RNNPool can be applied to any tensor structuredproblem,butwefocuson2Dimages for ease of exposition.

We thank the reviewers for their constructive comments on our paper. We address the major questions in the following. R1: The ability to handle large temporal inconsistency. In the experiments for most evaluated tasks, we do not observe the extreme cases mentioned by R1. The proposed IRT solves the multimodal inconsistency problem well, which is ignored by prior work.

Parameters can be learned using standard methods based on back-propagation, at a cost that scales linearly in the fabric size.

This paper investigates neural network-based biometric authentication using finger-drawn digits on touchscreen devices. We evaluated CNN and autoencoder architectures for user authentication through simple digit patterns (0-9) traced with finger input. Twenty participants contributed 2,000 finger-drawn digits each on personal touchscreen devices. We compared two CNN architectures: a modified Inception-V1 network and a lightweight shallow CNN for mobile environments. Additionally, we examined Convolutional and Fully Connected autoencoders for anomaly detection. Both CNN architectures achieved ~89% authentication accuracy, with the shallow CNN requiring fewer parameters. Autoencoder approaches achieved ~75% accuracy. The results demonstrate that finger-drawn symbol authentication provides a viable, secure, and user-friendly biometric solution for touchscreen devices. This approach can be integrated with existing pattern-based authentication methods to create multi-layered security systems for mobile applications.

Diabetic retinopathy (DR), a microvascular complication of diabetes and a leading cause of preventable blindness, is projected to affect more than 130 million individuals worldwide by 2030. Early identification is essential to reduce irreversible vision loss, yet current diagnostic workflows rely on methods such as fundus photography and expert review, which remain costly and resource-intensive. This, combined with DR's asymptomatic nature, results in its underdiagnosis rate of approximately 25 percent. Although convolutional neural networks (CNNs) have demonstrated strong performance in medical imaging tasks, limited interpretability and the absence of uncertainty quantification restrict clinical reliability. Therefore, in this study, a deep ensemble learning framework integrated with uncertainty estimation is introduced to improve robustness, transparency, and scalability in DR detection. The ensemble incorporates seven CNN architectures-ResNet-50, DenseNet-121, MobileNetV3 (Small and Large), and EfficientNet (B0, B2, B3)- whose outputs are fused through an accuracy-weighted majority voting strategy. A probability-weighted entropy metric quantifies prediction uncertainty, enabling low-confidence samples to be excluded or flagged for additional review. Training and validation on 35,000 EyePACS retinal fundus images produced an unfiltered accuracy of 93.70 percent (F1 = 0.9376). Uncertainty-filtering later was conducted to remove unconfident samples, resulting in maximum-accuracy of 99.44 percent (F1 = 0.9932). The framework shows that uncertainty-aware, accuracy-weighted ensembling improves reliability without hindering performance. With confidence-calibrated outputs and a tunable accuracy-coverage trade-off, it offers a generalizable paradigm for deploying trustworthy AI diagnostics in high-risk care.

The use of target networks is a popular approach for estimating value functions in deep Reinforcement Learning (RL). While effective, the target network remains a compromise solution that preserves stability at the cost of slowly moving targets, thus delaying learning. Conversely, using the online network as a bootstrapped target is intuitively appealing, albeit well-known to lead to unstable learning. In this work, we aim to obtain the best out of both worlds by introducing a novel update rule that computes the target using the MINimum estimate between the Target and Online network, giving rise to our method, MINTO. Through this simple, yet effective modification, we show that MINTO enables faster and stable value function learning, by mitigating the potential overestimation bias of using the online network for bootstrapping. Notably, MINTO can be seamlessly integrated into a wide range of value-based and actor-critic algorithms with a negligible cost. We evaluate MINTO extensively across diverse benchmarks, spanning online and of-fline RL, as well as discrete and continuous action spaces. Across all benchmarks, MINTO consistently improves performance, demonstrating its broad applicability and effectiveness. Reinforcement Learning (RL) has demonstrated exceptional performance and achieved major breakthroughs across a diverse spectrum of decision-making challenges. Noteworthy applications include learning complex locomotion skills (Haarnoja et al., 2018b; Rudin et al., 2022) and enabling sophisticated, real-world capabilities such as robotic manipulation (Andrychowicz et al., 2020; Lu et al., 2025). The foundation of this success lies primarily in Deep RL, initiated by the introduction of the Deep Q-Network (DQN) (Mnih et al., 2013), which marked the first successful application of deep neural networks in RL. To make that happen, Mnih et al. (2013) introduce various techniques to mitigate mainly the deadly triad issue (V an Hasselt et al., 2018) due to the usage of function approximators, off-policy data, and target bootstrapping.

Neural Information Processing Systems http://nips.cc/

We thank the reviewers for their constructive comments on our paper. We address the major questions in the following. R1: The ability to handle large temporal inconsistency. In the experiments for most evaluated tasks, we do not observe the extreme cases mentioned by R1. The proposed IRT solves the multimodal inconsistency problem well, which is ignored by prior work.

The use of target networks in deep reinforcement learning is a widely popular solution to mitigate the brittleness of semi-gradient approaches and stabilize learning. However, target networks notoriously require additional memory and delay the propagation of Bellman updates compared to an ideal target-free approach. In this work, we step out of the binary choice between target-free and target-based algorithms. We introduce a new method that uses a copy of the last linear layer of the online network as a target network, while sharing the remaining parameters with the up-to-date online network. This simple modification enables us to keep the target-free's low-memory footprint while leveraging the target-based literature. We find that combining our approach with the concept of iterated Q-learning, which consists of learning consecutive Bellman updates in parallel, helps improve the sample-efficiency of target-free approaches. Our proposed method, iterated Shared Q-Learning (iS-QL), bridges the performance gap between target-free and target-based approaches across various problems, while using a single Q-network, thus being a step forward towards resource-efficient reinforcement learning algorithms.