When the samples are limited, robustness is especially crucial to ensure the generalization capability of the classifier.

One major challenge for these applications is how to evaluate whether such generated texts are actually fluent, accurate, or effective. In this work, we conceptualize the evaluation of generated text as a text generation problem, modeled using pre-trained sequence-to-sequence models. The general idea is that models trained to convert the generated text to/from a reference output or the source text will achieve higher scores when the generated text is better.

Each mini-batch consists of half 2D and half 3D samples. S7, S8) are used for training and two subjects (S9, S11) for evaluation. The output of the last layer is a per-point probability map for each keypoint. Furthermore, our method is an improvement of existing capabilities but does not introduce a radically new capability in machine learning. Theoretically, the underlying density function cannot be perfectly reconstructed since the proposed basis distributions are fixed.

In this section we will derive the formula in Eqn. 13 of the paper for computing the discrete opacity It follows from Eqn. 12 of the paper that, α Eqn. 12 of the paper, we have α According to Eqn. 5 of the paper, the weight function w (t) is given by w (t) = T ( t) ρ (t), where ρ (t) = max null ( f ( p(t)) v) φ In this section we show that the weight function derived in naive solution is biased. According to Eqn. 8, the derivative of w (t) is given by: dw dt = T ( t) null dσ ( t) dt σ (t) Based on Eqn. 10 and Eqn. Therefore, the total number of sampled points for NeuS is 128. Figure 2: The section points and mid-points defined on a ray. E.1 Rendering Quality and Speed Besides the reconstructed surfaces, our method also renders high-quality images, as shown in Figure 4. Rendering an image in resolution of 1600x1200 costs about 320 seconds in the default In this experiment, we held out 10% of the images in the DTU dataset as the testing set and the others as the training set. As shown in Table 3, our method achieves comparable performance to NeRF.

We consider the problem of learning the optimal threshold policy for control problems.