Gaussian process (GP) hyperparameter optimization requires repeatedly solving
linear systems with $n \times n$ kernel matrices. To address the prohibitive
$\mathcal{O}(n^3)$ time complexity, recent work has employed fast iterative
numerical methods, like conjugate gradients (CG). However, as datasets increase
in magnitude, the corresponding kernel matrices become increasingly
ill-conditioned and still require $\mathcal{O}(n^2)$ space without
partitioning. Thus, while CG increases the size of datasets GPs can be trained
on, modern datasets reach scales beyond its applicability. In this work, we
propose an iterative method which only accesses subblocks of the kernel matrix,
effectively enabling \emph{mini-batching}. Our algorithm, based on alternating
projection, has $\mathcal{O}(n)$ per-iteration time and space complexity,
solving many of the practical challenges of scaling GPs to very large datasets.
Theoretically, we prove our method enjoys linear convergence and empirically we
demonstrate its robustness to ill-conditioning. On large-scale benchmark
datasets up to four million datapoints our approach accelerates training by a
factor of 2$\times$ to 27$\times$ compared to CG.

通过基于交替投影的迭代算法，我们提出了一种能够有效进行小批量处理的方法，在解决大规模高斯过程训练的实际挑战中，获得了线性收敛并具有良好的鲁棒性，实验证明在大规模基准数据集上，相比于共轭梯度方法，我们的方法加速了 2 倍到 27 倍。

大规模高斯过程的交替投影

Large-Scale Gaussian Processes via Alternating Projection

State of the art architectures for untrimmed video Temporal Action
Localization (TAL) have only considered RGB and Flow modalities, leaving the
information-rich audio modality totally unexploited. Audio fusion has been
explored for the related but arguably easier problem of trimmed (clip-level)
action recognition. However, TAL poses a unique set of challenges. In this
paper, we propose simple but effective fusion-based approaches for TAL. To the
best of our knowledge, our work is the first to jointly consider audio and
video modalities for supervised TAL. We experimentally show that our schemes
consistently improve performance for state of the art video-only TAL
approaches. Specifically, they help achieve new state of the art performance on
large-scale benchmark datasets - ActivityNet-1.3 (54.34 mAP@0.5) and THUMOS14
(57.18 mAP@0.5). Our experiments include ablations involving multiple fusion
schemes, modality combinations and TAL architectures. Our code, models and
associated data are available at this https URL

本文提出了简单而有效的基于融合的方法，首次同时考虑音频和视频模态用于监督式的未剪辑视频动作定位 (TAL)，在多个融合方案、模态组合和 TAL 架构的消融试验中，我们通过大规模基准数据集（ActivityNet-1.3 和 THUMOS14）实验性地表明，我们的方案在国内外领先的仅视频 TAL 方法中始终能提高性能，特别地在指标度量上（mAP@0.5）达到新的最优水平。

听我说：用混合方法增强音频时序动作定位

Hear Me Out: Fusional Approaches for Audio Augmented Temporal Action  Localization

Reading comprehension (RC) has been studied in a variety of datasets with the
boosted performance brought by deep neural networks. However, the
generalization capability of these models across different domains remains
unclear. To alleviate this issue, we are going to investigate unsupervised
domain adaptation on RC, wherein a model is trained on labeled source domain
and to be applied to the target domain with only unlabeled samples. We first
show that even with the powerful BERT contextual representation, the
performance is still unsatisfactory when the model trained on one dataset is
directly applied to another target dataset. To solve this, we provide a novel
conditional adversarial self-training method (CASe). Specifically, our approach
leverages a BERT model fine-tuned on the source dataset along with the
confidence filtering to generate reliable pseudo-labeled samples in the target
domain for self-training. On the other hand, it further reduces domain
distribution discrepancy through conditional adversarial learning across
domains. Extensive experiments show our approach achieves comparable accuracy
to supervised models on multiple large-scale benchmark datasets.

利用条件对抗自训练方法，结合 BERT 模型和置信度过滤在目标领域中生成可靠的伪标记样本，通过条件对抗学习来减少域分布差异，从而在多个大型基准数据集上实现与监督模型相媲美的准确性。