google-research / jax-influenceLinks
☆60Updated 3 years ago
Alternatives and similar repositories for jax-influence
Users that are interested in jax-influence are comparing it to the libraries listed below
Sorting:
- Influence Analysis and Estimation - Survey, Papers, and Taxonomy☆79Updated last year
- Data for "Datamodels: Predicting Predictions with Training Data"☆97Updated 2 years ago
- A simple PyTorch implementation of influence functions.☆88Updated last year
- A modern look at the relationship between sharpness and generalization [ICML 2023]☆43Updated last year
- ☆62Updated 4 years ago
- ☆89Updated last month
- Experiments and code to generate the GINC small-scale in-context learning dataset from "An Explanation for In-context Learning as Implici…☆106Updated last year
- ☆29Updated 2 years ago
- Influence Functions with (Eigenvalue-corrected) Kronecker-Factored Approximate Curvature☆156Updated last week
- Source code of "What can linearized neural networks actually say about generalization?☆20Updated 3 years ago
- A Kernel-Based View of Language Model Fine-Tuning https://arxiv.org/abs/2210.05643☆75Updated last year
- A fast, effective data attribution method for neural networks in PyTorch☆211Updated 7 months ago
- Code for the paper "The Journey, Not the Destination: How Data Guides Diffusion Models"☆22Updated last year
- ☆23Updated 9 months ago
- ☆30Updated 11 months ago
- ☆68Updated 6 months ago
- Implementation of Influence Function approximations for differently sized ML models, using PyTorch☆15Updated last year
- `dattri` is a PyTorch library for developing, benchmarking, and deploying efficient data attribution algorithms.☆77Updated 2 weeks ago
- The official repository for our paper "Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks". We…☆46Updated last year
- Code Release for "Broken Neural Scaling Laws" (BNSL) paper☆59Updated last year
- Simple and scalable tools for data-driven pretraining data selection.☆24Updated 2 weeks ago
- Official repository for our paper, Transformers Learn Higher-Order Optimization Methods for In-Context Learning: A Study with Linear Mode…☆16Updated 7 months ago
- ☆18Updated last year
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆42Updated last year
- Code for NeurIPS'23 paper "A Bayesian Approach To Analysing Training Data Attribution In Deep Learning"☆17Updated last year
- An Investigation of Why Overparameterization Exacerbates Spurious Correlations☆31Updated 4 years ago
- Code for "Tracing Knowledge in Language Models Back to the Training Data"☆38Updated 2 years ago
- ☆40Updated 2 years ago
- ☆50Updated 2 years ago
- Official code for "In Search of Robust Measures of Generalization" (NeurIPS 2020)☆28Updated 4 years ago