Academic paper writing
Deep networks are learned using huge training data where the problem of noisy labels is particularly relevant. （relevant – 重要的）
Deep Learning with noisy labels is a practically challenging problem in weakly supervised learning.
A. Solar energy conversion by photoelectrochemical cells
has been intensively investigated.
This was demonstrated in a number of studies thatshowed that composite plasmonic-metal/semiconductor photocatalysts achieved significantly higher rates in various photocatalytic reactions compared with their pure semiconductor counterparts.
Several excellent reviews describingthese applications are available, and we do not discuss these topics
Much work so far has focused onwide band gap semiconductors for water splitting for the sake of chemical stability.
Recent developments ofLewis acids and water-soluble organometallic catalysts
have attracted much attention.
An interesting approach inthe use of zeolite as a water-tolerant solid acid
was described by Ogawa et al.
Motivated by Co-training for multi-view learning and semi-supervised learning that aims to maximize the agreement on multiple distinct views, a straight-forward method for handling noisy labels is to apply the regularization from peer networks when training each single network.
However, although the regularization may improve the generalization ability of networks by encouraging agreement between them, it still suffers from memorization effects on noisy labels
To address this problem, we propose a novel approach named JoCoR （我们提出了）
Specifically, we train two net- works with a joint loss, including the conventional super- vised loss and the Co-Regularization loss. Furthermore, we use the joint loss to select small-loss examples, thereby ensuring the error flow from the biased selection would not be accumulated in a single network. （具体来说, 我们….. 。此外, 我们使用….., 从而保证）
Empirical results demonstrate that the robustness of deep models trained by our proposed approach is superior to many state-of-the-art approaches.
Furthermore, the ablation studies clearly demonstrate the effectiveness of Co-Regularization and Joint Training.
In this section, we briefly review existing works on learning with noisy labels.
In addition to the aforementioned approaches, there are some other deep learning solutions [13, 17] to deal with noisy labels, including pseudo-label based [35, 40] and robust loss based approaches [28, 46]. （除了上述方法外，还有一些其他深入学习的解决方案[13,17]来处理噪声标签，包括基于伪标签的[35,40]和基于鲁棒损失的ap-方法[28,46]）
For pseudo-label based approaches, Joint optimization  learns network parameters and infers the ground-true labels simultaneously. For robust loss based approaches,
This argument will be clearly supported by the ablation study in the later section.
A. GIXRD patterns in
Figure 1A showthe bulk structural information on as-deposited films.
As shown in Figure 7B, the steady-state current density decreases after cycling between 0.35 and 0.7 V, which is probably due to the dissolution of FeOx.
As can be seen from parts a and b of Figure 7, the reaction cycles start with the thermodynamically most favorable VOx structures（J. Phys. Chem. C 2014, 118, 24950−24958）
A. To gain more insight, we conducted several X-ray, NMR, and electrochemical studies.
B. To further confirm that phosphorylation of AMPA-R in hippocampal slices was catalyzed by CaM-KII,GluR1 was expressed in HEK-293 cells with or without CaM-KII.
C. In order to trace this dynamic development,it is our goal in this Letter, to describe the changes in the properties of such high efficiency Cu(In,Ga)Se2 (CIGS) solar cells. D. To gain/achieve a better understanding of how the immune system responds to MPDL3280A, the levels of the IL-18 immunostimulatory cytokine and IFN-γ,which is stimulated by IL-18, were examined over several cycles.
E. To further explore this concept, we provide an example, where patient A underwent radical prostatectomy with undetectable postoperative serum PSA in 2005.
F. In order to deeply investigate the aggregation properties in this range of concentrations, we studied these condary structure of these peptide derivatives by CD and FTIR spectroscopies.
This is supported bythe appearance in the Ni-doped compounds of an ultraviolet–visible absorption band at 420–520nm (see Fig. 3 inset), corresponding to an energy range of about 2.9 to 2.3 eV.
is consistent with the observation fromSEM–EDS.
This indicates a good agreement betweenthe observed and calculated intensities in monoclinic with space groupP2/c when the O atoms are included in the model.
D. The results
are in good consistent withthe observed photocatalytic activity…
Identical conclusions were obtained in studieswhere the SPR intensity and wavelength were modulated by manipulating the composition, shape,or size of plasmonic nanostructures.
F. It was also found that areas of persistent divergent surface flow
coincide withregions where convection appears to be consistently suppressed even when SSTs are above 27.5°C.