In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[Q]: Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain. In this work, we focus on  weakly supervised localization (WSL) where a model is trained to classify an image and localize regions of interest at pixel-level using only global image annotation. Typical convolutional attentions maps are prune to high false positive regions. To alleviate this issue, we propose a new deep learning method for WSL, composed of a localizer and a classifier, where the localizer is constrained to determine relevant and irrelevant regions using conditional entropy (CE) with the aim to reduce false positive regions. Experimental results on a public medical dataset and two natural datasets, using Dice index, show that, compared to state of the art WSL methods, our proposal can provide significant improvements in terms of image-level classification and pixel-level localization (low false positive) with robustness to overfitting. A public reproducible PyTorch implementation is provided.
[A]: A deep learning method for weakly-supervised pointwise localization that learns using image-level label only. It relies on conditional entropy to localize relevant and irrelevant regions aiming to minimize false positive regions.


[Q]: We introduce NAMSG, an adaptive first-order algorithm for training neural networks. The method is efficient in computation and memory, and is straightforward to implement. It computes the gradients at configurable remote observation points, in order to expedite the convergence by adjusting the step size for directions with different curvatures in the stochastic setting. It also scales the updating vector elementwise by a nonincreasing preconditioner to take the advantages of AMSGRAD. We analyze the convergence properties for both convex and nonconvex problems by modeling the training process as a dynamic system, and provide a strategy to select the observation factor without grid search. A data-dependent regret bound is proposed to guarantee the convergence in the convex setting. The method can further achieve a O(log(T)) regret bound for strongly convex functions. Experiments demonstrate that NAMSG works well in practical problems and compares favorably to popular adaptive methods, such as ADAM, NADAM, and AMSGRAD.
[A]: A new algorithm for training neural networks that compares favorably to popular adaptive methods.


[Q]: Genetic algorithms have been widely used in many practical optimization problems.
 Inspired by natural selection, operators, including mutation, crossover
 and selection, provide effective heuristics for search and black-box optimization.
 However, they have not been shown useful for deep reinforcement learning, possibly
 due to the catastrophic consequence of parameter crossovers of neural networks.
 Here, we present Genetic Policy Optimization (GPO), a new genetic algorithm
 for sample-efficient deep policy optimization. GPO uses imitation learning
 for policy crossover in the state space and applies policy gradient methods for mutation.
 Our experiments on MuJoCo tasks show that GPO as a genetic algorithm
 is able to provide superior performance over the state-of-the-art policy gradient
 methods and achieves comparable or higher sample efficiency.
[A]:
Genetic algorithms based approach for optimizing deep neural network policies