74 research outputs found
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints
Distributionally robust optimization has been shown to offer a principled way
to regularize learning models. In this paper, we find that Tikhonov
regularization is distributionally robust in an optimal transport sense (i.e.,
if an adversary chooses distributions in a suitable optimal transport
neighborhood of the empirical measure), provided that suitable martingale
constraints are also imposed. Further, we introduce a relaxation of the
martingale constraints which not only provides a unified viewpoint to a class
of existing robust methods but also leads to new regularization tools. To
realize these novel tools, tractable computational algorithms are proposed. As
a byproduct, the strong duality theorem proved in this paper can be potentially
applied to other problems of independent interest.Comment: Accepted by NeurIPS 202
Aberrant resting-state brain activity in Huntington's disease: A voxel-based meta-analysis
IntroductionFunctional neuroimaging could provide abundant information of underling pathophysiological mechanisms of the clinical triad including motor, cognitive and psychiatric impairment in Huntington's Disease (HD).MethodsWe performed a voxel-based meta-analysis using anisotropic effect size-signed differential mapping (AES-SDM) method.Results6 studies (78 symptomatic HD, 102 premanifest HD and 131 healthy controls) were included in total. Altered resting-state brain activity was primarily detected in the bilateral medial part of superior frontal gyrus, bilateral anterior cingulate/paracingulate gyrus, left insula, left striatum, right cortico-spinal projections area, right inferior temporal gyrus area, right thalamus, right cerebellum and right gyrus rectus area. Premanifest and symptomatic HD patients showed different alterative pattern in the subgroup analyses.DiscussionThe robust and consistent abnormalities in the specific brain regions identified in the current study could help to understand the pathophysiology of HD and explore reliable neuroimaging biomarkers for monitoring disease progression, or even predicting the onset of premanifest HD patients
Graph-guided Architecture Search for Real-time Semantic Segmentation
Designing a lightweight semantic segmentation network often requires
researchers to find a trade-off between performance and speed, which is always
empirical due to the limited interpretability of neural networks. In order to
release researchers from these tedious mechanical trials, we propose a
Graph-guided Architecture Search (GAS) pipeline to automatically search
real-time semantic segmentation networks. Unlike previous works that use a
simplified search space and stack a repeatable cell to form a network, we
introduce a novel search mechanism with new search space where a lightweight
model can be effectively explored through the cell-level diversity and
latencyoriented constraint. Specifically, to produce the cell-level diversity,
the cell-sharing constraint is eliminated through the cell-independent manner.
Then a graph convolution network (GCN) is seamlessly integrated as a
communication mechanism between cells. Finally, a latency-oriented constraint
is endowed into the search process to balance the speed and performance.
Extensive experiments on Cityscapes and CamVid datasets demonstrate that GAS
achieves the new state-of-the-art trade-off between accuracy and speed. In
particular, on Cityscapes dataset, GAS achieves the new best performance of
73.5% mIoU with speed of 108.4 FPS on Titan Xp.Comment: CVPR202
Controllable Multi-Objective Re-ranking with Policy Hypernetworks
Multi-stage ranking pipelines have become widely used strategies in modern
recommender systems, where the final stage aims to return a ranked list of
items that balances a number of requirements such as user preference,
diversity, novelty etc. Linear scalarization is arguably the most widely used
technique to merge multiple requirements into one optimization objective, by
summing up the requirements with certain preference weights. Existing
final-stage ranking methods often adopt a static model where the preference
weights are determined during offline training and kept unchanged during online
serving. Whenever a modification of the preference weights is needed, the model
has to be re-trained, which is time and resources inefficient. Meanwhile, the
most appropriate weights may vary greatly for different groups of targeting
users or at different time periods (e.g., during holiday promotions). In this
paper, we propose a framework called controllable multi-objective re-ranking
(CMR) which incorporates a hypernetwork to generate parameters for a re-ranking
model according to different preference weights. In this way, CMR is enabled to
adapt the preference weights according to the environment changes in an online
manner, without retraining the models. Moreover, we classify practical
business-oriented tasks into four main categories and seamlessly incorporate
them in a new proposed re-ranking model based on an Actor-Evaluator framework,
which serves as a reliable real-world testbed for CMR. Offline experiments
based on the dataset collected from Taobao App showed that CMR improved several
popular re-ranking models by using them as underlying models. Online A/B tests
also demonstrated the effectiveness and trustworthiness of CMR
Total and dark mass from observations of galaxy centers with Machine Learning
The galaxy total mass inside the effective radius encode important
information on the dark matter and galaxy evolution model. Total "central"
masses can be inferred via galaxy dynamics or with gravitational lensing, but
these methods have limitations. We propose a novel approach, based on Random
Forest, to make predictions on the total and dark matter content of galaxies
using simple observables from imaging and spectroscopic surveys. We use
catalogs of multi-band photometry, sizes, stellar mass, kinematic
"measurements" (features) and dark matter (targets) of simulated galaxies, from
Illustris-TNG100 hydrodynamical simulation, to train a Mass Estimate machine
Learning Algorithm (Mela). We separate the simulated sample in passive
early-type galaxies (ETGs), both "normal" and "dwarf", and active late-type
galaxies (LTGs) and show that the mass estimator can accurately predict the
galaxy dark masses inside the effective radius in all samples. We finally test
the mass estimator against the central mass estimates of a series of low
redshift (z0.1) datasets, including SPIDER, MaNGA/DynPop and SAMI dwarf
galaxies, derived with standard dynamical methods based on Jeans equations.
Dynamical masses are reproduced within 0.30 dex (), with a limited
fraction of outliers and almost no bias. This is independent of the
sophistication of the kinematical data collected (fiber vs. 3D spectroscopy)
and the dynamical analysis adopted (radial vs. axisymmetric Jeans equations,
virial theorem). This makes Mela a powerful alternative to predict the mass of
galaxies of massive stage-IV surveys' datasets
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