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The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” Faces and people in general may not be generated properly. The model was trained mainly with English captions and will not...
The question of how cell-to-cell differences in transcription rate affect RNA count distributions is fundamental for understanding biological processes underlying transcription. Answering this question requires quantitative models that are both interpret
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints. Tonin, Patrinos, Suykens https://arxiv.org/pdf/2011.12659.pdf Very deep VAEs generalize autoregressive models and can outperform them on images. Anonymous https://openreview.net/forum?id...
The two bottom rows present predictions from when training using synthetic images. Full size image Fig. 3 Example U-Net predictions on an image in the BraTS 2021 test set. Classes are visualized as colored overlay where red is GD-enhancing tumor, blue is peritumoral edema (ED) and green ...
colors. However, the classifier favors blue samples over green ones due to short-cut learning issues and an over-representation of blue training samples. This spurious learning of features leads to unfair predictions. The most considered sensitive features in fairness studies are age, race, and ...
The results in the table display the SINDy-discovered coefficients of the corresponding terms in (Eqs. 1–3). In panel (a) the Data and SINDy curves are very closely overlapping, hence the appearance of only a single blue curve. Full size image Model discovery from empirical data We ...
Firstly, we use fixations recorded while viewing 800 static natural scene images, and infer an observer-related characteristic: the task at hand. We achieve an average of 55.9% correct classification rate (chance = 33%). We show that correct classification rates positively correlate with the ...
We remark that the g(r) remains essentially unchanged when computed at different times during the motion (this is not shown in the figure). Figure 2 Cluster size as a function of U0 for two different values of the density: green and blue for N = 103, 2 × 103, respectively,...