AAAI Conference on Artificial Intelligence Β· 1014 papers
End-to-End Learning of LTLf Formulae by Faithful LTLf Encoding
Hai Wan (Sun Yat-sen University), Bo Peng (Sun Yat-sen University)
CodeClassificationExplainability and InterpretabilityTabular
π― What it does: This study proposes an end-to-end learning method for linear temporal logic (LTL_f) formulas, utilizing 'faithful LTL_f encoding' to ensure that the parameters of the neural network correspond one-to-one with the LTL_f formulas, thereby enabling the automatic discovery of tree-structured formulas from large-scale data.
π― What it does: A streaming time series classification network named strAPI has been designed and implemented, capable of real-time processing of time series on resource-constrained devices and providing classification results.
CodeRecognitionExplainability and InterpretabilityRecurrent Neural NetworkTransformerImage
π― What it does: A structured sketch recognition network has been constructed, utilizing a semantic component-level memory module to achieve interpretable sketch recognition and segmentation.
Enhancing Cognitive Diagnosis Using Un-interacted Exercises: A Collaboration-Aware Mixed Sampling Approach
Haiping Ma (Anhui University), Xingyi Zhang (Anhui University)
CodeTabular
π― What it does: This paper proposes the CMES (Collaborative-aware Mixed Exercise Sampling) framework, which enhances the accuracy of cognitive diagnosis by sampling and mixing non-interactive questions.
Enhancing Ensemble Clustering with Adaptive High-Order Topological Weights
Jiaxuan Xu (Southwestern University of Finance and Economics), Lei Duan (Southwestern University of Finance and Economics)
CodeTabular
π― What it does: This paper proposes a topology-based ensemble clustering algorithm with adaptive weights based on higher-order connections (AWEC). It learns the optimal connection matrix from the multi-order connection information of the co-affinity matrix and embeds topology learning to achieve more robust clustering results.
π― What it does: This paper proposes an unsupervised multi-exposure correction and dual-layer optimization framework that jointly trains NeRF to synthesize high-quality new perspective images under extreme lighting conditions such as low light and overexposure.
π― What it does: A RAW-to-sRGB mapping framework called FourierISP is proposed, which utilizes separate sub-networks for structure enhancement, style learning, and color fusion to achieve high-quality mobile RAW to DSLR RGB conversion.
Enhancing Semi-supervised Domain Adaptation via Effective Target Labeling
Jiujun He (Southwestern University of Finance and Economics), Guosheng Yin (University of Hong Kong)
CodeDomain AdaptationImage
π― What it does: An effective target sample labeling framework is proposed, which utilizes active learning and pseudo-labeling strategies to automatically select informative target domain samples, thereby enhancing the performance of semi-supervised domain adaptation (SSDA).
π― What it does: A stochastic gating model is introduced in deep SNNs to randomly filter spikes, enhancing adversarial robustness and energy efficiency.
Enhancing Training of Spiking Neural Network with Stochastic Latency
Srinivas Anumasa (Mohamed bin Zayed University of Artificial Intelligence), Bin Gu (Jilin University)
CodeSpiking Neural NetworkImage
π― What it does: A direct training method called Stochastic Latency Training (SLT) is proposed, enabling a single SNN model to maintain high accuracy across different inference latencies while significantly reducing training time.
π― What it does: This paper proposes an Entropic Open-Set Active Learning framework that selects informative samples by utilizing two entropy scores for known and unknown categories, thereby achieving better sample selection in open-set active learning.
π― What it does: A new return decomposition method based on sub-trajectory differences, called Diaster, is proposed to generate immediate agent rewards for reinforcement learning in environments with extremely delayed rewards.
π― What it does: This paper proposes the Equity-Transformer, which solves the min-max routing problem (multi-agent TSP and multi-agent pickup and delivery problem) using a sequential generation method, achieving efficient solutions for large-scale cities and numbers of agents.
π― What it does: A prototype-free, example-free class incremental learning framework called eTag is proposed, which utilizes embedding distillation to retain the knowledge of the feature extractor while generating task-oriented features that match the classifier to address the problem of catastrophic forgetting.
EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within Transformer
Fei Wang (Hefei University of Technology), Meng Wang (Hefei University of Technology)
CodeTransformerVideo
π― What it does: In this paper, the authors propose a Transformer-based EulerMormer framework that implements adaptive denoising and texture-shape separation in video motion magnification using a dynamic filtering strategy.
Evaluate Geometry of Radiance Fields with Low-Frequency Color Prior
Qihang Fang (Chinese Academy of Sciences), Liefeng Bo (Alibaba Group)
CodeNeural Radiance FieldPoint Cloud
π― What it does: A new metric for evaluating the geometric quality of light radiation fields under no geometric ground truth conditions is proposedβInverse Mean Square Residual Color (IMRC), which is theoretically and empirically validated.
Every Node Is Different: Dynamically Fusing Self-Supervised Tasks for Attributed Graph Clustering
Pengfei Zhu (Tianjin University), Qinghua Hu (Tianjin University)
CodeRepresentation LearningGraph Neural NetworkMixture of ExpertsGraph
π― What it does: A dynamic fusion self-supervised learning framework DyFSS is proposed, which adaptively fuses features from multiple self-supervised tasks for each node to enhance attribute graph clustering performance.
π― What it does: EvoPrompt is proposed, a continuous learning method based on learnable continuous prompts, using ViT for adaptive prompting and continuous memory fusion.
Mohimenul Kabir (National University of Singapore), Kuldeep S. Meel
CodeGraphBenchmark
π― What it does: The sharpASP framework is proposed, which achieves scalable exact answer set counting based on a new answer set definition and Copy operation.
π― What it does: A 3D human motion prediction framework considering uncertainty is proposed, combining Self-Attention Graph Generation Blocks (SAGGB) with Adaptive Salient Loss to achieve dynamic weighted learning for future frames.
Andrew Zhao (Tsinghua University), Gao Huang (Tsinghua University)
CodeExplainability and InterpretabilityReinforcement Learning from Human FeedbackTransformerLarge Language ModelAgentic AIPrompt EngineeringTextRetrieval-Augmented Generation
π― What it does: This paper proposes the ExpeL agent, which autonomously collects successful and failed trajectories on training tasks (using Reflexion for multiple attempts) to extract natural language insights from experience. During the evaluation phase, it enhances decision-making by utilizing retrieved similar successful trajectories and distilled insights, all without the need to fine-tune the LLM parameters.
Explaining Reinforcement Learning Agents through Counterfactual Action Outcomes
Yotam Amitai (Technion - Israel Institute of Technology), Ofra Amir (Technion - Israel Institute of Technology)
CodeAutonomous DrivingExplainability and InterpretabilityReinforcement LearningVideo
π― What it does: This paper proposes the COViz method, which visually displays the trajectory differences between actions chosen by the RL agent in a given state and the optimal alternative actions, and combines it with the Reward Decomposition method to assess its help in user understanding of agent preferences.
Explicit Visual Prompts for Visual Object Tracking
Liangtao Shi (Guangxi Normal University), Xianxian Li (Harbin Institute of Technology)
CodeObject TrackingTransformerVideo
π― What it does: A visual tracking framework called EVPTrack is proposed, which is based on explicit visual prompts (spatio-temporal and multi-scale). It utilizes token propagation to convey spatio-temporal information and directly fuses prompts with image tokens through a transformer encoder, thereby avoiding the template update problem.
π― What it does: This paper proposes using the element-mean difference of the CNN's terminal feature vector as auxiliary information for OOD detection, combined with energy scores to construct a new OOD score.
π― What it does: A multi-modal vehicle detection network PCDNet is proposed, which integrates RGB and linear polarization information to achieve robust vehicle detection in extreme lighting, rain, fog, and dense vehicle scenarios.
Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoning of Large Language Models
Dingzirui Wang (Harbin Institute of Technology), Wanxiang Che (Harbin Institute of Technology)
CodeTransformerLarge Language ModelPrompt EngineeringText
π― What it does: Proposes the use of equations as an intermediate semantic representation to solve numerical reasoning tasks, and enhances the equation generation capability of LLMs through the BRIDGE method.
Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations
Likang Wu (University of Science and Technology of China), Enhong Chen (University of Science and Technology of China)
CodeRecommendation SystemGraph Neural NetworkTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringTextGraph
π― What it does: A recruitment recommendation framework GLRec based on large language models is proposed, utilizing meta-paths of behavior graphs to construct prompts, enabling LLMs to understand user behavior graphs and generate personalized recommendation results.
Zhen Qin (Open NLPLab), Hui Deng (Northwestern Polytechnical University)
CodeTransformerText
π― What it does: The study investigates whether Transformer can support longer sequences during inference (length extrapolation), proposes and verifies the convergence conditions of RPE (Relative Position Encoding), derives the theoretical receptive field (TRF), and conducts experimental validation using two new RPEs.
π― What it does: This paper proposes a facial set recognition method called FaceCoresetNet based on differentiable core set selection. It first selects a core subset from an unbounded image collection that balances quality and diversity, then enhances core features using self-attention and cross-attention, and finally obtains a fixed-length template representation for face recognition.
Fact-Driven Logical Reasoning for Machine Reading Comprehension
Siru Ouyang (University of Illinois), Hai Zhao (Shanghai Jiao Tong University)
CodeGraph Neural NetworkTransformerLarge Language ModelText
π― What it does: This paper proposes a hierarchical graph model called Focal Reasoner based on factual units, which constructs factual units by extracting the subject-verb-object structure from sentences and simultaneously models sentence-level and entity-level interactions on a hypergraph to achieve logical reasoning.
π― What it does: An unsupervised decoupled representation learning framework is constructed, which decomposes images into multiple 'content + mask' pairs and reconstructs images through a conditional diffusion model, thereby learning interpretable visual concepts.
π― What it does: This paper proposes a long-term fairness optimization framework that considers distribution shifts caused by policies, and provides a corresponding executable policy update method.
π― What it does: A joint weighted entropy objective and representation matching loss function is proposed to achieve a trade-off between fairness and accuracy in the presence of a small number of unlabeled test samples and covariate shift.
FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization
Cheng Yang (Beijing University of Posts and Telecommunications), Chuan Shi (Beijing University of Posts and Telecommunications)
CodeGraph Neural NetworkGraphFinance Related
π― What it does: A fair graph neural network framework called FairSIN is proposed, which eliminates sensitive bias in node representations by introducing fairness-promoting features (F3) before information propagation.
Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals
Patrick Altmeyer (Delft University of Technology), Cynthia C. S. Liem (Delft University of Technology)
CodeAnomaly DetectionOptimizationExplainability and InterpretabilityData-Centric LearningImageTabularFinance RelatedStochastic Differential Equation
π― What it does: This paper proposes an energy-constrained and confidence-prediction-based counterfactual generation method called ECCCo, which aims to generate explanations that are both trustworthy and consistent with the data distribution in black-box models.
π― What it does: This paper proposes a sparse query framework called Far3D, which generates 3D adaptive queries using high-quality 2D detection priors and achieves long-range 3D object detection at 150 m through viewpoint-aware aggregation and range modulation denoising.
π― What it does: A fast and controllable post-training sparsification framework called FCPTS has been developed, which utilizes a differentiable bridge function to learn global sparsity rate allocation, generating high-accuracy sparse models within minutes.
Feature Distribution Matching by Optimal Transport for Effective and Robust Coreset Selection
Weiwei Xiao (Harbin Institute of Technology), Jingyong Su (Harbin Institute of Technology)
CodeClassificationSupervised Fine-TuningImage
π― What it does: A coreset selection method based on feature distribution matching, FDMat, is proposed, aiming to reduce the distribution bias between the subset and the original dataset through optimal transport.
Feature Fusion from Head to Tail for Long-Tailed Visual Recognition
Mengke Li (Guangdong Laboratory of Artificial Intelligence and Digital Economy), Hui Huang (Hong Kong Baptist University)
CodeClassificationRecognitionImage
π― What it does: A head-to-tail feature fusion method (H2T) is proposed for long-tail visual recognition, which enhances the semantic diversity of tail classes by randomly replacing some channels in the tail class feature map with head class features.
π― What it does: A Fed-QSSL framework is proposed to address the issues of data distribution heterogeneity and client bit-width heterogeneity in federated learning.
π― What it does: A non-IID annotation noise modeling and robust training method for multi-source data is proposed in federated medical image segmentation;
FedCD: Federated Semi-Supervised Learning with Class Awareness Balance via Dual Teachers
Yuzhi Liu (Shenzhen University), Jing Qin (Hong Kong Polytechnic University)
CodeFederated LearningKnowledge DistillationConvolutional Neural NetworkTransformerImageBiomedical Data
π― What it does: Proposes the FedCD framework, which utilizes dual teacher distillation and class-aware balancing to address knowledge drift and class imbalance issues in federated semi-supervised learning.
FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning
Xianjie Guo (Hefei University of Technology), Jiuyong Li (University of South Australia)
CodeFederated LearningSafty and PrivacyComputational EfficiencyTabular
π― What it does: A scalable and accurate federated causal structure learning method, FedCSL, is proposed to address the scalability and accuracy issues of existing methods under high-dimensional data and sample imbalance.
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels
Jichang Li (Sun Yat-sen University), Yizhou Yu (University of Hong Kong)
CodeFederated LearningImage
π― What it does: Designed and implemented the FedDiv framework to handle noisy labeled data in federated learning, achieving noise detection and sample re-labeling through a global noise filter and a prediction consistency sampler.
π― What it does: A federated graph learning framework named FGGP is proposed, which utilizes generalizable prototypes to achieve shared model training of multi-domain graph data, addressing the global model generalization problem under domain shift.
π― What it does: Proposes the FedMEMA framework, which employs a federated modality-specific encoder and a server-side multimodal fusion decoder, and optimizes both the full-modal server model and the personalized model of the client with missing modalities simultaneously through multi-anchor cross-attention calibration.
Zibin Pan (Chinese University of Hong Kong), Junhua Zhao (Chinese University of Hong Kong)
CodeOptimizationFederated LearningImage
π― What it does: This paper proposes the FedLF algorithm, which utilizes hierarchical public square directions to improve fairness in federated learning.
π― What it does: A heterogeneous federated learning framework named FedLPS is proposed, which utilizes shared encoders and task-specific predictors to achieve multi-task local parameter sharing, and reduces resource consumption on edge devices through channel-level pruning and heterogeneous aggregation, while maintaining or even improving model accuracy.
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning
Jian Li (Institute of Information Engineering Chinese Academy of Sciences), Weiping Wang (Institute of Information Engineering Chinese Academy of Sciences)
CodeOptimizationFederated LearningTabular
π― What it does: This paper proposes Federated Newton Sketch (FedNS) and its improved version FedNDES, which utilize sketch-based square root Hessian information to achieve second-order optimization in federated learning.
π― What it does: This paper proposes a federated style transfer learning framework called FedST, which utilizes a denoising diffusion probabilistic model to achieve style decoupling and synthesis across domains, thereby alleviating the impact of non-IID in image segmentation tasks.
CodeFederated LearningSafty and PrivacyConvolutional Neural NetworkContrastive LearningImage
π― What it does: A globally shareable and trainable prototype (TGP) is designed for heterogeneous federated learning, achieving greater separation between different categories through adaptive margin-enhanced contrastive learning, which guides the training of client models.
π― What it does: A dynamic filter is proposed, and two visual models, DFFormer and CDFFormer, are constructed based on the MetaFormer framework, improving the token mixer based on FFT benchmarks to achieve faster and more memory-efficient processing of high-resolution images.
Finding Visual Saliency in Continuous Spike Stream
Lin Zhu (Beijing Institute of Technology), Hua Huang (Beijing Normal University)
CodeSpiking Neural NetworkTransformerTime Series
π― What it does: This paper proposes a recursive spiking Transformer framework for full SNNs to detect visual saliency from continuous spike streams of spiking cameras.
π― What it does: Proposes the Fine Structure-Aware Sampling (FSS) scheme and its extensions NSP and MTL for training pixel-aligned implicit models to achieve single-view human reconstruction with clothing, significantly improving the reconstruction quality of thin components and reducing noise.
π― What it does: This paper proposes a fine-grained multi-view hand mesh reconstruction method that utilizes inverse rendering to finely recover hand posture and geometric details, and achieves realistic image synthesis through a pre-trained mesh neural renderer.
π― What it does: The research focuses on few-shot object detection based on meta-learning, proposing Fine-Grained Prototype Distillation (FFA) and improving feature aggregation and fusion;
π― What it does: When fine-tuning GNNs after pre-training, the fine-tuning effect is enhanced by constructing graphon to maintain the generative patterns of downstream graphs.
Fine-Tuning Large Language Model Based Explainable Recommendation with Explainable Quality Reward
Mengyuan Yang (Zhejiang University), Jianwei Yin (Zhejiang University)
CodeRecommendation SystemExplainability and InterpretabilityReinforcement Learning from Human FeedbackTransformerLarge Language ModelReinforcement LearningGenerative Adversarial NetworkContrastive LearningText
π― What it does: A large language model-based explainable recommendation system LLM2ER was constructed, and unsupervised fine-tuning of explanation quality was achieved through reinforcement learning combined with two reward models (Concept Consistency Reward CCR and High-Quality Alignment Reward HQAR), resulting in LLM2ER-EQR, which can generate personalized, consistent, and high-quality text explanations.
FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering
Zhenyu Li (Tsinghua University), Jianyong Wang (Tsinghua University)
CodeTransformerLarge Language ModelSupervised Fine-TuningText
π― What it does: Proposes the FlexKBQA framework, which uses large language models (LLM) as program translators to generate executable SPARQL / S-expression and creates a large number of synthetic question-answer pairs, then trains with a lightweight model, alleviating distribution drift and semantic errors through execution-guided self-training (EGST) and intrinsic reasoning (IR);
Fluctuation-Based Adaptive Structured Pruning for Large Language Models
Yongqi An (Chinese Academy of Sciences), Jinqiao Wang (Chinese Academy of Sciences)
CodeCompressionOptimizationTransformerLarge Language ModelText
π― What it does: FLAP is proposed, a training-free, structured pruning framework specifically designed for large language models (LLMs), achieving column-level pruning and compensating for losses through a volatility-based recoverability metric.
CodeObject DetectionTransformerLarge Language ModelDiffusion modelContrastive LearningTextPoint Cloud
π― What it does: This paper proposes FM-OV3D, which integrates cross-modal knowledge from various pre-trained foundational models to enhance the open vocabulary detection capability of 3D point clouds.
π― What it does: Proposes the Focus-Then-Decide (FTD) framework, which uses an attention selector to filter the objects returned by the base segmentation model, retaining only task-relevant objects to achieve visual reinforcement learning;
Qi Han (Xi'an Jiaotong University), Fei Guo (Xi'an Jiaotong University)
CodeOptimizationReinforcement LearningTime Series
π― What it does: This paper proposes a multi-armed bandit algorithm that alternates between greedy decision-making and forced exploration without requiring prior knowledge of the reward distribution parameters, and provides theoretical analysis and implementation in both steady-state and piecewise steady-state environments.
Formal Logic Enabled Personalized Federated Learning through Property Inference
Ziyan An (Vanderbilt University), Meiyi Ma (Vanderbilt University)
CodeFederated LearningRecurrent Neural NetworkTime Series
π― What it does: A personalized federated learning framework named FedSTL is proposed, which enhances the model's symbolic reasoning ability by automatically inferring the temporal logic properties of each client;
Frequency-Adaptive Pan-Sharpening with Mixture of Experts
Xuanhua He (University of Science and Technology of China), Man Zhou (Nanyang Technological University)
CodeImage TranslationRestorationMixture of ExpertsImage
π― What it does: This paper proposes a full-resolution multispectral image fusion method based on frequency adaptive mixture of experts (FAME), utilizing learnable frequency masks to process high and low-frequency information separately and fuse them dynamically;
π― What it does: A lightweight frequency domain learning network called FreqNet is proposed for general detection of unseen deepfake images with limited training data.
From Coarse to Fine: A Distillation Method for Fine-Grained Emotion-Causal Span Pair Extraction in Conversation
Xinhao Chen (East China Normal University), Aimin Zhou (East China Normal University)
CodeRecognitionKnowledge DistillationTransformerLarge Language ModelPrompt EngineeringText
π― What it does: This study investigates the extraction of fine-grained emotions and causal spans, proposing the KD-ECS method based on knowledge distillation, and constructs the FG-RECCON fine-grained emotional causal dataset.
From Retrieval to Generation: A Simple and Unified Generative Model for End-to-End Task-Oriented Dialogue
Zeyuan Ding (Dalian University of Technology), Hongfei Lin (Dalian University of Technology)
CodeGenerationRetrievalTransformerLarge Language ModelTextRetrieval-Augmented Generation
π― What it does: A unified generative model called Uni-ToD is proposed, which simultaneously accomplishes knowledge retrieval and dialogue generation using a single language model.
π― What it does: Utilizing a frozen CLIP Transformer as a point cloud encoder, combined with a point cloud tokenizer and task tokens, to directly fine-tune on various point cloud tasks.
Subhabrata Dutta (Indian Institute of Technology Delhi), Tanmoy Chakraborty (Indian Institute of Technology Bombay)
CodeTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningText
π― What it does: This paper presents the SYRELM system, which utilizes small-scale language models to translate natural language arithmetic problems into formal language (FL) expressions and calls a symbolic solver to complete the reasoning.
Fully Data-Driven Pseudo Label Estimation for Pointly-Supervised Panoptic Segmentation
Jing Li (University of Chinese Academy of Sciences), Zhaoxiang Zhang
CodeObject DetectionSegmentationTransformerImage
π― What it does: A completely data-driven pseudo-label branch is proposed, which utilizes sparse point labels to generate full-pixel pseudo-labels for training a panorama segmentation model focused on point annotations.
π― What it does: A joint training framework guided by a noise-aware classifier is constructed by combining conditional GANs with programmatic weak supervision, and representative samples are selected through submodular optimal subset selection to improve label quality and image generation effects.
FusionFormer: A Concise Unified Feature Fusion Transformer for 3D Pose Estimation
Yanlu Cai (Fudan University), Cheng Jin (Fudan University)
CodePose EstimationTransformerVideo
π― What it does: A unified feature fusion transformer, FusionFormer, is designed to integrate multi-view and multi-frame information for camera-parameter-free 3D human pose estimation.
π― What it does: This paper proposes a single-domain generalization object detection framework G-NAS based on differentiable neural architecture search, addressing the generalization problem of models trained on a single source domain to multiple target domains.
π― What it does: A global semantic awareness method based on graphs, Gβ―SAM, is proposed, which generates multimodal graph representations using a fine-grained graph alignment model, and determines sarcasm during the inference phase through kNN global semantic similarity voting. Additionally, label-aware graph contrastive learning is introduced to enhance semantic consistency.
GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework
Fangyikang Wang (Zhejiang University), Hui Qian (Zhejiang University)
CodeTabular
π― What it does: This paper proposes a particle variational inference framework GAD-PVI that simultaneously accelerates position updates and adjusts dynamic weights.
π― What it does: An unsupervised fake news detection method called GAMC is proposed, which utilizes graph autoencoders, feature masking, and contrastive learning to learn latent features from news propagation graphs.
Gated Attention Coding for Training High-Performance and Efficient Spiking Neural Networks
Xuerui Qiu (Institute of Automation, Chinese Academy of Sciences), Guoqi Li (Institute of Automation, Chinese Academy of Sciences)
CodeClassificationSpiking Neural NetworkImage
π― What it does: A Gated Attention Coding (GAC) pre-coding module is proposed in SNN, which efficiently encodes static images into powerful and spatiotemporally dynamic spike sequences using multidimensional gated attention units while maintaining spike-driven characteristics.
π― What it does: A weakly supervised temporal video localization framework is proposed, utilizing learnable Gaussian Mixture Proposals (GMP) and a Pull-Push Scheme to capture diverse events.
Wei Zhang (Columbia University), John Paisley (Columbia University)
CodeOptimizationExplainability and InterpretabilityTabular
π― What it does: This paper proposes a Gaussian Process-based Neural Additive Model (GP-NAM), which models the shape function of each feature as a one-dimensional Gaussian process by approximating the RBF kernel with Random Fourier Features (RFF), resulting in an interpretable, parameter-efficient, and convex additive model.
π― What it does: A universal graph convolution (UniGC) and dynamic graph convolution network framework GCNext are proposed, which can dynamically select the most suitable type of graph convolution for each layer and each sample, and can be used for training from scratch or refining existing GCNs.
Alberto Pozanco (J.P. Morgan), Daniel Borrajo (J.P. Morgan)
CodeOptimizationSafty and PrivacyTabular
π― What it does: In the task of redesigning planning environments, a general, metric-independent method is proposed that can minimize action deletions to redesign the environment based on the goals and metrics of any interested party; a series of new privacy and distance-related metrics are also introduced.
Generalizable Sleep Staging via Multi-Level Domain Alignment
Jiquan Wang (Zhejiang University), Gang Pan (Zhejiang University)
CodeClassificationDomain AdaptationConvolutional Neural NetworkTransformerAuto EncoderTime SeriesBiomedical Data
π― What it does: A domain generalization framework for sleep staging, SleepDG, is proposed, which can achieve high-accuracy staging on unseen datasets.
Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons
Julien Fageot (Tournesol Association), Oscar Villemaud (Tournesol Association)
Code
π― What it does: This paper proposes a general Bradley-Terry model (GBT) framework for transforming pairwise comparisons into object scores, providing theoretical guarantees such as strict convexity, monotonicity, and Lipschitz robustness within this framework.
Generalized Planning for the Abstraction and Reasoning Corpus
Chao Lei (University of Melbourne), Krista A. Ehinger (University of Melbourne)
Code
π― What it does: A generalized planning-based ARC solver GPAR is proposed, which maps each ARC task to a PDDL planning problem and implements object-level conditions and loops through external functions and pointer references;
Generalized Variational Inference via Optimal Transport
Jinjin Chi (Jilin University), Hongbin Pei (Xi'an Jiaotong University)
CodeOptimizationAuto EncoderImageTabular
π― What it does: This paper proposes a variational inference method called VOT that uses optimal transport distance (OT) to measure the difference between the prior and the variational posterior. By introducing a Ξ» hyperparameter, OT constraints are gradually incorporated, forming a gradient-compatible black-box optimization framework.
Generating Novel Leads for Drug Discovery Using LLMs with Logical Feedback
Shreyas Bhat Brahmavar (Birla Institute of Technology and Science Pilani), Raviprasad Aduri (TCS Innovation Labs)
CodeDrug DiscoveryTransformerLarge Language ModelPrompt EngineeringBiomedical Data
π― What it does: The study utilizes large language models (LLMs) and a logical feedback mechanism to propose an iterative LMLF method that automatically refines logical constraints and generates drug lead molecules.
Generating Universal Adversarial Perturbations for Quantum Classifiers
Gautham Anil (Indian Institute of Technology Madras), Apurva Narayan (University of Western Ontario)
CodeClassificationAdversarial AttackAuto EncoderGenerative Adversarial NetworkImagePhysics Related
π― What it does: This study proposes two methods for generating universal adversarial perturbations (UAP) for quantum classifiers: additive UAP based on classical generative models (QuGAP-A) and unitary UAP based on quantum generative models (QuGAP-U), and verifies their attack effects on classifiers based on parameterized quantum circuits (PQC).