In this project, we used formalin-fixed paraffin-embedded (FFPE) tissue
samples to measure thousands of spectra per tissue core with Fourier transform
mid-infrared spectroscopy using an FT-IR imaging system. These cores varied
between normal colon (NC) and colorectal primer carcinoma (CRC) tissues. We
created a database to manage all the multivariate data obtained from the
measurements. Then, we applied classifier algorithms to identify the tissue
based on its yielded spectra. For classification, we used the random forest, a
support vector machine, XGBoost, and linear discriminant analysis methods, as
well as three deep neural networks. We compared two data manipulation
techniques using these models and then applied filtering. In the end, we
compared model performances via the sum of ranking differences (SRD).

使用傅立叶变换中红外光谱法测量甲醛固定石蜡包埋组织样本，应用分类器算法来识别组织基于其光谱，比较两种数据处理技术并应用过滤，通过排序差异总和来比较模型性能。

用中红外光谱将结肠原位癌与正常结肠区分

Classification of colorectal primer carcinoma from normal colon with  mid-infrared spectra

Normalization techniques are a boon for modern deep learning. They let
weights converge more quickly with often better generalization performances. It
has been argued that the normalization-induced scale invariance among the
weights provides an advantageous ground for gradient descent (GD) optimizers:
the effective step sizes are automatically reduced over time, stabilizing the
overall training procedure. It is often overlooked, however, that the
additional introduction of momentum in GD optimizers results in a far more
rapid reduction in effective step sizes for scale-invariant weights, a
phenomenon that has not yet been studied and may have caused unwanted side
effects in the current practice. This is a crucial issue because arguably the
vast majority of modern deep neural networks consist of (1) momentum-based GD
(e.g. SGD or Adam) and (2) scale-invariant parameters. In this paper, we verify
that the widely-adopted combination of the two ingredients lead to the
premature decay of effective step sizes and sub-optimal model performances. We
propose a simple and effective remedy, SGDP and AdamP: get rid of the radial
component, or the norm-increasing direction, at each optimizer step. Because of
the scale invariance, this modification only alters the effective step sizes
without changing the effective update directions, thus enjoying the original
convergence properties of GD optimizers. Given the ubiquity of momentum GD and
scale invariance in machine learning, we have evaluated our methods against the
baselines on 13 benchmarks. They range from vision tasks like classification
(e.g. ImageNet), retrieval (e.g. CUB and SOP), and detection (e.g. COCO) to
language modelling (e.g. WikiText) and audio classification (e.g. DCASE) tasks.
We verify that our solution brings about uniform gains in those benchmarks.
Source code is available at this https URL

本文介绍了正则化技术在深度学习中的重要性，以及在使用冲量梯度下降优化器时可能出现的问题和解决方法。作者提出了 SGDP 和 AdamP 两种解决方案，通过去除每次优化步骤中的径向分量或增加规范的方向，来维护深度神经网络的性能，并验证了这些方法对 13 个基准测试任务的实验结果。