
Using Image Priors to Improve Scene Understanding
Semantic segmentation algorithms that can robustly segment objects acros...
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Correcting the bias in least squares regression with volumerescaled sampling
Consider linear regression where the examples are generated by an unknow...
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Estimating Aggregate Properties In Relational Networks With Unobserved Data
Aggregate network properties such as cluster cohesion and the number of ...
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Replication Markets: Results, Lessons, Challenges and Opportunities in AI Replication
The last decade saw the emergence of systematic largescale replication ...
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Incentives for Federated Learning: a Hypothesis Elicitation Approach
Federated learning provides a promising paradigm for collecting machine ...
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Hopfield Neural Network Flow: A Geometric Viewpoint
We provide gradient flow interpretations for the continuoustime continu...
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Private Stochastic Convex Optimization with Optimal Rates
We study differentially private (DP) algorithms for stochastic convex op...
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A Gaussian Process Upsampling Model for Improvements in Optical Character Recognition
Optical Character Recognition and extraction is a key tool in the automa...
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Reinforcement Learning with Perturbed Rewards
Recent studies have shown the vulnerability of reinforcement learning (R...
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User Profiling Using Hingeloss Markov Random Fields
A variety of approaches have been proposed to automatically infer the pr...
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Structured QueryBased Image Retrieval Using Scene Graphs
A structured query can capture the complexity of object interactions (e....
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Obliviousness Makes Poisoning Adversaries Weaker
Poisoning attacks have emerged as a significant security threat to machi...
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Scalable Structure Learning for Probabilistic Soft Logic
Statistical relational frameworks such as Markov logic networks and prob...
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Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Sensitive statistics are often collected across sets of users, with repe...
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Unbiased estimators for random design regression
In linear regression we wish to estimate the optimum linear least square...
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TimeInvariant LDPC Convolutional Codes
Spatially coupled codes have been shown to universally achieve the capac...
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Dynamic Influence Networks for Rulebased Models
We introduce the Dynamic Influence Network (DIN), a novel visual analyti...
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Learning User Intent from Action Sequences on Interactive Systems
Interactive systems have taken over the web and mobile space with increa...
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Using Noisy Extractions to Discover Causal Knowledge
Knowledge bases (KB) constructed through information extraction from tex...
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Modelling Protagonist Goals and Desires in FirstPerson Narrative
Many genres of natural language text are narratively structured, a testa...
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Inference of FineGrained Event Causality from Blogs and Films
Human understanding of narrative is mainly driven by reasoning about cau...
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Learning FineGrained Knowledge about Contingent Relations between Everyday Events
Much of the usergenerated content on social media is provided by ordina...
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Automatic Mapping of NES Games with Mappy
Game maps are useful for human players, generalgameplaying agents, and...
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Mechanics Automatically Recognized via Interactive Observation: Jumping
Jumping has been an important mechanic since its introduction in Donkey ...
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CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis
We propose and evaluate a new technique for learning hybrid automata aut...
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Automated Game Design Learning
While general game playing is an active field of research, the learning ...
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Super Mario as a String: Platformer Level Generation Via LSTMs
The procedural generation of video game levels has existed for at least ...
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Surprising properties of dropout in deep networks
We analyze dropout in deep networks with rectified linear units and the ...
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I Probe, Therefore I Am: Designing a Virtual Journalist with Human Emotions
By utilizing different communication channels, such as verbal language, ...
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BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies
We propose BlackOut, an approximation algorithm to efficiently train mas...
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Procedural Content Generation via Machine Learning (PCGML)
This survey explores Procedural Content Generation via Machine Learning ...
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Twotemperature logistic regression based on the Tsallis divergence
We develop a variant of multiclass logistic regression that achieves thr...
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On the Inductive Bias of Dropout
Dropout is a simple but effective technique for learning in neural netwo...
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Lowdimensional Data Embedding via Robust Ranking
We describe a new method called tETE for finding a lowdimensional embe...
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Learning From Graph Neighborhoods Using LSTMs
Many prediction problems can be phrased as inferences over local neighbo...
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Bayesian NonHomogeneous Markov Models via PolyaGamma Data Augmentation with Applications to Rainfall Modeling
Discretetime hidden Markov models are a broadly useful class of latent...
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Cascaded SegmentationDetection Networks for WordLevel Text Spotting
We introduce an algorithm for wordlevel text spotting that is able to a...
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HingeLoss Markov Random Fields and Probabilistic Soft Logic
A fundamental challenge in developing highimpact machine learning techn...
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Expectation Consistent Approximate Inference: Generalizations and Convergence
Approximations of loopy belief propagation, including expectation propag...
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WordRank: Learning Word Embeddings via Robust Ranking
Embedding words in a vector space has gained a lot of attention in recen...
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Bayesian Conditional Density Filtering
We propose a Conditional Density Filtering (CDF) algorithm for efficien...
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A Semiparametric Bayesian Extreme Value Model Using a Dirichlet Process Mixture of Gamma Densities
In this paper we propose a model with a Dirichlet process mixture of gam...
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Gaussian Processes and Limiting Linear Models
Gaussian processes retain the linear model either as a special case, or ...
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Text Annotation Graphs: Annotating Complex Natural Language Phenomena
This paper introduces a new webbased software tool for annotating text,...
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Summarizing Dialogic Arguments from Social Media
Online argumentative dialog is a rich source of information on popular b...
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A Dual Encoder Sequence to Sequence Model for OpenDomain Dialogue Modeling
Ever since the successful application of sequence to sequence learning f...
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Topic Based Sentiment Analysis Using Deep Learning
In this paper , we tackle Sentiment Analysis conditioned on a Topic in T...
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"How May I Help You?": Modeling Twitter Customer Service Conversations Using FineGrained Dialogue Acts
Given the increasing popularity of customer service dialogue on Twitter,...
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Combining Search with Structured Data to Create a More Engaging User Experience in Open Domain Dialogue
The greatest challenges in building sophisticated opendomain conversati...
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Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue
The use of irony and sarcasm in social media allows us to study them at ...
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