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.
Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits
arXiv:2607. 22564v1 Announce Type: new Abstract: Convolutional neural networks often contain redundant feature maps that increase storage and inference cost.
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arXiv Machine Learning
Jun 9
OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework
arXiv:2606. 08574v1 Announce Type: new Abstract: Data pruning (DP), as an oft-stated strategy to alleviate heavy training burdens, reduces the volume of training samples according to a well-defined pruning method while striving for near-lossless performance.
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.
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
Jun 11
RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning
arXiv:2606. 11761v1 Announce Type: new Abstract: Dynamic data pruning techniques aim to reduce computational cost while minimizing information loss by periodically selecting representative subsets of input data during model training.
arXiv Machine Learning
Jul 21
NIRVANA: Structured Pruning Reimagined for Large Language Model Compression
arXiv:2509. 14230v2 Announce Type: replace Abstract: While structured pruning presents a highly effective pathway for accelerating Large Language Model (LLM) inference, existing methods frequently suffer from significant performance degradation and demand computationally retraining to recover capabilities.
arXiv Machine Learning
Aug 10
Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models
arXiv:2608. 06901v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved remarkable generalization across diverse multimodal tasks through large-scale pre-training, yet their rapidly increasing computational and memory requirements pose significant challenges for deployment in constrained environments.
arXiv Machine Learning
Jul 20
Learning Faster without Deeper Networks: A*-Inspired Batch Selection for Efficient CNN Training
arXiv:2607. 15745v1 Announce Type: new Abstract: Common practice when training Convolutional Neural Networks (CNNs) is to use randomly shuffled mini-batches.
arXiv Machine Learning
Jun 25
Distill on a Diet: Efficient Knowledge Distillation via Learnable Data Pruning
arXiv:2606. 25488v1 Announce Type: new Abstract: Knowledge Distillation (KD) is widely used to obtain compact models for efficient inference in resource-constrained environments.
arXiv Machine Learning
Jun 3
Pruning Deep Neural Networks via the Marchenko--Pastur Distribution
arXiv:2606. 02608v1 Announce Type: new Abstract: We study a Marchenko--Pastur (MP) random-matrix approach to pruning deep neural networks with very small post-pruning fine-tuning budgets.
arXiv AI
Jun 4
Efficient Adversarial Attacks on High-dimensional Offline Bandits
arXiv:2602. 01658v2 Announce Type: replace-cross Abstract: Bandit algorithms have recently emerged as a powerful tool for evaluating machine learning models, including generative image models and large language models, by efficiently identifying top-performing candidates without exhaustive comparisons.
arXiv AI
Jul 28
TriSP: Tri-Signal Structured Pruning for Large Language Models
arXiv:2607. 22587v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance across diverse tasks but their deployment is constrained by the memory and compute cost of their parameters.