Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: The paper propose e Filter Gradient Decent (FGD), a family of stochastic optimization algorithms that makes the consistent estimation of the local gradient by solving an adaptive filtering problem. The paper shows that FDG can reduce the variance of the SGD and improve training a classifier on MNIST compared to traditional momentum-based methods.
strengths_and_weaknesses: # Strengths
- Various types of filters are proposed including Moving Average filter (FGD-MA), Auto-regressive filter (FGD-AR), Kalman filter (FGD-K), and Wavelet filter (FGD-W).
- Experiments show that FGD significantly improves the classification accuracy of MNIST compared to the conventional SGD and Adam optimizer.

# Weaknesses
- Experiments on more large-scale datasets should be considered e.g., CIFAR10 and CIFAR100.
suggested_changes: Experiments on more large-scale datasets should be considered e.g., CIFAR10 and CIFAR100.
review_title: null

Decision: Invite to archive