1,040 research outputs found
The Riemannian Geometry of Deep Generative Models
Deep generative models learn a mapping from a low dimensional latent space to
a high-dimensional data space. Under certain regularity conditions, these
models parameterize nonlinear manifolds in the data space. In this paper, we
investigate the Riemannian geometry of these generated manifolds. First, we
develop efficient algorithms for computing geodesic curves, which provide an
intrinsic notion of distance between points on the manifold. Second, we develop
an algorithm for parallel translation of a tangent vector along a path on the
manifold. We show how parallel translation can be used to generate analogies,
i.e., to transport a change in one data point into a semantically similar
change of another data point. Our experiments on real image data show that the
manifolds learned by deep generative models, while nonlinear, are surprisingly
close to zero curvature. The practical implication is that linear paths in the
latent space closely approximate geodesics on the generated manifold. However,
further investigation into this phenomenon is warranted, to identify if there
are other architectures or datasets where curvature plays a more prominent
role. We believe that exploring the Riemannian geometry of deep generative
models, using the tools developed in this paper, will be an important step in
understanding the high-dimensional, nonlinear spaces these models learn.Comment: 9 page
Lift-Based Bidding in Ad Selection
Real-time bidding (RTB) has become one of the largest online advertising
markets in the world. Today the bid price per ad impression is typically
decided by the expected value of how it can lead to a desired action event
(e.g., registering an account or placing a purchase order) to the advertiser.
However, this industry standard approach to decide the bid price does not
consider the actual effect of the ad shown to the user, which should be
measured based on the performance lift among users who have been or have not
been exposed to a certain treatment of ads. In this paper, we propose a new
bidding strategy and prove that if the bid price is decided based on the
performance lift rather than absolute performance value, advertisers can
actually gain more action events. We describe the modeling methodology to
predict the performance lift and demonstrate the actual performance gain
through blind A/B test with real ad campaigns in an industry-leading
Demand-Side Platform (DSP). We also discuss the relationship between
attribution models and bidding strategies. We prove that, to move the DSPs to
bid based on performance lift, they should be rewarded according to the
relative performance lift they contribute.Comment: AAAI 201
A Novel Convolutional Neural Network Architecture with a Continuous Symmetry
This paper introduces a new Convolutional Neural Network (ConvNet)
architecture inspired by a class of partial differential equations (PDEs)
called quasi-linear hyperbolic systems. With comparable performance on the
image classification task, it allows for the modification of the weights via a
continuous group of symmetry. This is a significant shift from traditional
models where the architecture and weights are essentially fixed. We wish to
promote the (internal) symmetry as a new desirable property for a neural
network, and to draw attention to the PDE perspective in analyzing and
interpreting ConvNets in the broader Deep Learning community.Comment: Accepted by the 3rd CAAI International Conference on Artificial
Intelligence (CICAI), 2023; with Addendum + minor edit
M3DSSD: Monocular 3D Single Stage Object Detector
In this paper, we propose a Monocular 3D Single Stage object Detector
(M3DSSD) with feature alignment and asymmetric non-local attention. Current
anchor-based monocular 3D object detection methods suffer from feature
mismatching. To overcome this, we propose a two-step feature alignment
approach. In the first step, the shape alignment is performed to enable the
receptive field of the feature map to focus on the pre-defined anchors with
high confidence scores. In the second step, the center alignment is used to
align the features at 2D/3D centers. Further, it is often difficult to learn
global information and capture long-range relationships, which are important
for the depth prediction of objects. Therefore, we propose a novel asymmetric
non-local attention block with multi-scale sampling to extract depth-wise
features. The proposed M3DSSD achieves significantly better performance than
the monocular 3D object detection methods on the KITTI dataset, in both 3D
object detection and bird's eye view tasks.Comment: Accepted to CVPR 202
Building quantum neural networks based on swap test
Artificial neural network, consisting of many neurons in different layers, is
an important method to simulate humain brain. Usually, one neuron has two
operations: one is linear, the other is nonlinear. The linear operation is
inner product and the nonlinear operation is represented by an activation
function. In this work, we introduce a kind of quantum neuron whose inputs and
outputs are quantum states. The inner product and activation operator of the
quantum neurons can be realized by quantum circuits. Based on the quantum
neuron, we propose a model of quantum neural network in which the weights
between neurons are all quantum states. We also construct a quantum circuit to
realize this quantum neural network model. A learning algorithm is proposed
meanwhile. We show the validity of learning algorithm theoretically and
demonstrate the potential of the quantum neural network numerically.Comment: 10 pages, 13 figure
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