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Fast Graph Learning with Unique Optimal Solutions
Graph Representation Learning (GRL) has been advancing at an unprecedent...
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Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning
Graph Representation Learning (GRL) methods have impacted fields from ch...
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DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation
Having engaging and informative conversations with users is the utmost g...
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Likelihood Ratio Exponential Families
The exponential family is well known in machine learning and statistical...
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Annealed Importance Sampling with q-Paths
Annealed importance sampling (AIS) is the gold standard for estimating p...
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MUSCLE: Strengthening Semi-Supervised Learning Via Concurrent Unsupervised Learning Using Mutual Information Maximization
Deep neural networks are powerful, massively parameterized machine learn...
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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 Class-Dependent 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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Sequential Unsupervised Domain Adaptation through Prototypical Distributions
We develop an algorithm for unsupervised domain adaptation (UDA) of a cl...
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Event Cartography: Latent Point Process Embeddings
Many important phenomena arise naturally as temporal point processes wit...
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Anchor Attention for Hybrid Crowd Forecasts Aggregation
Forecasting the future is a notoriously difficult task. To overcome this...
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Improving Generalization by Controlling Label-Noise Information in Neural Network Weights
In the presence of noisy or incorrect labels, neural networks have the u...
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Towards Learning Representations of Binary Executable Files for Security Tasks
Tackling binary analysis problems has traditionally implied manually def...
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Predictive Engagement: An Efficient Metric For Automatic Evaluation of Open-Domain Dialogue Systems
User engagement is a critical metric for evaluating the quality of open-...
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Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition
We study the bias in several state-of-the-art named entity recognition (...
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Deep Structured Neural Network for Event Temporal Relation Extraction
We propose a novel deep structured learning framework for event temporal...
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Stacking Models for Nearly Optimal Link Prediction in Complex Networks
Most real-world networks are incompletely observed. Algorithms that can ...
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Nearly-Unsupervised Hashcode Representations for Relation Extraction
Recently, kernelized locality sensitive hashcodes have been successfully...
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A Survey on Bias and Fairness in Machine Learning
With the widespread use of AI systems and applications in our everyday l...
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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: Higher-Order Graph Convolution Architectures via Sparsified Neighborhood Mixing
Existing popular methods for semi-supervised learning with Graph Neural ...
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Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings
Despite advances in open-domain dialogue systems, automatic evaluation o...
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Exact Rate-Distortion in Autoencoders via Echo Noise
Compression is at the heart of effective representation learning. Howeve...
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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 Error-Correcting Codes
Multivariate mutual information provides a conceptual framework for char...
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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 Block-Free 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 machine-learning framework for dialogue modeling whic...
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Adaptive prior probabilities via optimization of risk and entropy
An agent choosing between various actions tends to take the one with the...
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Auto-Encoding 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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Efficient Representation for Natural Language Processing via Kernelized Hashcodes
Kernel similarity functions have been successfully applied in classifica...
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Unifying Local and Global Change Detection in Dynamic Networks
Many real-world networks are complex dynamical systems, where both local...
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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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Multitask Learning and Benchmarking with Clinical Time Series Data
Health care is one of the most exciting frontiers in data mining and mac...
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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 DARPA Twitter Bot Challenge
A number of organizations ranging from terrorist groups such as ISIS to ...
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Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text
We advance the state of the art in biomolecular interaction extraction w...
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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 information-theoretic approach
We suggest an information-theoretic approach for measuring stylistic coo...
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Scalable Link Prediction in Dynamic Networks via Non-Negative 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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Active Inference for Binary Symmetric Hidden Markov Models
We consider active maximum a posteriori (MAP) inference problem for Hidd...
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Maximally Informative Hierarchical Representations of High-Dimensional Data
We consider a set of probabilistic functions of some input variables as ...
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Discovering Structure in High-Dimensional Data Through Correlation Explanation
We introduce a method to learn a hierarchy of successively more abstract...
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Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs
We present an asymptotic analysis of Viterbi Training (VT) and contrast ...
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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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