Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining | Paper Citation Record

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This paper cites write newline.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining write newline

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This paper cites The Surprising Power of Graph Neural Networks with Random Node Initialization.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining The Surprising Power of Graph Neural Networks with Random Node Initialization

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This paper cites Self- Supervised Learning From Images With a Joint-Embedding Predictive Architecture.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Self- Supervised Learning From Images With a Joint-Embedding Predictive Architecture

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This paper cites Geom, energy-annotated molecular conformations for property prediction and molecular generation.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Geom, energy-annotated molecular conformations for property prediction and molecular generation

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction

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This paper cites Towards foundational models for molecular learning on large-scale multi-task datasets.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Towards foundational models for molecular learning on large-scale multi-task datasets

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This paper cites Bronstein, and Haggai Maron.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Bronstein, and Haggai Maron

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This paper cites Design of protein-binding proteins from the target structure alone.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Design of protein-binding proteins from the target structure alone

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This paper cites Emerging properties in self-supervised vision transformers.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Emerging properties in self-supervised vision transformers

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This paper cites A simple framework for contrastive learning of visual representations.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining A simple framework for contrastive learning of visual representations

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This paper cites BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding

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This paper cites Elton, Zois Boukouvalas, Mark D.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Elton, Zois Boukouvalas, Mark D

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This paper cites UniCorn : A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining UniCorn : A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning

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This paper cites Fast Graph Representation Learning with PyTorch Geometric.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Fast Graph Representation Learning with PyTorch Geometric

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This paper cites Gemnet: Universal directional graph neural networks for molecules.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Gemnet: Universal directional graph neural networks for molecules

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This paper cites A comprehensive discovery platform for organophosphorus ligands for catalysis.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining A comprehensive discovery platform for organophosphorus ligands for catalysis

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Schoenholz, Patrick F

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Bootstrap your own latent - a new approach to self-supervised learning

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning

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This paper cites Inductive Representation Learning on Large Graphs.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Inductive Representation Learning on Large Graphs

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Masked Autoencoders Are Scalable Vision Learners

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This paper cites Convolutional neural network based on smiles representation of compounds for detecting chemical motif.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Convolutional neural network based on smiles representation of compounds for detecting chemical motif

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining GraphMAE : Self-Supervised Masked Graph Autoencoders

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This paper cites Strategies for Pre-training Graph Neural Networks , February 2020 a.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Strategies for Pre-training Graph Neural Networks , February 2020 a

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Gpt-gnn: Generative pre-training of graph neural networks

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining A fast and high quality multilevel scheme for partitioning irregular graphs

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Kipf and Max Welling

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This paper cites 3D-Mol : A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information , June 2024.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining 3D-Mol : A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information , June 2024

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Rdkit: Open-source cheminformatics http://www.rdkit.org

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining A path towards autonomous machine intelligence

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Augmentation- Free Self-Supervised Learning on Graphs

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Pre-training Molecular Graph Representation with 3D Geometry , May 2022 a

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining A group symmetric stochastic differential equation model for molecule multi-modal pretraining

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Auto3d: Automatic generation of the low-energy 3d structures with ani neural network potentials

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Unresolved cited work

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining MolMix : A Simple Yet Effective Baseline for Multimodal Molecular Representation Learning

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Unresolved cited work

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Graph neural networks can (often) count substructures

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining BYOL works even without batch statistics

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Self- Supervised Graph Transformer on Large-Scale Molecular Data

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining u tt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert M \

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Graph-level Representation Learning with Joint-Embedding Predictive Architectures , January 2025

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining a rk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan G \

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining InfoGraph : Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization , January 2020

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Dyer, R \'e mi Munos, Petar Veli c kovi \'c , and Michal Valko

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Attention is All you Need

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Evaluating self-supervised learning for molecular graph embeddings

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Smiles-bert: large scale unsupervised pre-training for molecular property prediction

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Automated 3d pre-training for molecular property prediction

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Molecular contrastive learning of representations via graph neural networks

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining The reduction of a graph to canonical form and the algebra which appears therein

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining The mechanism of prediction head in non-contrastive self-supervised learning

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Wigh, Jonathan M

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining a ger, Niklas Kemper, Leon Hetzel, Johanna Sommer, and Stephan G \

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Moleculenet: a benchmark for molecular machine learning

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Unresolved cited work

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Self- Supervised Representation Learning via Latent Graph Prediction

Reference 59

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining How Powerful are Graph Neural Networks ?, February 2019

Reference 60

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Self-supervised graph-level representation learning with local and global structure

Reference 61

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Graph contrastive learning automated

Reference 62

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Graph Contrastive Learning with Augmentations , April 2021 b

Reference 63

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Multimodal Molecular Pretraining via Modality Blending

Reference 64

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Deep sets

Reference 65

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

Reference 66

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Motif-based Graph Self-Supervised Learning for Molecular Property Prediction

Reference 67

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Unified 2d and 3d pre-training of molecular representations

Reference 68

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Coley, Yizhou Sun, and Wei Wang

Reference 69

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining @esa (Ref

Reference 70

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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining Unresolved cited work

Reference 71

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This paper cites 3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information.

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining 3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information

Reference 72

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