
Compressing Deep Neural Networks via Layer Fusion
This paper proposes layer fusion  a model compression technique that di...
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Robust Classification under ClassDependent Domain Shift
Investigation of machine learning algorithms robust to changes between t...
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All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference
The recently proposed Thermodynamic Variational Objective (TVO) leverage...
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Overview of Scanner Invariant Representations
Pooled imaging data from multiple sources is subject to bias from each s...
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Event Cartography: Latent Point Process Embeddings
Many important phenomena arise naturally as temporal point processes wit...
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Improving Generalization by Controlling LabelNoise Information in Neural Network Weights
In the presence of noisy or incorrect labels, neural networks have the u...
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Discovery and Separation of Features for Invariant Representation Learning
Supervised machine learning models often associate irrelevant nuisance f...
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Invariant Representations through Adversarial Forgetting
We propose a novel approach to achieving invariance for deep neural netw...
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NearlyUnsupervised Hashcode Representations for Relation Extraction
Recently, kernelized locality sensitive hashcodes have been successfully...
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Efficient Covariance Estimation from Temporal Data
Estimating the covariance structure of multivariate time series is a fun...
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MixHop: HigherOrder Graph Convolution Architectures via Sparsified Neighborhood Mixing
Existing popular methods for semisupervised learning with Graph Neural ...
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Exact RateDistortion in Autoencoders via Echo Noise
Compression is at the heart of effective representation learning. Howeve...
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Scanner Invariant Representations for Diffusion MRI Harmonization
Pooled imaging data from multiple sources is subject to variation betwee...
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Identifying and Analyzing Cryptocurrency Manipulations in Social Media
Interest surrounding cryptocurrencies, digital or virtual currencies tha...
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Maximizing Multivariate Information with ErrorCorrecting Codes
Multivariate mutual information provides a conceptual framework for char...
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Measures of Tractography Convergence
In the present work, we use information theory to understand the empiric...
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Evading the Adversary in Invariant Representation
Representations of data that are invariant to changes in specified nuisa...
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A Forest Mixture Bound for BlockFree Parallel Inference
Coordinate ascent variational inference is an important algorithm for in...
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Dialogue Modeling Via Hash Functions: Applications to Psychotherapy
We propose a novel machinelearning framework for dialogue modeling whic...
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AutoEncoding Total Correlation Explanation
Advances in unsupervised learning enable reconstruction and generation o...
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Stochastic Learning of Nonstationary Kernels for Natural Language Modeling
Natural language processing often involves computations with semantic or...
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Unifying Local and Global Change Detection in Dynamic Networks
Many realworld networks are complex dynamical systems, where both local...
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Unsupervised Learning via Total Correlation Explanation
Learning by children and animals occurs effortlessly and largely without...
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Low Complexity Gaussian Latent Factor Models and a Blessing of Dimensionality
Learning the structure of graphical models from data is a fundamental pr...
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Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge
While generative models such as Latent Dirichlet Allocation (LDA) have p...
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Toward Interpretable Topic Discovery via Anchored Correlation Explanation
Many predictive tasks, such as diagnosing a patient based on their medic...
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Variational Information Maximization for Feature Selection
Feature selection is one of the most fundamental problems in machine lea...
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Sifting Common Information from Many Variables
Measuring the relationship between any pair of variables is a rich and a...
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The Information Sieve
We introduce a new framework for unsupervised learning of representation...
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Understanding confounding effects in linguistic coordination: an informationtheoretic approach
We suggest an informationtheoretic approach for measuring stylistic coo...
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Scalable Link Prediction in Dynamic Networks via NonNegative Matrix Factorization
We propose a scalable temporal latent space model for link prediction in...
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Efficient Estimation of Mutual Information for Strongly Dependent Variables
We demonstrate that a popular class of nonparametric mutual information ...
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Maximally Informative Hierarchical Representations of HighDimensional Data
We consider a set of probabilistic functions of some input variables as ...
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Discovering Structure in HighDimensional Data Through Correlation Explanation
We introduce a method to learn a hierarchy of successively more abstract...
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Phase Transitions in Community Detection: A Solvable Toy Model
Recently, it was shown that there is a phase transition in the community...
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Coevolution of Selection and Influence in Social Networks
Many networks are complex dynamical systems, where both attributes of no...
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A Sequence of Relaxations Constraining Hidden Variable Models
Many widely studied graphical models with latent variables lead to nontr...
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