{"total":706,"rows":[{"id":"1909.08593","title":"Fine-Tuning Language Models from Human Preferences","created":"2019-09-18","cited_by_count":2150,"area":"alignment"},{"id":"2105.03023","title":"DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts","created":"2021-05-07","cited_by_count":441,"area":null},{"id":"2103.14749","title":"Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks","created":"2021-03-26","cited_by_count":395,"area":null},{"id":"2110.06199","title":"ABO: Dataset and Benchmarks for Real-World 3D Object Understanding","created":"2021-10-12","cited_by_count":366,"area":null},{"id":"2302.03169","title":"Data Selection for Language Models via Importance Resampling","created":"2023-02-06","cited_by_count":329,"area":null},{"id":"2106.10934","title":"GRAND: Graph Neural Diffusion","created":"2021-06-21","cited_by_count":258,"area":null},{"id":"2106.10994","title":"BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation","created":"2021-06-21","cited_by_count":195,"area":null},{"id":"2102.06514","title":"Large-Scale Representation Learning on Graphs via Bootstrapping","created":"2021-02-12","cited_by_count":186,"area":null},{"id":"2102.10757","title":"Self-Supervised Learning of Graph Neural Networks: A Unified Review","created":"2021-02-22","cited_by_count":184,"area":null},{"id":"2202.13013","title":"Sign and Basis Invariant Networks for Spectral Graph Representation Learning","created":"2022-02-25","cited_by_count":179,"area":null},{"id":"2106.12484","title":"From Canonical Correlation Analysis to Self-supervised Graph Neural Networks","created":"2021-06-23","cited_by_count":141,"area":null},{"id":"2105.11367","title":"FedScale: Benchmarking Model and System Performance of Federated Learning at Scale","created":"2021-05-24","cited_by_count":136,"area":null},{"id":"2101.09459","title":"Advances and Challenges in Conversational Recommender Systems: A Survey","created":"2021-01-23","cited_by_count":118,"area":null},{"id":"2110.07298","title":"LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5","created":"2021-10-14","cited_by_count":104,"area":null},{"id":"2204.04879","title":"How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision","created":"2022-04-11","cited_by_count":102,"area":null},{"id":"2210.10683","title":"Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP","created":"2022-10-19","cited_by_count":102,"area":null},{"id":"2202.13556","title":"Filter-enhanced MLP is All You Need for Sequential Recommendation","created":"2022-02-28","cited_by_count":96,"area":null},{"id":"2112.02472","title":"Augmentation-Free Self-Supervised Learning on Graphs","created":"2021-12-05","cited_by_count":94,"area":null},{"id":"2409.07825","title":"Deep Multimodal Learning with Missing Modality: A Survey","created":"2024-09-12","cited_by_count":92,"area":null},{"id":"2202.02519","title":"Intent Contrastive Learning for Sequential Recommendation","created":"2022-02-05","cited_by_count":91,"area":null},{"id":"2310.10108","title":"On Generative Agents in Recommendation","created":"2023-10-16","cited_by_count":90,"area":"agents"},{"id":"2111.00197","title":"Backdoor Pre-trained Models Can Transfer to All","created":"2021-10-30","cited_by_count":86,"area":null},{"id":"2202.06200","title":"Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning","created":"2022-02-13","cited_by_count":86,"area":null},{"id":"2402.09834","title":"All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining","created":"2024-02-15","cited_by_count":86,"area":null},{"id":"2106.02466","title":"Graph Barlow Twins: A self-supervised representation learning framework for graphs","created":"2021-06-04","cited_by_count":85,"area":null},{"id":"2110.09443","title":"Beltrami Flow and Neural Diffusion on Graphs","created":"2021-10-18","cited_by_count":84,"area":null},{"id":"2110.14012","title":"Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification","created":"2021-10-26","cited_by_count":80,"area":null},{"id":"2206.10071","title":"BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs","created":"2022-06-21","cited_by_count":80,"area":null},{"id":"2304.11116","title":"Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT","created":"2023-04-10","cited_by_count":80,"area":"lr"},{"id":"2104.09036","title":"Mining Latent Structures for Multimedia Recommendation","created":"2021-04-19","cited_by_count":79,"area":null},{"id":"2310.09881","title":"In-Context Learning with Iterative Demonstration Selection","created":"2023-10-15","cited_by_count":74,"area":null},{"id":"2110.03051","title":"Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation","created":"2021-10-06","cited_by_count":71,"area":null},{"id":"2203.06311","title":"ELLE: Efficient Lifelong Pre-training for Emerging Data","created":"2022-03-12","cited_by_count":70,"area":null},{"id":"2108.06625","title":"Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer","created":"2021-08-14","cited_by_count":69,"area":null},{"id":"2104.07477","title":"Lorentzian Graph Convolutional Networks","created":"2021-04-15","cited_by_count":68,"area":null},{"id":"2302.02914","title":"Energy-based Out-of-Distribution Detection for Graph Neural Networks","created":"2023-02-06","cited_by_count":68,"area":null},{"id":"2409.12740","title":"HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling","created":"2024-09-19","cited_by_count":63,"area":null},{"id":"2202.08057","title":"Understanding and Improving Graph Injection Attack by Promoting Unnoticeability","created":"2022-02-16","cited_by_count":62,"area":null},{"id":"2502.05424","title":"SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptation","created":"2025-02-08","cited_by_count":62,"area":null},{"id":"2104.13030","title":"A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation","created":"2021-04-27","cited_by_count":59,"area":null},{"id":"2108.06479","title":"Contrastive Self-supervised Sequential Recommendation with Robust Augmentation","created":"2021-08-14","cited_by_count":56,"area":null},{"id":"2110.04099","title":"Topology-Imbalance Learning for Semi-Supervised Node Classification","created":"2021-10-08","cited_by_count":56,"area":null},{"id":"2404.13207","title":"STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases","created":"2024-04-19","cited_by_count":55,"area":null},{"id":"2206.10206","title":"Personalized Subgraph Federated Learning","created":"2022-06-21","cited_by_count":54,"area":null},{"id":"2311.02089","title":"LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking","created":"2023-10-25","cited_by_count":53,"area":null},{"id":"2403.19021","title":"IDGenRec: LLM-RecSys Alignment with Textual ID Learning","created":"2024-03-27","cited_by_count":53,"area":null},{"id":"2105.11118","title":"Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads","created":"2021-05-24","cited_by_count":51,"area":null},{"id":"2112.08331","title":"Model Stealing Attacks Against Inductive Graph Neural Networks","created":"2021-12-15","cited_by_count":50,"area":null},{"id":"2404.11343","title":"Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System","created":"2024-04-17","cited_by_count":50,"area":null},{"id":"2502.10050","title":"A Survey on LLM-powered Agents for Recommender Systems","created":"2025-02-14","cited_by_count":50,"area":"agents"},{"id":"2212.01026","title":"Spectral Feature Augmentation for Graph Contrastive Learning and Beyond","created":"2022-12-02","cited_by_count":49,"area":null},{"id":"2305.12268","title":"Patton: Language Model Pretraining on Text-Rich Networks","created":"2023-05-20","cited_by_count":49,"area":null},{"id":"2109.03171","title":"Aspect-Controllable Opinion Summarization","created":"2021-09-07","cited_by_count":47,"area":null},{"id":"2405.20646","title":"LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation","created":"2024-05-31","cited_by_count":47,"area":null},{"id":"2105.08318","title":"Zero-Shot Recommender Systems","created":"2021-05-18","cited_by_count":46,"area":null},{"id":"2206.09140","title":"Certified Graph Unlearning","created":"2022-06-18","cited_by_count":46,"area":"safety"},{"id":"2305.14302","title":"VIP5: Towards Multimodal Foundation Models for Recommendation","created":"2023-05-23","cited_by_count":46,"area":null},{"id":"2402.08831","title":"eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data","created":"2024-02-13","cited_by_count":46,"area":null},{"id":"2105.00522","title":"Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer","created":"2021-05-02","cited_by_count":45,"area":null},{"id":"2212.09034","title":"Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs","created":"2022-12-18","cited_by_count":42,"area":null},{"id":"2103.05872","title":"Sampling methods for efficient training of graph convolutional networks: A survey","created":"2021-03-10","cited_by_count":40,"area":null},{"id":"2104.06828","title":"Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge","created":"2021-04-14","cited_by_count":40,"area":null},{"id":"2110.08450","title":"Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining","created":"2021-10-16","cited_by_count":39,"area":null},{"id":"2206.04726","title":"COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning","created":"2022-06-09","cited_by_count":39,"area":null},{"id":"2210.05801","title":"Linkless Link Prediction via Relational Distillation","created":"2022-10-11","cited_by_count":39,"area":null},{"id":"2312.02443","title":"E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation","created":"2023-12-05","cited_by_count":39,"area":null},{"id":"2309.12858","title":"Diffusion Augmentation for Sequential Recommendation","created":"2023-09-22","cited_by_count":38,"area":"diffusion"},{"id":"2310.18884","title":"Simple and Asymmetric Graph Contrastive Learning without Augmentations","created":"2023-10-29","cited_by_count":38,"area":null},{"id":"2102.05216","title":"VINS: Visual Search for Mobile User Interface Design","created":"2021-02-10","cited_by_count":37,"area":null},{"id":"2109.01165","title":"Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction","created":"2021-09-01","cited_by_count":37,"area":null},{"id":"2108.10521","title":"Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study","created":"2021-08-24","cited_by_count":36,"area":null},{"id":"2405.07406","title":"Machine Unlearning: A Comprehensive Survey","created":"2024-05-13","cited_by_count":35,"area":"safety"},{"id":"2503.02453","title":"Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations","created":"2025-03-04","cited_by_count":35,"area":null},{"id":"2105.13448","title":"Open-world Machine Learning: Applications, Challenges, and Opportunities","created":"2021-05-27","cited_by_count":34,"area":null},{"id":"2111.04130","title":"NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework","created":"2021-11-07","cited_by_count":34,"area":null},{"id":"2310.07365","title":"GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning","created":"2023-10-11","cited_by_count":33,"area":null},{"id":"2408.09698","title":"Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation","created":"2024-08-19","cited_by_count":33,"area":"mllm"},{"id":"2109.08907","title":"Releasing Graph Neural Networks with Differential Privacy Guarantees","created":"2021-09-18","cited_by_count":32,"area":null},{"id":"2405.02774","title":"Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs","created":"2024-05-05","cited_by_count":32,"area":null},{"id":"2210.00102","title":"MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization","created":"2022-09-30","cited_by_count":31,"area":null},{"id":"2212.01096","title":"Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment","created":"2022-12-02","cited_by_count":31,"area":null},{"id":"2203.09978","title":"WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series","created":"2022-03-18","cited_by_count":30,"area":null},{"id":"2204.11046","title":"Decoupled Side Information Fusion for Sequential Recommendation","created":"2022-04-23","cited_by_count":30,"area":null},{"id":"2207.00422","title":"Personalized Showcases: Generating Multi-Modal Explanations for Recommendations","created":"2022-06-30","cited_by_count":30,"area":null},{"id":"2209.07754","title":"On the Robustness of Graph Neural Diffusion to Topology Perturbations","created":"2022-09-16","cited_by_count":30,"area":null},{"id":"2309.07602","title":"Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?","created":"2023-09-14","cited_by_count":30,"area":null},{"id":"2106.04128","title":"Conversational Fashion Image Retrieval via Multiturn Natural Language Feedback","created":"2021-06-08","cited_by_count":29,"area":null},{"id":"2310.00793","title":"Revisiting Link Prediction: A Data Perspective","created":"2023-10-01","cited_by_count":29,"area":null},{"id":"2312.10325","title":"An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention","created":"2023-12-16","cited_by_count":28,"area":null},{"id":"2208.12807","title":"Towards Federated Learning against Noisy Labels via Local Self-Regularization","created":"2022-08-25","cited_by_count":26,"area":null},{"id":"2305.15616","title":"Reversible and irreversible bracket-based dynamics for deep graph neural networks","created":"2023-05-24","cited_by_count":26,"area":null},{"id":"2305.16876","title":"CombLM: Adapting Black-Box Language Models through Small Fine-Tuned Models","created":"2023-05-23","cited_by_count":26,"area":null},{"id":"2310.10121","title":"From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond","created":"2023-10-16","cited_by_count":26,"area":null},{"id":"2410.13765","title":"Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval","created":"2024-10-17","cited_by_count":26,"area":null},{"id":"2206.09166","title":"NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search","created":"2022-06-18","cited_by_count":25,"area":null},{"id":"2303.06337","title":"AutoMLP: Automated MLP for Sequential Recommendations","created":"2023-03-11","cited_by_count":25,"area":null},{"id":"2101.03392","title":"Generate Natural Language Explanations for Recommendation","created":"2021-01-09","cited_by_count":23,"area":null},{"id":"2301.12458","title":"SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking","created":"2023-01-29","cited_by_count":23,"area":null},{"id":"2304.11433","title":"Conditional Denoising Diffusion for Sequential Recommendation","created":"2023-04-22","cited_by_count":23,"area":"diffusion"},{"id":"2106.06165","title":"Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation","created":"2021-06-11","cited_by_count":22,"area":null}],"next_cursor":"100"}