2,201 research outputs found
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis
Interpretability has emerged as a crucial aspect of machine learning, aimed
at providing insights into the working of complex neural networks. However,
existing solutions vary vastly based on the nature of the interpretability
task, with each use case requiring substantial time and effort. This paper
introduces MARGIN, a simple yet general approach to address a large set of
interpretability tasks ranging from identifying prototypes to explaining image
predictions. MARGIN exploits ideas rooted in graph signal analysis to determine
influential nodes in a graph, which are defined as those nodes that maximally
describe a function defined on the graph. By carefully defining task-specific
graphs and functions, we demonstrate that MARGIN outperforms existing
approaches in a number of disparate interpretability challenges.Comment: Technical Repor
Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships
Recent advances in fine-grained representation learning leverage
local-to-global (emergent) relationships for achieving state-of-the-art
results. The relational representations relied upon by such methods, however,
are abstract. We aim to deconstruct this abstraction by expressing them as
interpretable graphs over image views. We begin by theoretically showing that
abstract relational representations are nothing but a way of recovering
transitive relationships among local views. Based on this, we design
Transitivity Recovering Decompositions (TRD), a graph-space search algorithm
that identifies interpretable equivalents of abstract emergent relationships at
both instance and class levels, and with no post-hoc computations. We
additionally show that TRD is provably robust to noisy views, with empirical
evidence also supporting this finding. The latter allows TRD to perform at par
or even better than the state-of-the-art, while being fully interpretable.
Implementation is available at https://github.com/abhrac/trd.Comment: Neural Information Processing Systems (NeurIPS) 202
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
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