Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T13:41:14.648462Z
Paper Citation Record · LEDGER
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases
As of 14 August 2026, this Paper Citation Record lists 87 of 87 outbound references and 3 inbound Pith citation observations for arXiv:2501.16271.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-31T18:32:17.250074Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
87 of 87 outbound references displayed
- verified exact20
- verified fuzzy12
- unresolved53
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- malformed identifier0
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External citation measurements
1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
Observation ac7fd25c-2b30-4359-a080-7b6693b22915 · outbound
This paper cites Neural additive models: Interpretable machine learning with neural nets.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Neural additive models: Interpretable machine learning with neural nets
Reference 1
- Resolution
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Source-reported events for the cited work
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Observation 3475c5d2-510c-4360-acba-b89a48cc127e · outbound
This paper cites Optuna: A next-generation hyperparameter optimization framework.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Optuna: A next-generation hyperparameter optimization framework
Reference 2
- Resolution
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Source-reported events for the cited work
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Observation 71424d93-877e-4492-bd4c-149ec4013389 · outbound
This paper cites GitHub - BioMachineLearning /openpom: Replication of the Principal Odor Map paper by Lee et al (2022).
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases GitHub - BioMachineLearning /openpom: Replication of the Principal Odor Map paper by Lee et al (2022)
Reference 3
- Resolution
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Source-reported events for the cited work
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Observation 12fb8b16-52ef-42aa-b545-10b6fc96c09e · outbound
This paper cites Smellosophy.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Smellosophy
Reference 4
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4f40e922-4d7e-4d71-8c34-2dfa96225aa2 · outbound
This paper cites More than meets the AI : The possibilities and limits of machine learning in olfaction.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases More than meets the AI : The possibilities and limits of machine learning in olfaction
Reference 5
- Resolution
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Source-reported events for the cited work
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Observation cebf0c3c-6b79-4df3-878d-4735c7653c41 · outbound
This paper cites Relational inductive biases, deep learning, and graph networks.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Relational inductive biases, deep learning, and graph networks
Reference 6
- Resolution
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Source-reported events for the cited work
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Observation 2d8ef480-34e7-41a3-ab8d-3eda915ac023 · outbound
This paper cites Representation Learning: A Review and New Perspectives.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Representation Learning: A Review and New Perspectives
Reference 7
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation efd71d9b-eaa6-4686-8038-a35b42f743b1 · outbound
This paper cites Algorithms for hyper-parameter optimization.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Algorithms for hyper-parameter optimization
Reference 8
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 516c7b87-b395-4636-b4d6-0ce521a34811 · outbound
This paper cites Experiment tracking with weights and biases, 2020.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Experiment tracking with weights and biases, 2020
Reference 9
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation edf1f6dc-6a2d-46c9-89d4-8a252bd3e111 · outbound
This paper cites GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
Reference 10
- Resolution
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Source-reported events for the cited work
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Observation bfe821ea-6034-4251-959c-88552219b92f · outbound
This paper cites How Attentive are Graph Attention Networks?.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases How Attentive are Graph Attention Networks?
Reference 11
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cdea1f7f-5f27-45cc-bb93-2cdecd00dd73 · outbound
This paper cites Humans can discriminate more than 1 trillion olfactory stimuli.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Humans can discriminate more than 1 trillion olfactory stimuli
Reference 12
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ab156635-6317-46c2-a57b-5074c5c48c90 · outbound
This paper cites Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting
Reference 13
- Resolution
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Source-reported events for the cited work
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Observation 2c420b04-5bb7-480b-aff4-58bc67be471a · outbound
This paper cites Pyrfume: A window to the world’s olfactory data.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Pyrfume: A window to the world’s olfactory data
Reference 14
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 05e63088-5742-433e-b4a6-b0b417253857 · outbound
This paper cites Open catalyst 2020 (oc20) dataset and community challenges.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Open catalyst 2020 (oc20) dataset and community challenges
Reference 15
- Resolution
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Observation 454b3f88-ad66-4ca9-aa1c-832a2f7d3533 · outbound
This paper cites XGBoost: A Scalable Tree Boosting System.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases XGBoost: A Scalable Tree Boosting System
Reference 16
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Observation 6724a0d5-950d-4281-bc3e-2f19c69df6cb · outbound
This paper cites ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
Reference 17
- Resolution
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Unavailable: canonical work link unavailable.
Observation 960682c1-3f79-457f-8988-cc8dd7d4611a · outbound
This paper cites Insect odorscapes: From plant volatiles to natural olfactory scenes.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Insect odorscapes: From plant volatiles to natural olfactory scenes
Reference 18
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 76d247f7-19be-4529-83d4-a038048d10ef · outbound
This paper cites Principal Neighbourhood Aggregation for Graph Nets.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Principal Neighbourhood Aggregation for Graph Nets
Reference 19
- Resolution
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Source-reported events for the cited work
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This paper cites The new european union flavouring regulation and its impact on essential oils: production of natural flavouring ingredients and maximum levels of restricted substances.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases The new european union flavouring regulation and its impact on essential oils: production of natural flavouring ingredients and maximum levels of restricted substances
Reference 20
- Resolution
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Observation b6a5bd2f-60ef-4961-a914-8eecbe23a081 · outbound
This paper cites Expansive linguistic representations to predict interpretable odor mixture discriminability.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Expansive linguistic representations to predict interpretable odor mixture discriminability
Reference 21
- Resolution
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Observation 69fb5cd9-5cdd-46c5-aee2-c67896a0bc17 · outbound
This paper cites Translation between Molecules and Natural Language.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Translation between Molecules and Natural Language
Reference 22
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Observation bb58e509-f465-47e3-abff-b740dcf0c148 · outbound
This paper cites Bohb: Robust and efficient hyperparameter optimization at scale.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Bohb: Robust and efficient hyperparameter optimization at scale
Reference 23
- Resolution
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Observation 21ed68ad-a7a4-4cc5-bb7e-98ba940edcf1 · outbound
This paper cites Fast Graph Representation Learning with PyTorch Geometric.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Fast Graph Representation Learning with PyTorch Geometric
Reference 24
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This paper cites Using rule-based labels for weak supervised learning: A ChemNet for transferable chemical property prediction.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Using rule-based labels for weak supervised learning: A ChemNet for transferable chemical property prediction
Reference 25
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This paper cites Automatic chemical design using a data-driven continuous representation of molecules.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Automatic chemical design using a data-driven continuous representation of molecules
Reference 26
- Resolution
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Observation fa4feb6b-b73b-4bd6-8f25-609f54d84f04 · outbound
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Chemprop: A machine learning package for chemical property prediction
Reference 27
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Observation 42e4f54d-4e0b-434c-8134-544eb47f5e4e · outbound
This paper cites SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery
Reference 28
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Open graph benchmark: Datasets for machine learning on graphs
Reference 29
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Reference 30
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases IFRA transparency list, 2024
Reference 31
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Transformers are graph neural networks
Reference 32
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Observation b227a22c-b0b3-49e1-82ce-ba26266204a6 · outbound
This paper cites Energy efficient artificial olfactory system with integrated sensing and computing capabilities for food spoilage detection.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Energy efficient artificial olfactory system with integrated sensing and computing capabilities for food spoilage detection
Reference 33
- Resolution
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Predicting human olfactory perception from chemical features of odor molecules
Reference 34
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This paper cites GitHub - bp-kelley/descriptastorus: Descriptor computation (chemistry) and (optional) storage for machine learning , 2024.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases GitHub - bp-kelley/descriptastorus: Descriptor computation (chemistry) and (optional) storage for machine learning , 2024
Reference 35
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This paper cites Predicting odor pleasantness from odorant structure: pleasantness as a reflection of the physical world.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Predicting odor pleasantness from odorant structure: pleasantness as a reflection of the physical world
Reference 36
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Adam: A Method for Stochastic Optimization
Reference 37
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Siamese neural networks for one-shot image recognition
Reference 38
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This paper cites Predicting human olfactory perception from activities of odorant receptors.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Predicting human olfactory perception from activities of odorant receptors
Reference 39
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Observation f5e1b608-0748-4266-9203-cf7fbee22774 · outbound
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases RDKit : Open-source cheminformatics software, 2022
Reference 40
- Resolution
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Observation b429d840-e320-4b6b-9a98-1294f6f45eee · outbound
This paper cites Prediction models for the pleasantness of binary mixtures in olfaction.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Prediction models for the pleasantness of binary mixtures in olfaction
Reference 41
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Observation da704ec2-4430-433d-966d-c97894d27986 · outbound
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases A principal odor map unifies diverse tasks in olfactory perception
Reference 42
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Molecule Attention Transformer
Reference 43
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This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Reference 44
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Modern hierarchical, agglomerative clustering algorithms
Reference 45
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Observation b92311e8-3f9f-411d-a05c-59fccea76524 · outbound
This paper cites Molecular property prediction and molecular design using a supervised grammar variational autoencoder.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Molecular property prediction and molecular design using a supervised grammar variational autoencoder
Reference 46
- Resolution
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Observation 53b28349-4b96-403c-a24c-0e28b3edad47 · outbound
This paper cites An integrated model of intensity and quality of odor mixtures.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases An integrated model of intensity and quality of odor mixtures
Reference 47
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Observation d2f542cb-97d9-445e-9cf2-41fcf882a697 · outbound
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases FiLM: Visual Reasoning with a General Conditioning Layer
Reference 49
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Theory, Analysis, and Best Practices for Sigmoid Self-Attention
Reference 50
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Recipe for a general, powerful, scalable graph transformer
Reference 51
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Observation e17fd2a4-5708-452f-8c43-f2c52812651f · outbound
This paper cites A measure of smell enables the creation of olfactory metamers.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases A measure of smell enables the creation of olfactory metamers
Reference 52
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Observation 47534dc8-b34a-4bb9-ae20-010fc10cae37 · outbound
This paper cites Large-scale chemical language representations capture molecular structure and properties.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Large-scale chemical language representations capture molecular structure and properties
Reference 53
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Observation ec1b1054-1d6e-456b-a6c4-563d11db4494 · outbound
This paper cites Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
Reference 54
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Observation 37defcb4-fb20-4fce-9995-e7a1a6cc077c · outbound
This paper cites Evaluating attribution for graph neural networks.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Evaluating attribution for graph neural networks
Reference 55
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 6bedc27f-de5c-429b-9053-821f770554c6 · outbound
This paper cites A gentle introduction to graph neural networks.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases A gentle introduction to graph neural networks
Reference 56
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Observation 6b36e32c-1221-439d-8a07-2dc3ac266d7e · outbound
This paper cites On the unpredictability of odor.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases On the unpredictability of odor
Reference 57
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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation f5d565e0-e8e9-435d-bb5c-2bd578566115 · outbound
This paper cites Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets
Reference 58
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Source-reported events for the cited work
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Observation c16dd7fa-a3b5-4fa3-94bd-8985cf5482ee · outbound
This paper cites Optimizing learning across multimodal transfer features for modeling olfactory perception.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Optimizing learning across multimodal transfer features for modeling olfactory perception
Reference 59
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation b6169a03-bd6d-48e9-b917-147259dd4b6a · outbound
This paper cites From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Reference 60
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Unavailable: canonical work link unavailable.
Observation 341cf463-4ef8-48a2-aeb5-e42c994026a6 · outbound
This paper cites Odor Descriptor Understanding through Prompting.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Odor Descriptor Understanding through Prompting
Reference 61
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 62753f38-f7fa-494d-8e7e-dccf3267a0e1 · outbound
This paper cites Olfactory Label Prediction on Aroma-Chemical Pairs.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Olfactory Label Prediction on Aroma-Chemical Pairs
Reference 62
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 75869890-d713-4fd6-a794-0a36b6d93104 · outbound
This paper cites Predicting odor perceptual similarity from odor structure.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Predicting odor perceptual similarity from odor structure
Reference 63
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4ed1fba3-991c-4e49-a616-7a8220a16578 · outbound
This paper cites SmellSpace : An odor-based social network as a platform for collecting olfactory perceptual data.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases SmellSpace : An odor-based social network as a platform for collecting olfactory perceptual data
Reference 64
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 20ce9041-4dba-4535-b80e-a4a30d857361 · outbound
This paper cites On deep set learning and the choice of aggregations.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases On deep set learning and the choice of aggregations
Reference 65
- Resolution
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- doi, observed 2026-08-10T13:41:14.779080Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 2a4dd007-8b44-431b-bfb8-e5c1b7d950cc · outbound
This paper cites Digitizing the chemical senses: Possibilities & pitfalls.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Digitizing the chemical senses: Possibilities & pitfalls
Reference 66
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 00405959-7db8-47ce-ae48-d16625e8942e · outbound
This paper cites Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS
Reference 67
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 000b90fd-282f-43ac-8866-39047f5bd70c · outbound
This paper cites Ranking over Regression for Bayesian Optimization and Molecule Selection.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Ranking over Regression for Bayesian Optimization and Molecule Selection
Reference 68
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 45195f45-a5c4-46db-a901-56b80761e3c6 · outbound
This paper cites DeepNose : Using artificial neural networks to represent the space of odorants.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases DeepNose : Using artificial neural networks to represent the space of odorants
Reference 69
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 9f27b46b-0b5a-4fb0-b991-798e0477c6ff · outbound
This paper cites Graph Attention Networks.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Graph Attention Networks
Reference 70
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 45335e09-5eba-4df8-841a-d5ba660dea0f · outbound
This paper cites Random forests: A machine learning methodology to highlight the volatile organic compounds involved in olfactory perception.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Random forests: A machine learning methodology to highlight the volatile organic compounds involved in olfactory perception
Reference 71
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 3937f6fa-2fe5-4c75-a34f-98a85bc88265 · outbound
This paper cites Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St \'e fan J.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St \'e fan J
Reference 72
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f2ecc708-6d87-4810-b6c2-22141eabc2b6 · outbound
This paper cites Chemical-Reaction-Aware Molecule Representation Learning.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Chemical-Reaction-Aware Molecule Representation Learning
Reference 73
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4d54e5f0-a96a-4b08-87c7-f6d22020cc6e · outbound
This paper cites Smiles-bert: Large scale unsupervised pre-training for molecular property prediction.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Smiles-bert: Large scale unsupervised pre-training for molecular property prediction
Reference 74
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bad7f0f1-dd54-4607-bbf1-87ea0e5b9faf · outbound
This paper cites A deep learning and digital archaeology approach for mosquito repellent discovery.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases A deep learning and digital archaeology approach for mosquito repellent discovery
Reference 75
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation d2fef455-23a0-433c-a090-432ae7d71c56 · outbound
This paper cites SMILES , a chemical language and information system.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases SMILES , a chemical language and information system
Reference 76
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a4e1f795-1f1c-4be2-808a-8bc45678460e · outbound
This paper cites Perceptual convergence of multi-component mixtures in olfaction implies an olfactory white.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Perceptual convergence of multi-component mixtures in olfaction implies an olfactory white
Reference 77
- Resolution
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- doi, observed 2026-08-10T13:41:14.713844Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 82ddd067-17e9-4494-8aed-9fcec21b2166 · outbound
This paper cites Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking
Reference 78
- Resolution
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- doi, observed 2026-08-10T13:41:14.703033Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 8f69c7d0-cac6-4fe6-ad18-953bd3ce7030 · outbound
This paper cites Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism
Reference 79
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b07ca07e-fd01-4a78-b2b2-b612a84e25e3 · outbound
This paper cites Analyzing learned molecular representations for property prediction.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Analyzing learned molecular representations for property prediction
Reference 80
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 84ad6df1-0d64-4469-b214-c19f886d8818 · outbound
This paper cites Do transformers really perform badly for graph representation? In Thirty-Fifth Conference on Neural Information Processing Systems, 2021.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Do transformers really perform badly for graph representation? In Thirty-Fifth Conference on Neural Information Processing Systems, 2021
Reference 81
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 433bb2b1-2888-43ae-bdc5-07985c9bc462 · outbound
This paper cites Deep Sets.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Deep Sets
Reference 82
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dc4eebb3-0a28-4c40-ab21-8214caab31c8 · outbound
This paper cites MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling
Reference 83
- Resolution
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- local_arxiv, observed 2026-08-10T13:41:14.926804Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation c6784844-3693-4363-b55f-dc01b4d012f3 · outbound
This paper cites A deep position-encoding model for predicting olfactory perception from molecular structures and electrostatics.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases A deep position-encoding model for predicting olfactory perception from molecular structures and electrostatics
Reference 84
- Resolution
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- doi, observed 2026-08-10T13:41:14.684862Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation e70e062d-f2fe-4f45-b4bd-69ee782a9fc0 · outbound
This paper cites write newline.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases write newline
Reference 85
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ce66d0d3-2a5e-49dd-b662-33853f45a456 · outbound
This paper cites @esa (Ref.
Reference 86
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1cc1a19c-c58d-4e5c-a832-c9ea2cbd3685 · outbound
This paper cites an unresolved cited work.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases Unresolved cited work
Reference 87
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f93de921-0f99-4388-85f0-e6ece21446f7 · outbound
This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases PyTorch: An Imperative Style, High-Performance Deep Learning Library
Reference 88
- Resolution
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Source-reported events for the cited work
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Pith citing papers
Observation ef5f1495-eda3-453d-b299-aab6a2634b3c · inbound
Artificial Intelligence for Food Innovation cites this paper.
Artificial Intelligence for Food Innovation From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases
Reference 79
- Resolution
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- arxiv_id, observed 2026-05-18T13:36:24.692395Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation f62dc8e0-fb00-4cf4-9170-74baaa98be27 · inbound
Machine learning for smell: Ordinal odor strength prediction of molecular perfumery components cites this paper.
Machine learning for smell: Ordinal odor strength prediction of molecular perfumery components From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases
Reference 4
- Resolution
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- arxiv_id, observed 2026-05-21T18:34:17.987777Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 2249da60-7368-4b68-89a6-0a6c546ed90f · inbound
Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction cites this paper.
Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases
Reference 42
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.