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Supplementary Material for DeWave: Discrete Encoding of EEG Waves for EEG to Text Translation
In this material, we will give more technical details as well as additional experiments to support the main paper. The overview of the proposed framework, DeWave, is illustrated in Figure 6. The dataset is split into training (80%), development (10%), and testing (10%) sets, comprising 10,874, 1,387, and 1,387 unique sentences, respectively, with no overlap. We release our implementation code through GitHub to contribute to this area. Section 3.3, where a 6-layer CNN encoder slides through the whole wave and gets the embedding The codex encoder shares the same structure with word-level features.
DataComp-LM: Insearchofthenextgenerationof trainingsetsforlanguagemodels
Asabaseline for DCLM, we conduct extensive experiments and find that model-based filtering is key to assembling a high-quality training set.
Bill Maher laments rise of gambling culture among young Americans during 'Real Time'
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On Learning Latent Models with Multi-Instance Weak Supervision
Our main contributions are as follows.