Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Knowledge-Adaptation Priors
Authors: Mohammad Emtiyaz Khan, Siddharth Swaroop
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that adaptation with K-priors achieves performance similar to full retraining, but only requires training on a handful of past examples. (Abstract) |
| Researcher Affiliation | Academia | Mohammad Emtiyaz Khan RIKEN Center for AI Project Tokyo, Japan EMAIL Siddharth Swaroop University of Cambridge Cambridge, UK EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. Methods are described in prose and mathematical equations. |
| Open Source Code | Yes | Code is available at https://github.com/team-approx-bayes/kpriors. (Section 1) |
| Open Datasets | Yes | Results on binary classification on USPS digits (Figure 1 caption); Logistic Regression on the UCI Adult dataset (Section 5); Logistic Regression on the USPS odd vs even dataset (Section 5); MNIST and CIFAR-10, neural networks. ... for 10-way classification on MNIST [32] with MLPs and 10-way classification on CIFAR-10 with CifarNet [62] (Section 5) |
| Dataset Splits | Yes | Validation acc (%) (Figure 1, 2, 3 labels); For the Add Data task, the base model uses 9% of the data and we add 1% new data. (Section 5) |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software components like 'L-BFGS optimizer', 'Adam optimizer', 'MLP', and 'CifarNet', but it does not specify version numbers for these or other relevant software libraries (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | For training, we use the L-BFGS optimizer for logistic regression with polynomial basis. (Section 5); For the Change Regularizer task, we change the L2 regularizer from δ = 50 to 5 (Section 5); the Change Architecture task compresses the architecture from a 2-hidden-layer MLP (100 units per layer) to a 1-hidden-layer MLP with 100 units. (Section 5) |