What are some techniques for explaining models that use high-dimensional embeddings?

Jul 14, 2020 ... The models we trained use for instance token embeddings of size 500 and feature embeddings of 12 to get to 512 in total. That makes sense ... We present a complementary system, AdaEmbed, to reduce the size of embeddings needed for the same DLRM accuracy via in-training embedding pruning. Apr 11, 2023 ... My main concern is determining the optimal or maximum sentence size for encoding each embedding. I understand that sentence length plays a crucial role. Apr 2, 2025 ... Resource and data size limits​ ; Embedding dimension. Per index. 4096 ; Indexes. Per endpoint. 50 ; Columns. Per index. 50 ; Columns. Supported ... Feb 20, 2024 ... Vectors are very long and complex. For instance, OpenAI's vector size is typically 1536, which means each embedding is an array of 1536 floating ... output_dimensionality. Optional: int. Used to specify output embedding size. If set, output embeddings will be truncated to the size specified. model = keras.Sequential() >>> model.add(keras.layers.Embedding(1000, 64)) >>> # The model will take as input an integer matrix of size (batch, > ... Mar 7, 2025 ... We've improved our context length from previous models allowing you to embed large chunks of text, code, or other data. Output dimensions of 3K ... Apr 8, 2023 ... The output dimension is mantained, since values coming from each attention head are concatenated and then projected with a matrix WO∈Rhdv×dmodel ... In this paper, we suggest a simple method for estimating τ ∗ and τ w based on symbolic analysis and symbolic entropy.