Variational optimization of neural-network representations of quantum states has been successfully applied to solve interacting fermionic problems.
We propose CodedVTR (Codebook-based Voxel TRansformer), which improves data efficiency and generalization ability for 3D sparse voxel transformers.
Through this dynamic precision framework, we can reduce the bit-width of convolution, which is the most computational cost, while keeping the training process close to the full precision floating-point training.
In this paper, considering scenarios with capacity budget, we aim to discover adversarially robust architecture at targeted capacities.
We propose a new method to detect deepfake images using the cue of the source feature inconsistency within the forged images.
Neural Architecture Search (NAS) has received extensive attention due to its capability to discover neural network architectures in an automated manner.
We also design the micro-level search space to strengthen the information flow for BNN.
An identification is found between meta-learning and the problem of determining the ground state of a randomly generated Hamiltonian drawn from a known ensemble.
A major challenge in NAS is to conduct a fast and accurate evaluation of neural architectures.
For training a variety of models on CIFAR-10, using 1-bit mantissa and 2-bit exponent is adequate to keep the accuracy loss within $1\%$.
A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization.
In budgeted pruning, how to distribute the resources across layers (i. e., sparsity allocation) is the key problem.
Experimental results on various search spaces confirm GATES's effectiveness in improving the performance predictor.
Unlike the Variational Autoencoder framework, IMAE starts from a stochastic encoder that seeks to map each input data to a hybrid discrete and continuous representation with the objective of maximizing the mutual information between the data and their representations.
We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN).
We interpret part of the experimental results of Shwartz-Ziv and Tishby .