Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language | Paper Citation Record

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This paper cites 2020 , booktitle = Oakland, keywords =.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language 2020 , booktitle = Oakland, keywords =

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Observation e4b1a4de-17f3-4cd7-9071-fa2a8e82ee42 · outbound

This paper cites and Chau, Siu Lun and Burwood, Ryan P.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language and Chau, Siu Lun and Burwood, Ryan P

Reference 2

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This paper cites Attention Is All You Need.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Attention Is All You Need

Reference 3

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

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 4

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This paper cites SciBERT: A Pretrained Language Model for Scientific Text.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language SciBERT: A Pretrained Language Model for Scientific Text

Reference 5

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This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 6

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language , month = feb, year =

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language 1988 , note =

Reference 8

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This paper cites Don't Stop Pretraining: Adapt Language Models to Domains and Tasks.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Reference 9

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This paper cites Nature Machine Intelligence , author =.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Nature Machine Intelligence , author =

Reference 10

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Superlinear , author =

Reference 11

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This paper cites ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 12

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This paper cites ChemBERTa-2: Towards Chemical Foundation Models.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language ChemBERTa-2: Towards Chemical Foundation Models

Reference 13

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This paper cites IBM Research , month = feb, year =.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language IBM Research , month = feb, year =

Reference 14

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This paper cites Gaussian Process Model for Estimating Piecewise Continuous Regression Functions.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Gaussian Process Model for Estimating Piecewise Continuous Regression Functions

Reference 15

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language The Variational Gaussian Process

Reference 16

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This paper cites Can Large Language Models Understand Molecules?.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Can Large Language Models Understand Molecules?

Reference 17

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language ACS Central Science , author =

Reference 18

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This paper cites Google for Developers , file =.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Google for Developers , file =

Reference 19

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This paper cites ChemTEB: Chemical Text Embedding Benchmark, an Overview of Embedding Models Performance & Efficiency on a Specific Domain.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language ChemTEB: Chemical Text Embedding Benchmark, an Overview of Embedding Models Performance & Efficiency on a Specific Domain

Reference 20

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Chemical Science , author =

Reference 21

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This paper cites MoleculeNet: A Benchmark for Molecular Machine Learning.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language MoleculeNet: A Benchmark for Molecular Machine Learning

Reference 22

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Unresolved cited work

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language CHIMIA , author =

Reference 25

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language arXiv.org , author =

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Results in Engineering , author =

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Journal of Cheminformatics , author =

Reference 30

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This paper cites Fine-tuning can cripple your foundation model; preserving features may be the solution.

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Fine-tuning can cripple your foundation model; preserving features may be the solution

Reference 31

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Revisiting

Reference 32

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language How does the task complexity of masked pretraining objectives affect downstream performance?

Reference 33

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Translation between Molecules and Natural Language

Reference 34

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 35

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Journal of Cheminformatics , author =

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Journal of Cheminformatics , author =

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Nougat: Neural Optical Understanding for Academic Documents

Reference 40

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language GLU Variants Improve Transformer

Reference 41

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

Reference 42

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Modern Distributed Data-Parallel Large-Scale Pre-training Strategies For NLP models

Reference 43

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language and Jette, Morris A

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language A Simple Framework for Contrastive Learning of Visual Representations

Reference 45

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language The NT-Xent loss upper bound

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Momentum Contrast for Unsupervised Visual Representation Learning

Reference 47

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning

Reference 48

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language scikit-learn , file =

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language arXiv.org , author =

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Machine Learning , author =

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Proceedings of the 22nd

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language The Journal of Physical Chemistry Letters , author =

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Current Opinion in Chemical Engineering , author =

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Chemical Research in Toxicology , author =

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?

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Reference 68

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Reference 69

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Reference 70

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Reference 79

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Reference 81

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language PhysBERT: A Text Embedding Model for Physics Scientific Literature

Reference 82

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Reference 83

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Reference 84

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Reference 85

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Reference 86

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 87

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Reference 88

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language Nomic Embed: Training a Reproducible Long Context Text Embedder

Reference 89

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Reference 90

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Reference 91

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Reference 92

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Reference 93

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Reference 94

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Reference 96

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language and Leskovec, Jure and Coley, Connor W

Reference 98

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Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language PubMedQA: A Dataset for Biomedical Research Question Answering

Reference 99

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Reference 100

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Reference 101

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