OpenAI recently introduced a new Truncatable - Matryoshka Embeddings, allowing the use of chunks of dimensions from the total 2048d vector. Peng et al. introduced the Receptance Weighted Key Value (RWKV) model, combining aspects of Transformers and RNNs. NEAR developed neural embeddings for amino acid relationships and trained a ResNet embedding model using N-pairs loss function. Additionally, NEAR's neural embedding model computes per-residue embeddings for protein sequences and is implemented as a 1D Residual Convolutional Neural Network.
NEAR is implemented as a 1D Residual Convolutional Neural Network. A batch of sequences is initially embedded as a [batch x 256 Xseq length tensor using a context-unaware residue embedding layer. The tensor is then passed through 8 residual blocks.
NEAR's ResNet embedding model is trained using an N-pairs loss function guided by sequence alignments generated by the widely used HMMER3 tool.
NEAR's neural embedding model computes per-residue embeddings for target and query protein sequences, and identifies alignment candidates with a pipeline consisting of k-NN search, filtration, and neighbor aggregation.
DeepGOMeta can predict protein functions even in the absence of explicit sequence similarity or homology to known proteins. For measuring the semantic similarity between protein pairs, DeepGOMeta utilized Resnik's similarity method, combined with Best Match Average strategy.
NEAR: Neural Embeddings for Amino acid Relationships https://t.co/7FRgN1wUPj #biorxiv_bioinfo
📌 The Receptance Weighted Key Value (RWKV) model introduced by Peng et al. aims to reconcile the trade-off between computational efficiency and model performance in sequence processing tasks. 📌 RWKV combines aspects of both Transformers and RNNs into a novel architecture that… https://t.co/wPjCytpExe https://t.co/KncPEkfLmO
An Overview of OpenAI's New Truncatable - Matryoshka Embeddings🪆 OpenAI recently announced embeddings that you can simply use chunks of (say the first 8, 16, 32, 64, 128 or 256 ... dimensions of the total 2048d vector) they use Matryoshka representation learning(MRL). This is… https://t.co/srsEF2DjzN