Richard G. Baraniuk
12 papers · 2013–2024 · 8 venues · across top CS/AI venues
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Research topics
Machine Learning
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Core Methods
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Deep Learning
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Optimization & Theory
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Application Areas
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Learning Types · Machine Learning
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Computer Science
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Computer Vision
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Foundations
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Keywords
implicit neural representation
(2)
dimensionality reduction
(1)
piecewise linear
(1)
orthogonal matching pursuit
(1)
sparse recovery
(1)
feature learning
(1)
image generation
(1)
probabilistic modeling
(1)
transfer learning
(1)
distributed learning
(1)
wavelet transform
(1)
network visualization
(1)
deep learning
(1)
image denoising
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lottery ticket hypothesis
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model pruning
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signal recovery
(1)
early stopping
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training efficiency
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wasserstein distance
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Papers
Learning Transferable Features for Implicit Neural Representations
NeurIPS 2024
A Blessing of Dimensionality in Membership Inference through Regularization
AISTATS 2023
Evaluating Generative Networks Using Gaussian Mixtures of Image Features
WACV 2023
SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries
CVPR 2023
WIRE: Wavelet Implicit Neural Representations
CVPR 2023
MINER: Multiscale Implicit Neural Representation
ECCV 2022
Educational Question Mining At Scale: Prediction, Analysis and Personalization
AAAI 2021
Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks
ICLR 2020
A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery
ICLR 2019
Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks
ICLR 2019
Sparse Factor Analysis for Learning and Content Analytics
JMLR 2014
Greedy Feature Selection for Subspace Clustering
JMLR 2013