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Grad Queue : A probabilistic framework to reinforce sparse gradients

Irfan Mohammad Al Hasib

TL;DR

The paper tackles the problem of informative gradients being diluted in large mini-batch updates by introducing Grad Queue (GQ), which maintains a finite online gradient queue to estimate the current mean $μ_t$ and standard deviation $σ_t$ and to compute a scarcity-based weight $Δ_ρ$ that amplifies rare updates. A loss-trend–driven, dynamic queue length $qlen$ adapts the history window, and when batches grow large, data are clustered in a latent feature space to preserve aligned sub-tasks before aggregation, ensuring diversity and reducing destructive interference. The scarcity operator $Δ_ρ$ bounds and weights gradients based on how far they deviate from the past distribution, while intra-batch sparsity is enhanced by clustering and weighting cluster centers before summing to a final gradient $G^*$. Empirical results on CIFAR-10, MNIST, and Reuters show consistent improvements over standard optimizers, with larger cluster counts benefiting bigger batches, indicating practical potential for scalable, diverse gradient updates.

Abstract

Informative gradients are often lost in large batch updates. We propose a robust mechanism to reinforce the sparse components within a random batch of data points. A finite queue of online gradients is used to determine their expected instantaneous statistics. We propose a function to measure the scarcity of incoming gradients using these statistics and establish the theoretical ground of this mechanism. To minimize conflicting components within large mini-batches, samples are grouped with aligned objectives by clustering based on inherent feature space. Sparsity is measured for each centroid and weighted accordingly. A strong intuitive criterion to squeeze out redundant information from each cluster is the backbone of the system. It makes rare information indifferent to aggressive momentum also exhibits superior performance with larger mini-batch horizon. The effective length of the queue kept variable to follow the local loss pattern. The contribution of our method is to restore intra-mini-batch diversity at the same time widening the optimal batch boundary. Both of these collectively drive it deeper towards the minima. Our method has shown superior performance for CIFAR10, MNIST, and Reuters News category dataset compared to mini-batch gradient descent.

Grad Queue : A probabilistic framework to reinforce sparse gradients

TL;DR

The paper tackles the problem of informative gradients being diluted in large mini-batch updates by introducing Grad Queue (GQ), which maintains a finite online gradient queue to estimate the current mean and standard deviation and to compute a scarcity-based weight that amplifies rare updates. A loss-trend–driven, dynamic queue length adapts the history window, and when batches grow large, data are clustered in a latent feature space to preserve aligned sub-tasks before aggregation, ensuring diversity and reducing destructive interference. The scarcity operator bounds and weights gradients based on how far they deviate from the past distribution, while intra-batch sparsity is enhanced by clustering and weighting cluster centers before summing to a final gradient . Empirical results on CIFAR-10, MNIST, and Reuters show consistent improvements over standard optimizers, with larger cluster counts benefiting bigger batches, indicating practical potential for scalable, diverse gradient updates.

Abstract

Informative gradients are often lost in large batch updates. We propose a robust mechanism to reinforce the sparse components within a random batch of data points. A finite queue of online gradients is used to determine their expected instantaneous statistics. We propose a function to measure the scarcity of incoming gradients using these statistics and establish the theoretical ground of this mechanism. To minimize conflicting components within large mini-batches, samples are grouped with aligned objectives by clustering based on inherent feature space. Sparsity is measured for each centroid and weighted accordingly. A strong intuitive criterion to squeeze out redundant information from each cluster is the backbone of the system. It makes rare information indifferent to aggressive momentum also exhibits superior performance with larger mini-batch horizon. The effective length of the queue kept variable to follow the local loss pattern. The contribution of our method is to restore intra-mini-batch diversity at the same time widening the optimal batch boundary. Both of these collectively drive it deeper towards the minima. Our method has shown superior performance for CIFAR10, MNIST, and Reuters News category dataset compared to mini-batch gradient descent.
Paper Structure (8 sections, 3 theorems, 18 equations, 6 figures, 1 algorithm)

This paper contains 8 sections, 3 theorems, 18 equations, 6 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Literature review
  3. Methodology
  4. Attention to rare signal
  5. Monotonous and Noisy Gradients
  6. Enhancing Intra-Batch sparsity
  7. Experimental Results
  8. Conclusion

Key Result

Lemma 3.1

Using gradients generated from eq. [eq3] the consecutive values for t=1 to N would be - $g_1=g_2 ... =g_{N-1} = u ; \ g_N = C$. Initializing momentum update equation $m_{t+1} = \beta m_t + m_{t+1}$, with $m_0 = 0$, $kN^{th}$ momentum will be -

Figures (6)

  • Figure 1: Figure 2:
  • Figure 2: $\beta_N \ , \gamma_N \ for \ \rho=2,3,5$ is shown for N steps.
  • Figure 3: Momentum values for gradient generated from [\ref{['eq3']}].
  • Figure 4: Gradients and propagation of weights for 5 consequitive time steps for filter-1 and filter-2 trained on synthetic line dataset
  • Figure 5: For sgdm and gq-sgdm method (a) weights of filter-2 (b) activations of filter-2 with training steps (c) loss
  • ...and 1 more figures

Theorems & Definitions (3)

  • Lemma 3.1
  • Lemma 3.2
  • Lemma 3.3