table 1
A Appendix
Usage Application The model is intended for open-vocabulary object detection.Known Caveats
Scaling Open-Vocabulary Object Detection
Major challenges in scaling self-training are the choice of label space, pseudo-annotation filtering, and training efficiency.
A Implementation Details 1 A.1 Preliminary Study 2 The basic GPT-2 model
Model details can be found at https://huggingface.co/gpt2 Popular phrases such as "pair of" are examples of repetitive
Supplementary Materials: Towards robust and generalizable representations of extracellular data using contrastive learning
This augmentation is applied to waveforms with a probability of 0.7. T emporal Jitter: This augmentation works through two steps. This augmentation is applied to waveforms with a probability of 0.5. We use a batch size of 128 and learning rate of 0.0001 for all multi-channel models. CEED benchmark models seems appropriate.
[R1/R2] Infinite width assumption: the infinite width assumption is needed due to the technical detail that the norm
We thank reviewers for their valuable comments. We respond to the main concerns below. Similar to that in Zhang et al. [31], we chose 10k block ResNet to stress the We will rephrase L243 to better express this. Derivative of weights depend on this term due to the chain rule. We will make this explicit in the revised manuscript.
d045c59a90d7587d8d671b5f5aec4e7c-AuthorFeedback.pdf
We thank all reviewers for their constructive comments and address the raised issues below. As described in Secion 3.2 of the manuscript, we introduce the The source code, as mentioned on L141, will be made available to the public. R1: Why the adaptive flow filtering is a better way of reducing artifacts? Our method could be seen as a learnable median filter in spirit. Although the quantitative improvement from the adaptive flow filtering (ada.) is small, this component is important in generating results with higher visual quality SepConv has originally been trained on high-quality videos with large motion.
. Figure 1 m n 100 1000 10 29 4 s 33 6 s 50 8 1 min 9 1 min 100 15 1 min 24 2 min Table 2: Time to reach relative improvement 10
We thank the reviewers for their comments. We then address reviewer's comments individually (due to space limits please zoom in the tiny figures). For [18] we used Alg. 2 We thank the reviewer for the additional reference, which we will add to the paper. Gradient Descent) applied in parallel to multiple starting points. We thank R2 for the reference "Entropic regularization of continuous optimal transport problems".