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..
Optimizing affinity-based binary hashing using auxiliary coordinates
Authors: Ramin Raziperchikolaei, Miguel A. Carreira-Perpinan
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared to this, our optimization is guaranteed to obtain better hash functions while being not much slower, as demonstrated experimentally in various supervised datasets. |
| Researcher Affiliation | Academia | Ramin Raziperchikolaei EECS, University of California, Merced EMAIL Miguel A. Carreira-Perpi n an EECS, University of California, Merced EMAIL |
| Pseudocode | No | The supplementary material gives the overall MAC algorithm to learn a hash function by optimizing an affinity-based loss function. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | (1) CIFAR [13] contains 60 000 images in 10 classes. We use D = 320 GIST features [23] from each image. We use 58 000 images for training and 2 000 for test. (2) Infinite MNIST [20]. We generated, using elastic deformations of the original MNIST handwritten digit dataset, 1 000 000 images for training and 2 000 for test, in 10 classes. |
| Dataset Splits | Yes | (1) CIFAR [13] contains 60 000 images in 10 classes. ... We use 58 000 images for training and 2 000 for test. (2) Infinite MNIST [20]. ... We generated ... 1 000 000 images for training and 2 000 for test, in 10 classes. We train the hash functions in a subset of 10 000 points of the training set, and report precision and recall by searching for a test query on the entire dataset (the base set). |
| Hardware Specification | No | The runtime per iteration for our 10 000-point training sets with b = 48 bits and κ+ = 100 and κ = 500 neighbors in a laptop is 2 for both MACcut and MACquad. |
| Software Dependencies | No | As hash functions (for each bit), we use linear SVMs (trained with LIBLINEAR; [9]) and kernel SVMs (with 500 basis functions). |
| Experiment Setup | Yes | We use the following schedule for the penalty parameter µ in the MAC algorithm (regardless of the hash function type or dataset). We initialize Z with µ = 0, i.e., the result of quad or cut. Starting from µ1 = 0.3 (MACcut) or 0.01 (MACquad), we multiply µ by 1.4 after each iteration (Z and h step). |