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Found 4 papers, 1 papers with code
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks
Our work quantitatively demonstrates that combining compression methods can synergistically reduce model size, and that practitioners should prioritize (1) quantization, (2) knowledge distillation, and (3) pruning to maximize accuracy vs. model size tradeoffs.
Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations
Our dataset consists of amino acid sequences, Q8 secondary structures, position specific scoring matrices, multiple sequence alignment co-evolutionary features, backbone atom distance matrices, torsion angles, and 3D coordinates.
Linguistically Regularized LSTM for Sentiment Classification
This paper deals with sentence-level sentiment classification.
Linguistically Regularized LSTMs for Sentiment Classification
In this paper, we propose simple models trained with sentence-level annotation, but also attempt to generating linguistically coherent representations by employing regularizers that model the linguistic role of sentiment lexicons, negation words, and intensity words.
