wang; Haitao Wang; Qiong Luo; Feng Wang; Hejun Wu
Venue: AAAI 2025 (main)

Abstract: Radio telescopes produce visibility data about celestial objects, but these data are sparse and noisy. As a result, images created on raw visibility data are of low quality. Recent studies have used deep learning models to reconstruct visibility data to get cleaner images. However, these methods rely on a substantial amount of labeled training data, which requires significant labeling effort from radio astronomers. Addressing this challenge, we propose VisRec, a model-agnostic semi-supervised learning approach to visibility data reconstruction in radio astronomy. Specifically, VisRec consists of both a supervised learning module and an unsupervised learning module. In the supervised learning module, we introduce a set of data augmentation functions to produce diverse visibility examples. In comparison, the unsupervised learning module in VisRec augments unlabeled data and uses reconstructions from non-augmented visibility as pseudo-labels for training. This hybrid approach allows VisRec to effectively leverage both labeled and unlabeled data. This way, VisRec performs well even when labeled data is scarce. Our evaluation results show that VisRec is applicable to various models, and outperforms all baseline methods in terms of reconstruction quality, robustness, and generalizability.
FMPM-DNet: Hyperspectral Pansharpening Dynamic Network Based on Feature Modulation and Probability Mask
Authors: Xiaozheng Wang; Yong Yang; Shuying Huang; Hangyuan Lu; Weiguo Wan; Aoqi Zhao
Venue: AAAI 2025 (main)

Abstract: Currently, most Hyperspectral (HS) pansharpening methods have two problems, namely the lack of consideration the spatial variations of HS images and inaccurate feature reconstruction in multi-channel complex mapping relationships, leading to spectral and spatial distortions in the fusion results. To address these issues, we propose a dynamic network based on feature modulation and probability mask (FMPM-DNet) for HS pansharpening, including two stages of spectral-spatial feature modulation and feature reconstruction. In the first stage, to increase the feature representation ability of the model, a wave function is defined based on complex transformation to convert spatial features into wave-like features. On this basis, considering the spatial variations of HS images, a dynamic feature modulation unit (DFMU) is constructed to achieve adaptive modulation and coarse fusion of features by dynamically generating spectral-spatial correction matrix. In the second stage, a feature probability mask unit (FPMU) is designed to realize global feature embedding at different depths and local feature embedding at the same depth to obtain refined fused features. Extensive experiments on three widely used datasets demonstrate that the proposed FMPM-Net achieves significant improvements in both spatial and spectral quality metrics compared to some state-of-the-art (SOTA) methods.
Aerodynamic Coefficients Prediction via Cross-Attention Fusion and Physical-Informed Training
Authors: Yueqing Wang; Peng Zhang; Yushuang Liu; Jianing Zhao; Jie Lin; Yi Chen
Venue: AAAI 2025 (main)

Abstract: Aerodynamic coefficient prediction is pivotal in aircraft and vehicles' design, performance evaluation, and motion control. Integrating artificial neural networks into aerodynamic coefficient prediction offers a promising alternative to traditional numerical methods burdened by extensive computations and high costs. Nevertheless, this data-driven approach faces several critical challenges, which limit its further performance enhancement: i) The current research lacks a profound understanding of the complex interplay between the shape of an object and its aerodynamic characteristics. ii) The scarcity of high-quality aerodynamic data poses a significant barrier. The models trained on limited datasets lack generalization ability, struggling to accurately predict and adapt to diverse aerodynamic performance under new shapes or conditions. To overcome these challenges, we introduce an innovative framework that employs cross-attention to capture the intimate interplay between shape and flow conditions and allows for the direct utilization of pre-trained models on general shape datasets to mitigate the scarcity of aerodynamic data. Furthermore, to bolster the inference capabilities of this data-driven approach, we integrate physical information constraints into the model, leveraging them as guiding principles to enhance the model's predictive power under unknown conditions. Experimental validation demonstrates that our proposed method performs excellently in multiple aerodynamic prediction tasks. This achievement brings a new technological breakthrough to the field of aerodynamic prediction and provides robust support for the design optimization of complex systems such as aircraft and vehicles.
Generalized Implicit Neural Representations for Dynamic Molecular Surface Modeling
Authors: Fang Wu; Bozhen Hu; Stan Z. Li
Venue: AAAI 2025 (main)

Abstract: Molecular dynamics (MD) has long been the de facto choice for simulating intricate physical systems from first principles. Recent efforts utilize the implicit neural representation (INR) to directly learn surface point clouds' signed distance function (SDF) with promising outcomes. However, INR's temporal generalization to unexplored molecular systems remains limited, which poses a significant barrier to applying INR to a broader range of real-world scenarios. This study introduces MoE-DSR, an enhanced version of dynamic surface representations (DSR) that effectively integrates the mixture-of-experts (MoE) strategy. Specifically, the router employs a novel geometric surface cloud network to extract the structural information from the initial static protein conformation as the prior knowledge. Meanwhile, experts compromising a team of equivariant implicit neural networks (E-INNs), each responsible for distinct protein families, ensure precise SDF estimation across varied protein data landscapes. We showcase the ability of MoE-DSR to model dynamic protein surface shapes using ensembles from ATLAS, the largest available protein MD simulations database. Extensive experiments validate its effectiveness in analyzing complex molecular systems across continuous space and time domains.
Vision Transformers Beat WideResNets on Small Scale Datasets Adversarial Robustness
Authors: Juntao Wu; Ziyu Song; Xiaoyu Zhang; Shujun Xie; Longxin Lin; Ke Wang
Venue: AAAI 2025 (main)

Abstract: For an extensive period, Vision Transformers (ViTs) have been deemed unsuitable for attaining robust performance on small-scale datasets, with WideResNet models maintaining dominance in this domain. While WideResNet models have persistently set the state-of-the-art (SOTA) benchmarks for robust accuracy on datasets such as CIFAR-10 and CIFAR-100, this paper challenges the prevailing belief that only WideResNet can excel in this context. We pose the critical question of whether ViTs can surpass the robust accuracy of WideResNet models. Our results provide a resounding affirmative answer. By employing ViT, enhanced with data generated by a diffusion model for adversarial training, we demonstrate that ViTs can indeed outshine WideResNet in terms of robust accuracy. Specifically, under the Infty-norm threat model with epsilon = 8/255, our approach achieves robust accuracies of 74.97% on CIFAR-10 and 44.07% on CIFAR-100, representing improvements of +3.9% and +1.4%, respectively, over the previous SOTA models. Notably, our ViT-B/2 model, with 3 times fewer parameters, surpasses the previously best-performing WRN-70-16. Our achievement opens a new avenue, suggesting that future models employing ViTs or other novel efficient architectures could eventually replace the long-dominant WRN models.
Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimization
Authors: Lirong Wu; Haitao Lin; Yufei Huang; Zhangyang Gao; Cheng Tan; Yunfan Liu; Tailin Wu; Stan Z. Li
Venue: AAAI 2025 (main)

Abstract: Antibodies are Y-shaped proteins that protect the host by binding to specific antigens, and their binding is mainly determined by the Complementary Determining Regions (CDRs) in the antibody. Despite the great progress made in CDR design, existing computational methods still encounter several challenges: 1) poor capability of modeling complex CDRs with long sequences due to insufficient contextual information; 2) conditioned on pre-given antigenic epitopes and their static interaction with the target antibody; 3) neglect of specificity during antibody optimization leads to non-specific antibodies. In this paper, we take into account a variety of node features, edge features, and edge relations to include more contextual and geometric information. We propose a novel Relation-Aware Antibody Design (RAAD) framework, which dynamically models antigen-antibody interactions for co-designing the sequences and structures of antigen-specific CDRs. Furthermore, we propose a new evaluation metric to better measure antibody specificity and develop a contrasting specificity-enhancing constraint to optimize the specificity of antibodies. Extensive experiments have demonstrated the superior capability of RAAD in terms of antibody modeling, generation, and optimization across different CDR types, sequence lengths, pre-training strategies, and input contexts.
MYOPIA: Protecting Face Privacy from Malicious Personalized Text-to-Image Synthesis via Unlearnable Examples
Authors: Zhihao Wu; Yushi Cheng; Tianyang Sun; Xiaoyu Ji; Wenyuan Xu
Venue: AAAI 2025 (main)

Abstract: Personalized text-to-image synthesis models, such as DreamBooth, have demonstrated significant potential in creating lifelike images tailored to a specific individual by fine-tuning from a limited set of face images and simple prompts. However, if misused, these model could pose a serious risk of privacy infringement by generating harmful images containing violent or pornographic content. To tackle this issue, this paper introduces MYOPIA, a method that renders facial images unlearnable by incorporating error-minimizing perturbations. These meticulously designed perturbations enables the model to quickly overfit to them, resulting in a swift reduction in loss and the cessation of model fine-tuning, effectively preventing the model from capturing genuine facial features. Moreover, to ensure the imperceptibility and robustness of the perturbations, we utilize the Just-Noticeable-Difference and Expectation-of-Transformation techniques to regulate both their location and intensity. Evaluation on two face dataset, i.e., VGGFace2 and CelebA-HQ, with various model versions illustrates the effectiveness of our approach in preserving personal privacy. Furthermore, our method showcases robust transferability across diverse model versions and demonstrates resilience against various image pre-processing techniques.
MultiSFL: Towards Accurate Split Federated Learning via Multi-Model Aggregation and Knowledge Replay
Authors: Zeke Xia; Ming Hu; Dengke Yan; Ruixuan Liu; Anran Li; Xiaofei Xie; Mingsong Chen
Venue: AAAI 2025 (main)

Abstract: Although Split Federated Learning (SFL)  effectively enables knowledge sharing among resource-constrained clients, it suffers from low training performance due to the neglect of data heterogeneity and catastrophic forgetting problems. To address these issues, we propose a novel SFL approach named MultiSFL, which adopts i) an effective multi-model aggregation mechanism to alleviate gradient divergence caused by heterogeneous data and ii) a novel knowledge replay strategy to deal with the catastrophic forgetting problem.
MultiSFL adopts two servers (i.e., the fed server and main server) to maintain multiple branch models 
for local training and an aggregated master model for knowledge sharing among branch models. 
To mitigate catastrophic forgetting, the main server of MultiSFL selects multiple assistant devices for knowledge replay according to the training data distribution of each full branch model.
Experimental results obtained from various non-IID and IID scenarios demonstrate that MultiSFL significantly outperforms conventional SFL methods by up to a 23.25% test accuracy improvement.
Generalizable Sensor-Based Activity Recognition via Categorical Concept Invariant Learning
Authors: Di Xiong; Shuoyuan Wang; Lei Zhang; Wenbo Huang; Chaolei Han
Venue: AAAI 2025 (main)

Abstract: Human Activity Recognition (HAR) aims to recognize activities by training models on massive sensor data. In real-world deployment, a crucial aspect of HAR that has been largely overlooked is that the test sets may have different distributions from training sets due to inter-subject variability including age, gender, behavioral habits, etc., which leads to poor generalization performance. One promising solution is to learn domain-invariant representations to enable a model to generalize on an unseen distribution. However, most existing methods only consider the feature-invariance of the penultimate layer for domain-invariant learning, which leads to suboptimal results. In this paper, we propose a Categorical Concept Invariant Learning (CCIL) framework for generalizable activity recognition, which introduces a concept matrix to regularize the model in the training stage by simultaneously concertrating on feature-invariance and logit-invariance. Our key idea is that the concept matrix for samples belonging to the same activity category should be similar. Extensive experiments on four public HAR benchmarks demonstrate that our CCIL substantially outperforms the state-of-the-art approaches under cross-person, cross-dataset, cross-position, and one-person-to-another settings.
Improving Multimodal Social Media Popularity Prediction via Selective Retrieval Knowledge Augmentation
Authors: Xovee Xu; Yifan Zhang; Fan Zhou; Jingkuan Song
Venue: AAAI 2025 (main)

Abstract: Understanding and predicting the popularity of online User-Generated Content (UGC) is critical for various social and recommendation systems. Existing efforts have focused on extracting predictive features and using pre-trained deep models to learn and fuse multimodal UGC representations. However, the dissemination of social UGCs is not an isolated process in social network; rather, it is influenced by contextual relevant UGCs and various exogenous factors, including social ties, trends, user interests, and platform algorithms. In this work, we propose a retrieval-based framework to enhance the popularity prediction of multimodal UGCs. Our framework extends beyond a simple semantic retrieval, incorporating a meta retrieval strategy that queries a diverse set of relevant UGCs by considering multimodal content semantics, and metadata from user and post. Moreover, to eliminate irrelevant and noisy UGCs in retrieval, we introduce a new measure called Relative Retrieval Contribution to Prediction (RRCP), which selectively refines the retrieved UGCs. We then aggregate the contextual UGC knowledge using vision-language graph neural networks, and fuse them with an RRCP-Attention-based prediction network. Extensive experiments on three large-scale social media datasets demonstrate significant improvements ranging from 26.68% to 48.19% across all metrics compared to strong baselines.
DearLLM: Enhancing Personalized Healthcare via Large Language Models-Deduced Feature Correlations
Authors: Yongxin Xu; Xinke Jiang; Xu Chu; Rihong Qiu; Yujie Feng; Hongxin Ding; Junfeng Zhao; Yasha Wang; Bing Xie
Venue: AAAI 2025 (main)

Abstract: Exploring the correlations between medical features is essential for extracting patient health patterns from electronic health records (EHR) data, and strengthening medical predictions and decision-making. To constrain the hypothesis space of pure data-driven deep learning in the context of limited annotated data, a common trend is to incorporate external knowledge, especially knowledge priors related to personalized health contexts, to optimize model training. However, most existing methods lack flexibility and are constrained by the uncertainties brought about by fixed feature correlation priors. In addition, in utilizing knowledge, these methods overlook the knowledge informative for personalized healthcare. To this end, we propose DearLLM, a novel and effective framework that leverages feature correlations deduced by large language models (LLMs) to enhance personalized healthcare. Concretely, DearLLM captures and learns quantitative correlations between medical features by calculating the conditional perplexity of LLMs’ deduction based on personalized patient backgrounds. Then, DearLLM enhances healthcare predictions by emphasizing knowledge that carries unique patient information through a feature-frequency-aware graph pooling method. Extensive experiments on two real-world benchmark datasets show significant performance gains brought by DearLLM. Furthermore, the discovered findings align well with medical literature, offering meaningful clinical interpretations.
PriFold: Biological Priors Improve RNA Secondary Structure Predictions
Authors: Chenchen Yang; Hao Wu; Tao Shen; Kai Zou; Siqi Sun
Venue: AAAI 2025 (main)

Abstract: Predicting RNA secondary structures is crucial for understanding RNA function, designing RNA-based therapeutics, and studying molecular interactions within cells. Existing deep-learning-based methods for RNA secondary structure prediction have mainly focused on local structural properties, often overlooking the global characteristics and evolutionary features of RNA sequences. Guided by biological priors, we propose PriFold, incorporating two key innovations: 1) improving attention mechanism with pairing probabilities to utilize global pairing characteristics, and 2) implementing data augmentation based on RNA covariation to leverage evolutionary information. Our structured enhanced pretraining and finetuning strategy significantly optimizes model performance. Extensive experiments demonstrate that PriFold achieves state-of-the-art (SOTA) results in RNA secondary structure prediction on benchmark datasets such as bpRNA, RNAStrAlign and ArchiveII. These results not only validate our prediction approach but also highlight the potential of integrating biological priors, such as global characteristics and evolutionary information, into RNA structure prediction tasks, opening new avenues for research in RNA biology and bioinformatics.
Graph Structure Learning for Spatial-Temporal Imputation: Adapting to Node and Feature Scales
Authors: Xinyu Yang; Yu Sun; Xinyang Chen; Ying Zhang; Xiaojie Yuan
Venue: AAAI 2025 (main)

Abstract: Spatial-temporal data collected across different geographic locations often suffer from missing values, posing challenges to data analysis. Existing methods primarily leverage fixed spatial graphs to impute missing values, which implicitly assume that the spatial relationship is roughly the same for all features across different locations. However, they may overlook the different spatial relationships of diverse features recorded by sensors in different locations. To address this, we introduce the multi-scale Graph Structure Learning framework for spatial-temporal Imputation (GSLI) that dynamically adapts to the heterogeneous spatial correlations. Our framework encompasses node-scale graph structure learning to cater to the distinct global spatial correlations of different features, and feature-scale graph structure learning to unveil common spatial correlation across features within all stations. Integrated with prominence modeling, our framework emphasizes nodes and features with greater significance in the imputation process. Furthermore, GSLI incorporates cross-feature and cross-temporal representation learning to capture spatial-temporal dependencies. Evaluated on six real incomplete spatial-temporal datasets, GSLI showcases the improvement in data imputation and downstream applications.
Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting
Authors: Tong Ye; Yangkai Du; Tengfei Ma; Lingfei Wu; Xuhong Zhang; Shouling Ji; Wenhai Wang
Venue: AAAI 2025 (main)

Abstract: Large Language Models (LLMs) have demonstrated remarkable proficiency in generating code. However, the misuse of LLM-generated (synthetic) code has raised concerns in both educational and industrial contexts, underscoring the urgent need for synthetic code detectors. Existing methods for detecting synthetic content are primarily designed for general text and struggle with code due to the unique grammatical structure of programming languages and the presence of numerous ``low-entropy'' tokens. Building on this, our work proposes a novel zero-shot synthetic code detector based on the similarity between the original code and its LLM-rewritten variants. Our method is based on the observation that differences between LLM-rewritten and original code tend to be smaller when the original code is synthetic. We utilize self-supervised contrastive learning to train a code similarity model and evaluate our approach on two synthetic code detection benchmarks. Our results demonstrate a significant improvement over existing SOTA synthetic content detectors, delivering notable gains in both performance and robustness on the APPS and MBPP benchmarks.