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Towards Unsupervised Model Selection for Domain Adaptive Object Detection Hengfu Y u Jinhong Deng Wen Li
It suffers from performance degradation as the training goes on.
Jinyang Li
Text-to-SQL parsing, which aims at converting natural language questions into executable SQLs, has gained increasing attention in recent years.
Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models
We study the task of learning SIMs in the agnostic (a.k.a.
Supplementary 1 A Details of sampling process
Most of the breeds will have the first type of collapse as in Figure 5, 6, and 7. Figure 5, 6 shows the
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.
Towards robust and generalizable representations of extracellular data using contrastive learning
In CEED, we assume that waveforms are already extracted from an extracellular recording. Each waveform is then passed through our stochastic view generation module, where different views are obtained by applying transformations.