deep-learning

Neural-DISCO 提出一种解纠缠表示学习框架，用于神经群体活动的源条件反事实编辑：可替换单个行为或刺激标签的潜在分量，同时保留其他标签与源特异性变异性，并结合特征归因识别单神经元层面的功能角色。

A pre-registered study comparing SVM and convolutional recurrent models for five-class respiratory sound classification finds that feature standardization (macro recall 0.51→0.78) has a far larger impact than architecture choice, and that data augmentation provides no measurable benefit in low-resource, offline clinical settings.

A Stanford CS312 (Deep Learning Alchemy) instructor posts exam-style prediction questions testing deep learning intuition, asking whether learning-rate or weight-decay rescaling can compensate for an 8× batch size increase in an AdamW-trained Transformer.

该论文提出 Graph-Dependent Pooling (GDPool)，一种动态决定每个图池化粒度的层级图神经网络池化方法，通过自注意力选择锚点并在金属有机框架和分子图基准上取得最佳性能。

该论文提出了多变量时间序列超分辨率预测（MTS-SRF）任务及 TimeMagnifier 模型，能够从粗粒度历史数据中预测细粒度未来，在十个数据集上以 12.3% 的平均 MSE 降低击败 15 个基线。

论文提出 Isharah-Selfie 数据集，这是一个完全通过前置智能手机自拍视角采集的大型阿拉伯手语（ArSL）数据集，包含 16 名签名者的 8,997 段视频和 1,493 个句子，用于连续手语识别与翻译，并揭示现有方法在自拍条件及未见句子泛化上的不足。

提出 CA-DPSGD 框架，利用近似二阶信息构建曲率感知的梯度裁剪空间，以缓解 DPSGD 中的裁剪偏差问题，并在图像和句子分类基准上验证其优于现有裁剪方法。

提出 Dual-Branch Calibration Module (DBCM)，通过共享适配器与低秩通道适配器的双分支设计，在时序预测的测试时自适应中处理非平稳分布偏移，并在多个数据集、预测跨度和骨干模型上超越现有方法。

提出了 Cautious ConFIG，一个三行扩展的优化算法，修复了 ConFIG 与 Adam 等自适应优化器结合时破坏无冲突梯度方向的问题，并在多个 PINN 基准上取得一致性能提升。

A comprehensive review of the NDE 4.0 landscape, analyzing non-destructive inspection through computational algorithms, physical techniques, industrial applications, and research objectives, highlighting the shift toward deep learning and generative models for automated defect recognition and identifying key gaps like open datasets, explainable AI, and standardized digital twin protocols.

实验表明 PBT 并不能加速 grokking，但在固定超参数全部失效的'死区'内可以成功逃逸并诱导 grokking；复盘显示真正起作用的是学习率/权重衰减的调度路径而非种群间的权重拷贝。

This paper presents the first gold-standard dataset of Nepali political texts for election commitment mining, comparing machine learning, deep learning, and LLM approaches, with results integrated into a mobile app for electoral accountability visualization.

This paper measures exactly how much weight gradient is lost when training looped transformers via truncated backpropagation across nine models, finding that the lost gradient is largely set near the output, diminishes during fine-tuning, and does not clearly outperform measurement-free cost heuristics like Parcae's rule.

提出了运动等变卷积 LSTM（MELSTM），将序列数据中的运动视为规范对称性（gauge symmetry），通过速度模型作为规范场实现对任意运动的可证明等变性，在未来帧预测、运动数字分类和动作识别任务上以更少状态超越 ConvLSTM 与流量等变基线。

本文提出 TF-IG，一种参数高效的交互门控时频学习框架，用于基于 PPG 的心率估计，在 BIDMC PPG 数据集上以仅 84.6K 参数实现 1.227 BPM 的 MAE 和 0.9934 的皮尔逊相关系数。

A research post introducing 'reactive optimizers': an agent watches training dynamics and rewrites the optimizer's source code during training, matching or beating the best hand-tuned recipes on image and language tasks while autonomously guarding against loss spikes and overfitting. The method is released via the open-source OPAL package.

This paper extends an Extreme Value Theory approach for CVaR optimization in reinforcement learning to high-dimensional neural-network policies, obtaining a differentiable tail-risk objective computable via automatic differentiation, and shows improved hedging performance under extreme scenarios compared to empirical-CVaR-trained policies.

MoSAIK 从标准多线圈 TSE k-space 中用 Transformer 直接估计刚体运动参数，并将其用于模型化的运动感知重建，从而在无需外部导航器的情况下快速回顾性校正脑 MRI 运动伪影。

CorteXforge is a scenario-based framework that generates realistic, reproducible annotated RF datasets on programmable SDR testbeds (SLICES-RI/CorteXlab), validated via automatic modulation classification showing synthetic-to-real gaps and the benefits of synthetic pretraining.

CoASFL 提出一种面向异步分割联邦学习的不一致性感知框架，通过动态激活合并与一致性感知模型聚合，同时缓解客户端间水平不一致和客户端-服务器垂直不一致，在80设备边缘测试床上将训练加速1.61×至3.74×并提升最高3.26%的准确率。