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..
BOLD: Boolean Logic Deep Learning
Authors: Van Minh NGUYEN, Cristian Ocampo-Blandon, Aymen Askri, Louis Leconte, Ba-Hien Tran
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We explore its convergence, conduct extensively experimental benchmarking, and provide consistent complexity evaluation by considering chip architecture, memory hierarchy, dataflow, and arithmetic precision. Our approach achieves baseline full-precision accuracy in Image Net classification and surpasses state-of-the-art results in semantic segmentation, with notable performance in image super-resolution, and natural language understanding with transformer-based models. |
| Researcher Affiliation | Industry | Mathematical and Algorithmic Sciences Laboratory, Huawei Paris Research Center, France |
| Pseudocode | Yes | Algorithm 1: Illustration with a FC layer. Algorithm 2: Boolean optimizer Algorithm 3: Equivalent formulation of Boolean optimizer Algorithm 4: Python code of XOR linear layer Algorithm 5: Python code of the backpropagation logic of XOR linear layer Algorithm 6: Backpropagation logic with Boolean received backpropagation Algorithm 7: Backpropagation logic with real received backpropagation Algorithm 8: Python code of Boolean optimizer |
| Open Source Code | Yes | In this section we provide example codes in Python of a Boolean linear layer that employs xor logic kernel. This implementation is in particular based on Py Torch [78]. |
| Open Datasets | Yes | Our B LD method is tested on two network configurations: small & compact and large & deep. In the former scenario, we utilize the VGG-SMALL [90] baseline trained on CIFAR10. ...Image Net classification and surpasses state-of-the-art results in semantic segmentation, with notable performance in image super-resolution, and natural language understanding with transformer-based models. |
| Dataset Splits | Yes | For IMAGENET, the training images were 192 × 192 px and 224 × 224 px for validation images. The batch size was 300 for both sets and the cross-entropy loss was used during training. |
| Hardware Specification | Yes | This approach is implemented for the Nvidia GPU (Tesla V100) and Ascend [63] architectures. |
| Software Dependencies | No | The presented methodology and the architecture of the described Boolean neural networks (NNs) were implemented in Py Torch [78] and trained on 8 Nvidia Tesla V100 GPUs. The paper mentions PyTorch but does not provide a specific version number for it or other software dependencies. |
| Experiment Setup | Yes | The networks thought predominantly Boolean, also contain a fraction of FP parameters that were optimized using the Adam optimizer [54] with learning rate 10^-3. For learning the Boolean parameters we used the Boolean optimizer (see Algorithm 8). Training the Boolean networks for image classification was conducted with learning rates η = 150 and η = 12 (see Equation 10), for architectures with and without batch normalization, respectively. The hyper-parameters were chosen by grid search using the validation data. During the experiments, both optimizers used the cosine scheduler iterating over 300 epochs. |