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..
Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel
Authors: Valerii Likhosherstov, Krzysztof M Choromanski, Kumar Avinava Dubey, Frederick Liu, Tamas Sarlos, Adrian Weller
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate DERFs experimentally in various machine learning applications. |
| Researcher Affiliation | Collaboration | Valerii Likhosherstov University of Cambridge EMAIL Krzysztof Choromanski Google Deep Mind & Columbia University EMAIL Avinava Dubey Google Research Frederick Liu Google Research Tamas Sarlos Google Research Adrian Weller University of Cambridge & The Alan Turing Institute |
| Pseudocode | No | The paper describes algorithms and methods in prose but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | mnist and cifar10 are where x, y are random images from MNIST [19] or CIFAR10 [27]... on 8 benchmarks from UCI [21]... We train Performer-encoders and test them on the Libri Speech corpus [35]... The General Language Understanding Evaluation (GLUE) benchmark [46]... We pretrained BERT model on two publicly available datasets (see: Table 3). Books [54] Wikipedia |
| Dataset Splits | Yes | As in [31], we obtain training, validation and test splits by shuffling the raw dataset and taking 90%, 5%, 5% objects respectively. The splits are fixed for all RF methods. |
| Hardware Specification | Yes | For the Transformer setups, we use TPU cluster and JAX [7] library. All tested Transformer variants were trained and tested on a TPU pods containing 4 TPU v3 chips with JAX and on GPUs (V100). |
| Software Dependencies | No | The paper mentions using 'Num Py [25]' and 'JAX [7]' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Table 2: Hyperparameters for the base models for pre-training for all methods (e.g., # of heads 12, Hidden layer size 768, Batch size 256, Learning rate 10^-4). Our Conformer-Transducer variant was characterized by: 20 conformer layers, model_dim = 512, relative position embedding dimensionality rped = 512 and h = 8 heads. We used batch size bs = 2048 and trained with the adam optimizer on TPUs. |