Mobile SoCs can improve ML inference by 2x when all components are used concurrently.
problem Power-performance trade-offs in mobile ML inference on heterogeneous SoCs.
method Quantitative evaluation of different ML components on mobile SoCs.
result Mobile SoCs can achieve up to 2x improvement in inference performance by using all components concurrently.
The need to efficiently calculate first- and higher-order derivatives of increasingly complex models expressed in Python has stressed or exceeded the capabilities of available tools. In this work, we explore techniques from the field of automatic differentiation (AD) that can give researchers expressive power, performa…
Although deep learning has shown its powerful performance in many applications, the mathematical principles behind neural networks are still mysterious. In this paper, we consider the problem of learning a one-hidden-layer neural network with quadratic activations. We focus on the under-parameterized regime where the n…
Study evaluates RKHS choices for assessing graph models using KSD tests.
problem Effect of RKHS choice on KSD tests for graph model assessment.
method Investigated power performance and computational runtime of KSD tests for ERGMs and synthetic graph generators.
result Different RKHS choices affect KSD test performance and computational runtime.
Qlib aims to integrate AI into quantitative investment.
problem Challenges in applying AI to quantitative investment.
method Design and develop Qlib to accommodate AI-driven workflow.
result Qlib realizes the potential of AI technologies in quantitative investment.
Paper proposes using generalized lambda distributions for stochastic simulators.
problem Uncertainty quantification with complex stochastic models is computationally challenging.
method Flexible generalized lambda distribution approximates response PDF, parameters are sparse polynomial chaos expansions.
result Local inference of response PDF at each point of experimental design using replicated model evaluations.
Improves VAT method for semi-supervised learning with simpler techniques.
problem Improving semi-supervised learning performance with robust classifiers.
method Adopts Bad GAN approach to generate bad samples and employs adversarial training.
result Achieves competitive performance with fewer computations.
Paper detects and estimates breaks in high-dimensional functional time series.
problem Detecting and estimating structural breaks in heterogeneous mean functions of high-dimensional functional time series.
method Proposes a new test statistic combining functional CUSUM and power enhancement components, with a clustering algorithm for group structure estimation.
result The proposed techniques have satisfactory performance in finite samples, detecting and estimating breaks effectively.
Paper presents an algorithm to analyze DGNNs by identifying generative boundaries and sampling.
problem Challenges in identifying the generation mechanism of DGNNs.
method Explorative sampling algorithm to analyze DGNNs by defining generative boundaries and optimizing essential set.
result Empirically reveals the characteristics of DGNNs' internal layers and finds more homogeneous samples.
The paper analyzes the trade-off between smoothness and sparsity in GCN using lp-regularized learning.
problem Quantifying the trade-off between smoothness and sparsity in GCN.
method Proposes a novel SGD proximal algorithm for GCNs with an inexact operator to analyze the stability of the ℓp-regularized stochastic learning. result Establishes an explicit theoretical understanding of GCN with ℓp-regularized stochastic learning. This work tackles GAN training instability through parallel tempering.
problem Training instability and mode collapse in GANs.
method Introduces a parallel tempering framework to stabilize GAN training.
result Significantly reduces gradient variance and improves training efficiency.
Paper proposes a new method to assess synthetic data generators.
problem Assessing the quality of implicit generative models.
method Kernelised Stein Statistic (KSD) test based on non-parametric Stein operator.
result Improved power performance compared to existing approaches.
Study evaluates 31 performance predictors in NAS, recommending best for different settings.
problem Understanding and comparing different performance prediction techniques in NAS.
method Analysis of 31 techniques, testing correlation and rank-based measures, speed-up potential.
result Certain predictor families can be combined for better predictive power.
Paper develops statistical tests for covariance matrix regression on manifold.
problem Regression with random covariance matrices in Fréchet space.
method Develops Wasserstein F-tests for Bures-Wasserstein manifold.
result Asymptotic null distribution and power of the test.