arXiv AI
Jun 9
Structured Neuron Pruning in Deep Neural Networks Using Multi-Armed Bandits
arXiv:2606. 07615v1 Announce Type: cross Abstract: Deep neural networks often contain redundant hidden units.
Structured vs. Unstructured Pruning: An Exponential Gap
arXiv:2603. 02234v3 Announce Type: replace-cross Abstract: The Strong Lottery Ticket Hypothesis (SLTH) states that large, randomly initialized neural networks contain sparse subnetworks capable of approximating a target function at initialization without training, suggesting that pruning alone is sufficient.
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Hugging Face Trending Papers
Jun 10
Finding Sparse Subnetworks in One Training Cycle via Progressive Magnitude-Based Pruning
Neural network pruning reduces model size by removing less important parameters while aiming to preserve predictive performance. Although the Lottery Ticket Hypothesis (LTH) shows that sparse subnetworks can match dense networks when trained from suitable initializations, its iterative pruning procedure requires multiple complete training cycles.
arXiv Machine Learning
Sep 23
Activation-Energy Pruning for Spiking Neural Networks: Unsupervised Personalization via Spike-Count Saliency
arXiv:2609.26167v1 Announce Type: new Abstract: Activation-energy pruning -- removing weights whose product of magnitude and cumulative pre-synaptic spike count falls below a threshold -- was establi...
arXiv Machine Learning
Jun 11
Finding Sparse Subnetworks in One Training Cycle via Progressive Magnitude-Based Pruning
arXiv:2606. 12278v1 Announce Type: cross Abstract: Neural network pruning reduces model size by removing less important parameters while aiming to preserve predictive performance.
arXiv Machine Learning
Jun 30
HiAP: A Multi-Granular Stochastic Auto-Pruning Framework for Vision Transformers
arXiv:2603. 12222v2 Announce Type: replace-cross Abstract: Vision Transformers require significant computational resources and memory bandwidth, severely limiting their deployment on resource-constraint hardware.
arXiv Machine Learning
Jul 7
A Unified Framework for Quantized and Continuous Strong Lottery Tickets
arXiv:2607. 03860v1 Announce Type: new Abstract: The Strong Lottery Ticket Hypothesis (SLTH) asserts that sufficiently overparameterized, randomly initialized neural networks contain sparse subnetworks that, even without any training, can match the performance of a small trained network on a given dataset.
Hugging Face Trending Papers
Aug 5
Effective pruning of task-trained recurrent neural networks using noisy fluctuations and connection rescaling
The pruning of network connections is key to brain function but, despite its importance, there exist few biologically-plausible pruning rules with demonstrated good performance. In this work we evaluate noise-prune, a recently introduced unsupervised local pruning rule for recurrent networks that uses noisy fluctuations to determine the importance of connections.
arXiv Machine Learning
Sep 14
Theoretical Guarantees for One-Shot Magnitude Pruning and Compute-Adaptive Early Exit
The paper investigates how to reduce computation in neural networks by combining one‑shot magnitude pruning in a static setting with early exit in an adaptive setting. In a simplified single‑neuron model it proves a concentration theorem for pruning and introduces a conditional perceptron whose excess error decreases as a power of the compute gap, with the exponent increasing as partial and full computations align. The authors extend these results to deep networks, showing how pruning distortions accumulate with depth and deriving a compute‑accuracy trade‑off for frozen‑backbone early exit under a Gaussian process framework, with numerical simulations supporting the theoretical scaling laws.
arXiv Machine Learning
Aug 7
Effective pruning of task-trained recurrent neural networks using noisy fluctuations and connection rescaling
arXiv:2608. 05464v1 Announce Type: cross Abstract: The pruning of network connections is key to brain function but, despite its importance, there exist few biologically-plausible pruning rules with demonstrated good performance.
arXiv Machine Learning
Aug 28
Pruning Binarized Neural Networks: A Dedicated Framework and Globally Weighted Algorithms
The paper presents a PyTorch-based framework for designing and optimizing binarized neural networks, incorporating freezing and pruning mechanisms. It introduces a novel pruning method that uses a global weighting scheme to assess parameter importance across abstraction levels, achieving a 70% pruning rate on VGG11 without sacrificing accuracy—outperforming existing binarized pruning results of 41%. The framework facilitates rapid, reproducible evaluation and prototyping of state‑of‑the‑art binarized network techniques.
arXiv Machine Learning
Sep 22
Artificial Structure Function Search: Preserving Artificial Functional Connectivity for Structured Pruning
arXiv:2609.24401v1 Announce Type: new Abstract: Structured pruning is a model compression technique that is used to reduce the computational cost of deploying deep neural networks on resource-constra...
arXiv Machine Learning
Jun 11
On the Stability of Growth in Structural Plasticity
arXiv:2605. 15435v2 Announce Type: replace Abstract: Standard deep-learning pipelines usually choose the network architecture before training and keep it fixed throughout optimization.