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Found 6 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.
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.
Differentiable Combinatorial Losses through Generalized Gradients of Linear Programs
When samples have internal structure, we often see a mismatch between the objective optimized during training and the model's goal during inference.
Approximation Capabilities of Neural ODEs and Invertible Residual Networks
Neural ODEs and i-ResNet are recently proposed methods for enforcing invertibility of residual neural models.
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.
