1,185 research outputs found
A generalized Gaussian process model for computer experiments with binary time series
Non-Gaussian observations such as binary responses are common in some
computer experiments. Motivated by the analysis of a class of cell adhesion
experiments, we introduce a generalized Gaussian process model for binary
responses, which shares some common features with standard GP models. In
addition, the proposed model incorporates a flexible mean function that can
capture different types of time series structures. Asymptotic properties of the
estimators are derived, and an optimal predictor as well as its predictive
distribution are constructed. Their performance is examined via two simulation
studies. The methodology is applied to study computer simulations for cell
adhesion experiments. The fitted model reveals important biological information
in repeated cell bindings, which is not directly observable in lab experiments.Comment: 49 pages, 4 figure
Relaxations and Cutting Planes for Linear Programs with Complementarity Constraints
We study relaxations for linear programs with complementarity constraints,
especially instances whose complementary pairs of variables are not
independent. Our formulation is based on identifying vertex covers of the
conflict graph of the instance and generalizes the extended
reformulation-linearization technique of Nguyen, Richard, and Tawarmalani to
instances with general complementarity conditions between variables. We
demonstrate how to obtain strong cutting planes for our formulation from both
the stable set polytope and the boolean quadric polytope associated with a
complete bipartite graph. Through an extensive computational study for three
types of practical problems, we assess the performance of our proposed linear
relaxation and new cutting-planes in terms of the optimality gap closed
MobileOne: An Improved One millisecond Mobile Backbone
Efficient neural network backbones for mobile devices are often optimized for
metrics such as FLOPs or parameter count. However, these metrics may not
correlate well with latency of the network when deployed on a mobile device.
Therefore, we perform extensive analysis of different metrics by deploying
several mobile-friendly networks on a mobile device. We identify and analyze
architectural and optimization bottlenecks in recent efficient neural networks
and provide ways to mitigate these bottlenecks. To this end, we design an
efficient backbone MobileOne, with variants achieving an inference time under 1
ms on an iPhone12 with 75.9% top-1 accuracy on ImageNet. We show that MobileOne
achieves state-of-the-art performance within the efficient architectures while
being many times faster on mobile. Our best model obtains similar performance
on ImageNet as MobileFormer while being 38x faster. Our model obtains 2.3%
better top-1 accuracy on ImageNet than EfficientNet at similar latency.
Furthermore, we show that our model generalizes to multiple tasks - image
classification, object detection, and semantic segmentation with significant
improvements in latency and accuracy as compared to existing efficient
architectures when deployed on a mobile device. Code and models are available
at https://github.com/apple/ml-mobileoneComment: Accepted at CVPR 202
FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
The recent amalgamation of transformer and convolutional designs has led to
steady improvements in accuracy and efficiency of the models. In this work, we
introduce FastViT, a hybrid vision transformer architecture that obtains the
state-of-the-art latency-accuracy trade-off. To this end, we introduce a novel
token mixing operator, RepMixer, a building block of FastViT, that uses
structural reparameterization to lower the memory access cost by removing
skip-connections in the network. We further apply train-time
overparametrization and large kernel convolutions to boost accuracy and
empirically show that these choices have minimal effect on latency. We show
that - our model is 3.5x faster than CMT, a recent state-of-the-art hybrid
transformer architecture, 4.9x faster than EfficientNet, and 1.9x faster than
ConvNeXt on a mobile device for the same accuracy on the ImageNet dataset. At
similar latency, our model obtains 4.2% better Top-1 accuracy on ImageNet than
MobileOne. Our model consistently outperforms competing architectures across
several tasks -- image classification, detection, segmentation and 3D mesh
regression with significant improvement in latency on both a mobile device and
a desktop GPU. Furthermore, our model is highly robust to out-of-distribution
samples and corruptions, improving over competing robust models. Code and
models are available at https://github.com/apple/ml-fastvit.Comment: ICCV 202
- …