All sources cited or reviewed

This is a list of all sources we have used in the TransferLab, with links to the referencing content and metadata, like accompanying code, videos, etc. If you think we should look at something, drop us a line

### References

[Kum23B]

BunDLe-Net: Neuronal Manifold Learning Meets Behaviour,

[Lee23I]

Inducing Point Operator Transformer: A Flexible and Scalable Architecture for Solving PDEs,

[Lee22aM]

Mesh-Independent Operator Learning for Partial Differential Equations,

[Art16L]

Learning principled bilingual mappings of word embeddings while preserving monolingual invariance,

[Boj17E]

Enriching Word Vectors with Subword Information,

[Con18W]

Word Translation Without Parallel Data,

[Min22W]

WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models,

[Lu21L]

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators,

[Rai19P]

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

[Shu24D]

Deep neural operators as accurate surrogates for shape optimization,

[Dos20I]

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,

[Gan24I]

Interpreting CLIP's Image Representation via Text-Based Decomposition,

[Rad21L]

Learning Transferable Visual Models From Natural Language Supervision,

[Xu23D]

Demystifying CLIP Data,

[Fre22S]

Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena,

[Gor23A]

Amortized Bayesian Decision Making for simulation-based models,

[Jus23L]

LAVA: Data Valuation without Pre-Specified Learning Algorithms,

[Boe22F]

Flexible and efficient simulation-based inference for models of decision-making,

[Del22R]

Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation,

[Glo22V]

Variational methods for simulation-based inference,

[Gre19A]

Automatic Posterior Transformation for Likelihood-Free Inference,

[Her20L]

Likelihood-free MCMC with Amortized Approximate Ratio Estimators,

[Lop17R]

Revisiting Classifier Two-Sample Tests,

[Lue17F]

Flexible statistical inference for mechanistic models of neural dynamics,

[Mil21T]

Truncated Marginal Neural Ratio Estimation,

[Pap18F]

Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation,

[Pap19S]

Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows,

[Tal20V]

Validating Bayesian Inference Algorithms with Simulation-Based Calibration,