{"list":"other","total":53,"rows":[{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"2302.05543","title":"Adding Conditional Control to Text-to-Image Diffusion Models","created":"2023-02-10","cited_by_count":5503},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1612.00796","title":"Overcoming catastrophic forgetting in neural networks","created":"2016-12-02","cited_by_count":4787},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1902.00751","title":"Parameter-Efficient Transfer Learning for NLP","created":"2019-02-02","cited_by_count":4236},{"id":"2101.00190","title":"Prefix-Tuning: Optimizing Continuous Prompts for Generation","created":"2021-01-01","cited_by_count":3915},{"id":"2104.08691","title":"The Power of Scale for Parameter-Efficient Prompt Tuning","created":"2021-04-18","cited_by_count":3795},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"1606.04080","title":"Matching Networks for One Shot Learning","created":"2016-06-13","cited_by_count":2255},{"id":"1703.00564","title":"MoleculeNet: A Benchmark for Molecular Machine Learning","created":"2017-03-02","cited_by_count":1051},{"id":"1905.12265","title":"Strategies for Pre-training Graph Neural Networks","created":"2019-05-29","cited_by_count":971},{"id":"2004.05439","title":"Meta-Learning in Neural Networks: A Survey","created":"2020-04-11","cited_by_count":950},{"id":"2110.04366","title":"Towards a Unified View of Parameter-Efficient Transfer Learning","created":"2021-10-08","cited_by_count":872},{"id":"2005.00247","title":"AdapterFusion: Non-Destructive Task Composition for Transfer Learning","created":"2020-05-01","cited_by_count":788},{"id":"2303.16199","title":"LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention","created":"2023-03-28","cited_by_count":781},{"id":"2103.10385","title":"GPT Understands, Too","created":"2021-03-18","cited_by_count":716},{"id":"2205.10803","title":"GraphMAE: Self-Supervised Masked Graph Autoencoders","created":"2022-05-22","cited_by_count":448},{"id":"1802.01483","title":"Explicit Inductive Bias for Transfer Learning with Convolutional Networks","created":"2018-02-05","cited_by_count":227},{"id":"1805.10002","title":"Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning","created":"2018-05-25","cited_by_count":211},{"id":"2106.06130","title":"ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction","created":"2021-06-11","cited_by_count":204},{"id":"2102.10757","title":"Self-Supervised Learning of Graph Neural Networks: A Unified Review","created":"2021-02-22","cited_by_count":184},{"id":"2203.06904","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","created":"2022-03-14","cited_by_count":180},{"id":"2303.15647","title":"Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning","created":"2023-03-28","cited_by_count":173},{"id":"2205.06743","title":"A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities","created":"2022-05-13","cited_by_count":163},{"id":"2110.00987","title":"Motif-based Graph Self-Supervised Learning for Molecular Property Prediction","created":"2021-10-03","cited_by_count":159},{"id":"2110.04126","title":"3D Infomax improves GNNs for Molecular Property Prediction","created":"2021-10-08","cited_by_count":146},{"id":"2210.17451","title":"AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning","created":"2022-10-31","cited_by_count":137},{"id":"2206.00133","title":"Pre-training via Denoising for Molecular Property Prediction","created":"2022-05-31","cited_by_count":124},{"id":"1905.01436","title":"Edge-labeling Graph Neural Network for Few-shot Learning","created":"2019-05-04","cited_by_count":114},{"id":"1611.03199","title":"Low Data Drug Discovery with One-shot Learning","created":"2016-11-10","cited_by_count":91},{"id":"2102.07916","title":"Few-Shot Graph Learning for Molecular Property Prediction","created":"2021-02-16","cited_by_count":81},{"id":"2309.13625","title":"GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph","created":"2023-09-24","cited_by_count":69},{"id":"2106.04509","title":"MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph","created":"2021-06-05","cited_by_count":65},{"id":"2109.11730","title":"GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction","created":"2021-09-24","cited_by_count":61},{"id":"2310.05174","title":"GSLB: The Graph Structure Learning Benchmark","created":"2023-10-08","cited_by_count":50},{"id":"2310.14753","title":"Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules","created":"2023-10-23","cited_by_count":49},{"id":"2107.07994","title":"Property-Aware Relation Networks for Few-Shot Molecular Property Prediction","created":"2021-07-16","cited_by_count":41},{"id":"2305.18405","title":"Dink-Net: Neural Clustering on Large Graphs","created":"2023-05-28","cited_by_count":38},{"id":"2310.06234","title":"Efficient Adaptation of Large Vision Transformer via Adapter Re-Composing","created":"2023-10-10","cited_by_count":35},{"id":"2206.03364","title":"KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction","created":"2022-06-02","cited_by_count":31},{"id":"2305.09481","title":"Context-enriched molecule representations improve few-shot drug discovery","created":"2023-04-24","cited_by_count":24},{"id":"2306.07812","title":"Automated 3D Pre-Training for Molecular Property Prediction","created":"2023-06-13","cited_by_count":24},{"id":"2309.15123","title":"Uncovering Neural Scaling Laws in Molecular Representation Learning","created":"2023-09-15","cited_by_count":23},{"id":"2402.07225","title":"Rethinking Graph Masked Autoencoders through Alignment and Uniformity","created":"2024-02-11","cited_by_count":22},{"id":"2202.02989","title":"Graph Self-supervised Learning with Accurate Discrepancy Learning","created":"2022-02-07","cited_by_count":21},{"id":"2205.02708","title":"Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction","created":"2022-05-05","cited_by_count":21},{"id":"2302.13693","title":"Learning Topology-Specific Experts for Molecular Property Prediction","created":"2023-02-27","cited_by_count":15},{"id":"2306.16780","title":"Graph Sampling-based Meta-Learning for Molecular Property Prediction","created":"2023-06-29","cited_by_count":10},{"id":"2402.12220","title":"Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic Forgetting","created":"2024-02-19","cited_by_count":9},{"id":"2408.04400","title":"DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization","created":"2024-08-08","cited_by_count":7},{"id":"2403.13830","title":"Bridging Text and Molecule: A Survey on Multimodal Frameworks for Molecule","created":"2024-03-07","cited_by_count":4}],"next_cursor":null}