Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks

• URL: http://arxiv.org/abs/2405.15481v2
• Date: Tue, 10 Jun 2025 15:04:10 GMT
• Title: Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks
• Authors: Jialin Zhao, Yingtao Zhang, Xinghang Li, Huaping Liu, Carlo Vittorio Cannistraci,
• Abstract summary: Low-Rank Adaptation (LoRA) and ReLoRA face challenges with their low-rank structure.<n>We propose Sparse Spectral Training (SST) to optimize memory usage for pre-training.<n>SST reduces the perplexity gap between other low-rank methods and full-rank training by 97.4%.
• Score: 9.96381061452642
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing demands on GPU memory posed by the increasing number of neural network parameters call for training approaches that are more memory-efficient. Previous memory reduction training techniques, such as Low-Rank Adaptation (LoRA) and ReLoRA, face challenges, with LoRA being constrained by its low-rank structure, particularly during intensive tasks like pre-training, and ReLoRA suffering from saddle point issues. In this paper, we propose Sparse Spectral Training (SST) to optimize memory usage for pre-training. SST updates all singular values and selectively updates singular vectors through a multinomial sampling method weighted by the magnitude of the singular values. Furthermore, SST employs singular value decomposition to initialize and periodically reinitialize low-rank parameters, reducing distortion relative to full-rank training compared to other low-rank methods. Through comprehensive testing on both Euclidean and hyperbolic neural networks across various tasks, SST demonstrates its ability to outperform existing memory reduction training methods and is comparable to full-rank training in various cases. On LLaMA-1.3B, with only 18.7\% of the parameters trainable compared to full-rank training (using a rank equivalent to 6\% of the embedding dimension), SST reduces the perplexity gap between other low-rank methods and full-rank training by 97.4\%. This result highlights SST as an effective parameter-efficient technique for model pre-training.

Related papers

• An Augmented Backward-Corrected Projector Splitting Integrator for Dynamical Low-Rank Training [47.69709732622765]
  We introduce a novel low-rank training method that reduces the number of required QR decompositions. Our approach integrates an augmentation step into a projector-splitting scheme, ensuring convergence to a locally optimal solution.
  arXiv  Detail & Related papers  (2025-02-05T09:03:50Z)
• The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws [51.608402959163925]
  We present the first systematic exploration of optimal sparse pre-training configurations for large language models. We find that initiating pruning at 25% of total training compute and concluding at 75% achieves near-optimal final evaluation loss. We propose a new scaling law that modifies the Chinchilla scaling law to use the average parameter count over pre-training.
  arXiv  Detail & Related papers  (2025-01-21T20:23:22Z)
• SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning [63.93193829913252]
  We propose an innovative METL strategy called SHERL for resource-limited scenarios. In the early route, intermediate outputs are consolidated via an anti-redundancy operation. In the late route, utilizing minimal late pre-trained layers could alleviate the peak demand on memory overhead.
  arXiv  Detail & Related papers  (2024-07-10T10:22:35Z)
• SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining [39.56934385513862]
  Training large language models (LLMs) from scratch requires significant computational power and extensive memory capacity. Recent studies have explored low-rank structures on weights for efficient fine-tuning in terms of parameters and memory. We propose to parameterize the weights as a sum of low-rank and sparse matrices for pretraining, which we call SLTrain.
  arXiv  Detail & Related papers  (2024-06-04T11:14:21Z)
• Approximated Likelihood Ratio: A Forward-Only and Parallel Framework for Boosting Neural Network Training [30.452060061499523]
  We introduce an approximation technique for the likelihood ratio (LR) method to alleviate computational and memory demands in gradient estimation. Experiments demonstrate the effectiveness of the approximation technique in neural network training.
  arXiv  Detail & Related papers  (2024-03-18T23:23:50Z)
• PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs [22.557682089926004]
  We show that updating a small subset of parameters can suffice to recover or even enhance performance after pruning.<n>We introduce two novel LoRA variants that, unlike standard LoRA, allow merging adapters back without compromising sparsity.
  arXiv  Detail & Related papers  (2023-12-23T11:45:22Z)
• Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks [70.75043144299168]
  Spiking Neural Networks (SNNs) are promising energy-efficient models for neuromorphic computing. We propose the Spatial Learning Through Time (SLTT) method that can achieve high performance while greatly improving training efficiency. Our method achieves state-of-the-art accuracy on ImageNet, while the memory cost and training time are reduced by more than 70% and 50%, respectively, compared with BPTT.
  arXiv  Detail & Related papers  (2023-02-28T05:01:01Z)
• Online Training Through Time for Spiking Neural Networks [66.7744060103562]
  Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with low latency. We propose online training through time (OTTT) for SNNs, which is derived from BPTT to enable forward-in-time learning.
  arXiv  Detail & Related papers  (2022-10-09T07:47:56Z)
• LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning [82.93130407930762]
  It is costly to update the entire parameter set of large pre-trained models. PETL techniques allow updating a small subset of parameters inside a pre-trained backbone network for a new task. We propose Ladder Side-Tuning (LST), a new PETL technique that reduces training memory requirements by more substantial amounts.
  arXiv  Detail & Related papers  (2022-06-13T23:51:56Z)
• Learning in Feedback-driven Recurrent Spiking Neural Networks using full-FORCE Training [4.124948554183487]
  We propose a supervised training procedure for RSNNs, where a second network is introduced only during the training. The proposed training procedure consists of generating targets for both recurrent and readout layers. We demonstrate the improved performance and noise robustness of the proposed full-FORCE training procedure to model 8 dynamical systems.
  arXiv  Detail & Related papers  (2022-05-26T19:01:19Z)
• Parameter-Efficient Sparsity for Large Language Models Fine-Tuning [63.321205487234074]
  We propose a. sparse-efficient Sparse Training (PST) method to reduce the number of trainable parameters during sparse-aware training. Experiments with diverse networks (i.e., BERT, RoBERTa and GPT-2) demonstrate PST performs on par or better than previous sparsity methods.
  arXiv  Detail & Related papers  (2022-05-23T02:43:45Z)
• Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey [69.3939291118954]
  State-of-the-art deep learning models have a parameter count that reaches into the billions. Training, storing and transferring such models is energy and time consuming, thus costly. Model compression lowers storage and transfer costs, and can further make training more efficient by decreasing the number of computations in the forward and/or backward pass. This work is a survey on methods which reduce the number of trained weights in deep learning models throughout the training.
  arXiv  Detail & Related papers  (2022-05-17T05:37:08Z)
• Dynamic Sparse Training for Deep Reinforcement Learning [36.66889208433228]
  We propose for the first time to dynamically train deep reinforcement learning agents with sparse neural networks from scratch. Our approach is easy to be integrated into existing deep reinforcement learning algorithms. We evaluate our approach on OpenAI gym continuous control tasks.
  arXiv  Detail & Related papers  (2021-06-08T09:57:20Z)
• Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training [16.81321230135317]
  We propose the concept of In-Time Over- Resibilityization (ITOP) in sparse training. ITOP closes the gap in the express between sparse training and dense training. We present a series of experiments to support our conjecture and achieve the state-of-the-art sparse training performance.
  arXiv  Detail & Related papers  (2021-02-04T20:59:31Z)
• Selfish Sparse RNN Training [13.165729746380816]
  We propose an approach to train sparse RNNs with a fixed parameter count in one single run, without compromising performance. We achieve state-of-the-art sparse training results with various datasets on Penn TreeBank and Wikitext-2.
  arXiv  Detail & Related papers  (2021-01-22T10:45:40Z)
• FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training [81.85361544720885]
  We propose FracTrain that integrates progressive fractional quantization which gradually increases the precision of activations, weights, and gradients. FracTrain reduces computational cost and hardware-quantified energy/latency of DNN training while achieving a comparable or better (-0.12%+1.87%) accuracy.
  arXiv  Detail & Related papers  (2020-12-24T05:24:10Z)
• Predicting Training Time Without Training [120.92623395389255]
  We tackle the problem of predicting the number of optimization steps that a pre-trained deep network needs to converge to a given value of the loss function. We leverage the fact that the training dynamics of a deep network during fine-tuning are well approximated by those of a linearized model. We are able to predict the time it takes to fine-tune a model to a given loss without having to perform any training.
  arXiv  Detail & Related papers  (2020-08-28T04:29:54Z)
• Subset Sampling For Progressive Neural Network Learning [106.12874293597754]
  Progressive Neural Network Learning is a class of algorithms that incrementally construct the network's topology and optimize its parameters based on the training data. We propose to speed up this process by exploiting subsets of training data at each incremental training step. Experimental results in object, scene and face recognition problems demonstrate that the proposed approach speeds up the optimization procedure considerably.
  arXiv  Detail & Related papers  (2020-02-17T18:57:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.