Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks | Paper Citation Record

Observation fdd2f264-e713-4ecc-a805-a541387c8870 · outbound

This paper cites Exploring the Limits of Large Scale Pre-training.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Exploring the Limits of Large Scale Pre-training

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:06.827148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c05f761-d0e2-4959-a44b-e57c05670dd6 · outbound

This paper cites LC-PFN includes its own normalization method for y-values, enabling it to predict learning curves across various ranges and directions.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks LC-PFN includes its own normalization method for y-values, enabling it to predict learning curves across various ranges and directions

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:15.723117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e0439754-151a-43f1-b06a-f278a1964b61 · outbound

This paper cites an unresolved cited work.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:01:15.543246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 77b3b307-14c7-4a15-b40f-5f22e5ea9932 · outbound

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

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:07.639110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb18a8bd-7f8d-4043-b2f9-b52da951dc1a · outbound

This paper cites The Llama 3 Herd of Models.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks The Llama 3 Herd of Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:07.863181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2d8515e-2c6b-4839-8602-98daed0520dc · outbound

This paper cites Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:08.180763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1382cf42-2a8d-4721-a172-7a1bbde7e19e · outbound

This paper cites Scaling Laws for Neural Machine Translation.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Scaling Laws for Neural Machine Translation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:08.302824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cd22627-902f-417d-bd1c-ff5840d1d0c4 · outbound

This paper cites A., Duh, K., and Kaplan, J.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks A., Duh, K., and Kaplan, J

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:18.373051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f9d6c398-719c-4519-86e5-51c99222c00c · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:08.309768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1196ffd-17e5-4607-9739-8ec17f15943e · outbound

This paper cites Training Compute-Optimal Large Language Models.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Training Compute-Optimal Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:08.318171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9638e76d-ca03-4e52-8dfc-14dde6a80ae7 · outbound

This paper cites TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:08.424671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6a147d3-b88b-4145-8350-0c0cbf31437b · outbound

This paper cites Bayesian Active Learning for Classification and Preference Learning.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Bayesian Active Learning for Classification and Preference Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:08.593177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05738292-c67b-4e03-94db-251f250f987a · outbound

This paper cites Scaling Laws for Neural Language Models.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Scaling Laws for Neural Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:08.900521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 185d4cf6-1f3f-454d-920c-238f96a17562 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Adam: A Method for Stochastic Optimization

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:09.077805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ced9f3ce-ce25-4a6a-bd1d-a94109fe6646 · outbound

This paper cites Big transfer (bit): General vi- sual representation learning.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Big transfer (bit): General vi- sual representation learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:18.029104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 917e5e46-da28-41c3-9087-494e22d20220 · outbound

This paper cites Learning Curves for Decision Making in Supervised Machine Learning: A Survey.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Learning Curves for Decision Making in Supervised Machine Learning: A Survey

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:09.624853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e297b8a-b8e3-4ea8-b205-ee52f7c2d91a · outbound

This paper cites Deep double descent: Where bigger models and more data hurt.Journal of Statistical Mechan- ics: Theory and Experiment, 2021(12):124003,.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Deep double descent: Where bigger models and more data hurt.Journal of Statistical Mechan- ics: Theory and Experiment, 2021(12):124003,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:17.852426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 41ac1026-cf3f-4f01-a04e-f2fa094152aa · outbound

This paper cites In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:10.039009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63f008db-3ba3-4339-9a0d-85137d6cff62 · outbound

This paper cites Scaling Laws for Deep Learning.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Scaling Laws for Deep Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:10.240133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f304bba6-12a5-4e57-9755-0e607ee82ca7 · outbound

This paper cites A Constructive Prediction of the Generalization Error Across Scales.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks A Constructive Prediction of the Generalization Error Across Scales

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:10.422809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28fdb080-4af9-49d4-a6ce-036d9a6149b1 · outbound

This paper cites Sharma, U.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Sharma, U

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:10.601654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47003c76-beec-4893-bb10-ac69546a5f06 · outbound

This paper cites Freeze-Thaw Bayesian Optimization.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Freeze-Thaw Bayesian Optimization

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:10.758299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dceb799-15c7-4d3a-82f3-978fb371e9d4 · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:10.912570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a10f4bca-2e6b-4766-bb59-07f1ad731e60 · outbound

This paper cites LaMDA: Language Models for Dialog Applications.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks LaMDA: Language Models for Dialog Applications

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:11.120689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09475916-3935-4c85-bd93-a8a7358fb0bc · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks LLaMA: Open and Efficient Foundation Language Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:11.277616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 236a4a35-7656-44fe-bd5b-71517a8e2383 · outbound

This paper cites LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series Forecasting.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series Forecasting

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:11.415372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2a2197e-cc72-41fe-ada7-cf7a83c95839 · outbound

This paper cites an unresolved cited work.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:01:17.690861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9f92c4b2-651f-4390-85aa-9f464995aaff · outbound

This paper cites Alabdulmohsin et al.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Alabdulmohsin et al

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:17.515798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 64b6c894-f62b-4144-94e0-84e1bcc64334 · outbound

This paper cites point estimates.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks point estimates

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:17.332057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f8c2a3c5-2cc9-4d77-abc9-588e68cd5c2d · outbound

This paper cites For each downstream task, the benchmark provides 18 scaling law variations (2 model sizes × 3 model types × 3 few-shot settings), resulting in a total of 72 scaling laws.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks For each downstream task, the benchmark provides 18 scaling law variations (2 model sizes × 3 model types × 3 few-shot settings), resulting in a total of 72 scaling laws

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:17.158123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ba26bcd2-b287-4b0f-8faa-40350071b5bb · outbound

This paper cites an unresolved cited work.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:01:17.011746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 56f86d2c-f5d5-4840-b3ed-9309e333a37c · outbound

This paper cites nanoGPT-Bench (Nano) is a benchmark introduced in (Kadra et al., 2023), which evaluates the performance of nanoGPT trained on the OpenWebText dataset (Gokaslan et al.,.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks nanoGPT-Bench (Nano) is a benchmark introduced in (Kadra et al., 2023), which evaluates the performance of nanoGPT trained on the OpenWebText dataset (Gokaslan et al.,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:16.849019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7baf59dc-cabc-4b67-bece-2bdac6acd460 · outbound

This paper cites Both the final and best performance are reported, resulting in a total of 24 scaling laws.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Both the final and best performance are reported, resulting in a total of 24 scaling laws

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:16.657629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation aa110da6-e63b-4d61-9c9c-0027d683f000 · outbound

This paper cites In these scaling laws, the x-axis represents either the model embedding size or the number of observed examples, while the y-axis reflects the test error or cross-entropy test loss.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks In these scaling laws, the x-axis represents either the model embedding size or the number of observed examples, while the y-axis reflects the test error or cross-entropy test loss

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:16.462680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f204a248-9cbb-44fa-be96-b1a7086c7da8 · outbound

This paper cites an unresolved cited work.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:01:16.261718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6310bd41-068a-4b09-897c-74ec254e7c83 · outbound

This paper cites Hyperparameters, including those of the neural network used as the basis function for BLR, are tuned via marginal log-likelihood.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Hyperparameters, including those of the neural network used as the basis function for BLR, are tuned via marginal log-likelihood

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:16.053455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e85c61cf-b281-4ab8-bbca-579c163d59cb · outbound

This paper cites As shown in Table 12, BLR models with predefined basis functions exhibit significant performance degradation.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks As shown in Table 12, BLR models with predefined basis functions exhibit significant performance degradation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:15.877690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 85ba6b84-2a41-499d-abb4-11e634c12ae8 · outbound

This paper cites an unresolved cited work.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:01:15.381096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 454c9c9a-eb8d-4abe-8a6d-e60a2ae5e77a · outbound

This paper cites an unresolved cited work.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:01:15.150691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 76ff4ed8-7639-45fb-b855-d520eb53432b · outbound

This paper cites an unresolved cited work.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:01:14.988379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e9d365ba-7a9f-45ae-9835-eeb2b9dbf2e8 · outbound

This paper cites an unresolved cited work.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:01:14.799387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8e20e35e-b20b-40e6-a531-448f380a0643 · outbound

This paper cites an unresolved cited work.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:01:14.648193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 55d78090-472b-4055-b782-1d35016fc46a · outbound

This paper cites an unresolved cited work.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:01:14.509419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 74a19e3d-2831-4e03-b995-85aa163ea3ed · outbound

This paper cites an unresolved cited work.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:01:14.319633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation da1cbe46-0efa-4a87-89f1-2f9e62c76555 · outbound

This paper cites Transformers Can Do Bayesian Inference.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Transformers Can Do Bayesian Inference

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:09.751664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89758209-cf53-49f0-a7df-eb0e457a1ad2 · outbound

This paper cites Bojar, O., Buck, C., Federmann, C., Haddow, B., Koehn, P., Leveling, J., Monz, C., Pecina, P., Post, M., Saint- Amand, H., et al.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Bojar, O., Buck, C., Federmann, C., Haddow, B., Koehn, P., Leveling, J., Monz, C., Pecina, P., Post, M., Saint- Amand, H., et al

Reference 2006

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:18.760694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1579d161-20ba-49d2-b4ef-531731c92ff8 · outbound

This paper cites Pre- dicting accuracy on large datasets from smaller pilot data.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Pre- dicting accuracy on large datasets from smaller pilot data

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:18.206556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d07133d3-8122-4490-bc16-2b2065132a5e · outbound

This paper cites and Lane, I.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks and Lane, I

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:01:18.548458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 444f3cbc-131b-4dd7-a666-013374b0900c · outbound

This paper cites Language Models are Few-Shot Learners.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Language Models are Few-Shot Learners

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:07.281201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab99f2b2-7929-4bd2-afee-3fa4de63f9c4 · outbound

This paper cites A Survey on In-context Learning.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks A Survey on In-context Learning

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:07.715248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cce7a69-a793-481a-bb78-c776ecbb5e0a · outbound

This paper cites How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?

Reference 2017

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:01:14.084310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f5939238-4506-4893-a364-d953de9fb331 · outbound

This paper cites Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation

Reference 2018

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:01:13.793491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b2687651-7b3a-4c08-af57-a1711c93a170 · outbound

This paper cites Learning generative visual models from few training examples: An incremen- tal bayesian approach tested on 101 object categories.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Learning generative visual models from few training examples: An incremen- tal bayesian approach tested on 101 object categories

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:08.043296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 282fcb84-bb5a-4003-a40f-89c1026087ec · outbound

This paper cites Broken Neural Scaling Laws.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Broken Neural Scaling Laws

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:07.344212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c15ca9f-ddbb-4021-a0c8-fc0b51a2fc80 · outbound

This paper cites GPT-4 Technical Report.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks GPT-4 Technical Report

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:06.967498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac97fa5b-1e38-48a6-9fe9-66199ed4dad7 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:07.078889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb8821e6-5ba6-45c5-a6d2-f5a79739dda9 · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Deep Learning Scaling is Predictable, Empirically

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:08.312943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9e54adb-4ead-45dc-8789-86d25bd2e632 · outbound

This paper cites A Hitchhiker's Guide to Scaling Law Estimation.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks A Hitchhiker's Guide to Scaling Law Estimation

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:07.578271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers