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arXiv AI

The Polytopal Neural Network

The paper introduces Polytopal Neural Networks (PNNs), a framework that enforces a polytope-based structure on neural network representations to extract distinct layer-wise aspects. By using learned corpus representations and an amortized simplex inference procedure, PNNs preserve meaningful latent structures with minimal performance loss and yield compressed representations comparable to vector‑quantized (VQ) methods. The authors demonstrate that PNNs provide a new, interpretable approach to VQ deep learning training while maintaining competitive performance.

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Same Predictions, Different Reasons: The Effect of Quantization on Model Explanations

Post-training quantization (PTQ) has become a practical solution for deploying deep learning models on resource-constrained edge devices by compressing high-precision floating-point weights into low-precision representations without requiring retraining. Past research has demonstrated that quantization largely preserves classification accuracy; however, whether it also preserves the model's internal reasoning remains an open question.

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arXiv Machine Learning
Jul 28

Same Predictions, Different Reasons: The Effect of Quantization on Model Explanations

arXiv:2607. 22872v1 Announce Type: new Abstract: Post-training quantization (PTQ) has become a practical solution for deploying deep learning models on resource-constrained edge devices by compressing high-precision floating-point weights into low-precision representations without requiring retraining.

By Kazi Kamruzzaman Rabbi, Md. Zami Al Zunaed Farabe, M. Sohel Rahman
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arXiv AI
2d ago

Predicate Neuron: Differentiable Quantization of Predicate Relations in Gradient-based Learning

The paper introduces the Native Predicate Neuron, a differentiable logic‑reasoning module that can be inserted into standard neural architectures. It learns to alternate between logical operations using a single trainable parameter and employs a dual‑nature gradient mechanism that performs hard logical operations during training while remaining differentiable for backpropagation. Experiments on Fully‑Connected, Convolutional, Recurrent, and Transformer models show that adding Predicate Neurons speeds convergence, improves accuracy, and reduces loss, especially on tasks involving monotonic reasoning or discontinuities.

By Ioannis Konstantoulas, Iliana Loi, Kyriakos Sgarbas
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arXiv AI
Sep 15

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks

The paper discusses tensorizing neural networks by reshaping dense weight matrices into higher-order tensors and approximating them with low-rank tensor network decompositions. This approach offers promising model compression and introduces bond indices that create new latent spaces, potentially enhancing interpretability. Despite encouraging empirical results, tensorized neural networks remain underused, and the authors call for more research to address practical scaling and adoption challenges.

By Safa Hamreras, Sukhbinder Singh, Rom\'an Or\'us
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Hugging Face Trending Papers
Jun 22

Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions

Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations of their performance remain incomplete. From a statistical viewpoint, a natural question is: can DNNs attain feature-learning and prediction consistency comparable to that of classical models?

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