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Found 5 papers, 2 papers with code
Quantum Semi-Supervised Kernel Learning
While the availability of data for training machine learning models is steadily increasing, oftentimes it is much easier to collect feature vectors that to obtain the corresponding labels.
Shapeshifter: a Parameter-efficient Transformer using Factorized Reshaped Matrices
Language models employ a very large number of trainable parameters.
CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task
For the second subtask, which is to select the reason why a statement does not make sense, we stand within the first six teams (93. 7%) among 27 participants with very competitive results.
word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement
Our approach achieves a hundred-fold or more reduction in the space required to store the embeddings with almost no relative drop in accuracy in practical natural language processing tasks.
Quantum Sparse Support Vector Machines
We analyze the computational complexity of Quantum Sparse Support Vector Machine, a linear classifier that minimizes the hinge loss and the $L_1$ norm of the feature weights vector and relies on a quantum linear programming solver instead of a classical solver.
