139 research outputs found
Zero Shot Learning with the Isoperimetric Loss
We introduce the isoperimetric loss as a regularization criterion for
learning the map from a visual representation to a semantic embedding, to be
used to transfer knowledge to unknown classes in a zero-shot learning setting.
We use a pre-trained deep neural network model as a visual representation of
image data, a Word2Vec embedding of class labels, and linear maps between the
visual and semantic embedding spaces. However, the spaces themselves are not
linear, and we postulate the sample embedding to be populated by noisy samples
near otherwise smooth manifolds. We exploit the graph structure defined by the
sample points to regularize the estimates of the manifolds by inferring the
graph connectivity using a generalization of the isoperimetric inequalities
from Riemannian geometry to graphs. Surprisingly, this regularization alone,
paired with the simplest baseline model, outperforms the state-of-the-art among
fully automated methods in zero-shot learning benchmarks such as AwA and CUB.
This improvement is achieved solely by learning the structure of the underlying
spaces by imposing regularity.Comment: Accepted to AAAI-2
Graph signal processing for machine learning: a review and new perspectives
The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains, such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to address this challenge. In this article, we review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. In particular, our discussion focuses on the following three aspects: exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. Furthermore, we provide new perspectives on the future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side and machine learning and network science on the other. Cross-fertilization across these different disciplines may help unlock the numerous challenges of complex data analysis in the modern age
Graph signal processing for machine learning: A review and new perspectives
The effective representation, processing, analysis, and visualization of
large-scale structured data, especially those related to complex domains such
as networks and graphs, are one of the key questions in modern machine
learning. Graph signal processing (GSP), a vibrant branch of signal processing
models and algorithms that aims at handling data supported on graphs, opens new
paths of research to address this challenge. In this article, we review a few
important contributions made by GSP concepts and tools, such as graph filters
and transforms, to the development of novel machine learning algorithms. In
particular, our discussion focuses on the following three aspects: exploiting
data structure and relational priors, improving data and computational
efficiency, and enhancing model interpretability. Furthermore, we provide new
perspectives on future development of GSP techniques that may serve as a bridge
between applied mathematics and signal processing on one side, and machine
learning and network science on the other. Cross-fertilization across these
different disciplines may help unlock the numerous challenges of complex data
analysis in the modern age
Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations
Contrastive learning is a highly effective method which uses unlabeled data
to produce representations which are linearly separable for downstream
classification tasks. Recent works have shown that contrastive representations
are not only useful when data come from a single domain, but are also effective
for transferring across domains. Concretely, when contrastive representations
are trained on data from two domains (a source and target) and a linear
classification head is trained to predict labels using only the labeled source
data, the resulting classifier also exhibits good transfer to the target
domain. In this work, we analyze this linear transferability phenomenon,
building upon the framework proposed by HaoChen et al (2021) which relates
contrastive learning to spectral clustering of a positive-pair graph on the
data. We prove that contrastive representations capture relationships between
subpopulations in the positive-pair graph: linear transferability can occur
when data from the same class in different domains (e.g., photo dogs and
cartoon dogs) are connected in the graph. Our analysis allows the source and
target classes to have unbounded density ratios and be mapped to distant
representations. Our proof is also built upon technical improvements over the
main results of HaoChen et al (2021), which may be of independent interest
Zero-shot causal learning
Predicting how different interventions will causally affect a specific
individual is important in a variety of domains such as personalized medicine,
public policy, and online marketing. There are a large number of methods to
predict the effect of an existing intervention based on historical data from
individuals who received it. However, in many settings it is important to
predict the effects of novel interventions (\emph{e.g.}, a newly invented
drug), which these methods do not address. Here, we consider zero-shot causal
learning: predicting the personalized effects of a novel intervention. We
propose CaML, a causal meta-learning framework which formulates the
personalized prediction of each intervention's effect as a task. CaML trains a
single meta-model across thousands of tasks, each constructed by sampling an
intervention, along with its recipients and nonrecipients. By leveraging both
intervention information (\emph{e.g.}, a drug's attributes) and individual
features~(\emph{e.g.}, a patient's history), CaML is able to predict the
personalized effects of novel interventions that do not exist at the time of
training. Experimental results on real world datasets in large-scale medical
claims and cell-line perturbations demonstrate the effectiveness of our
approach. Most strikingly, CaML's zero-shot predictions outperform even strong
baselines trained directly on data from the test interventions
February 2, 2015
The Breeze is the student newspaper of James Madison University in Harrisonburg, Virginia
NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching
We present Neural Correspondence Prior (NCP), a new paradigm for computing
correspondences between 3D shapes. Our approach is fully unsupervised and can
lead to high-quality correspondences even in challenging cases such as sparse
point clouds or non-isometric meshes, where current methods fail. Our first key
observation is that, in line with neural priors observed in other domains,
recent network architectures on 3D data, even without training, tend to produce
pointwise features that induce plausible maps between rigid or non-rigid
shapes. Secondly, we show that given a noisy map as input, training a feature
extraction network with the input map as supervision tends to remove artifacts
from the input and can act as a powerful correspondence denoising mechanism,
both between individual pairs and within a collection. With these observations
in hand, we propose a two-stage unsupervised paradigm for shape matching by (i)
performing unsupervised training by adapting an existing approach to obtain an
initial set of noisy matches, and (ii) using these matches to train a network
in a supervised manner. We demonstrate that this approach significantly
improves the accuracy of the maps, especially when trained within a collection.
We show that NCP is data-efficient, fast, and achieves state-of-the-art results
on many tasks. Our code can be found online: https://github.com/pvnieo/NCP.Comment: NeurIPS 2022, 10 pages, 9 figure
SPECTRE : Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
We approach the graph generation problem from a spectral perspective by first
generating the dominant parts of the graph Laplacian spectrum and then building
a graph matching these eigenvalues and eigenvectors. Spectral conditioning
allows for direct modeling of the global and local graph structure and helps to
overcome the expressivity and mode collapse issues of one-shot graph
generators. Our novel GAN, called SPECTRE, enables the one-shot generation of
much larger graphs than previously possible with one-shot models. SPECTRE
outperforms state-of-the-art deep autoregressive generators in terms of
modeling fidelity, while also avoiding expensive sequential generation and
dependence on node ordering. A case in point, in sizable synthetic and
real-world graphs SPECTRE achieves a 4-to-170 fold improvement over the best
competitor that does not overfit and is 23-to-30 times faster than
autoregressive generators.Comment: 20 pages, 10 figure
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