
PopulationBased BlackBox Optimization for Biological Sequence Design
The use of blackbox optimization for the design of new biological seque...
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Machine Learning on Graphs: A Model and Comprehensive Taxonomy
There has been a surge of recent interest in learning representations fo...
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Towards Differentiable Resampling
Resampling is a key component of samplebased recursive state estimation...
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Regularized Autoencoders via Relaxed Injective Probability Flow
Invertible flowbased generative models are an effective method for lear...
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The Garden of Forking Paths: Towards MultiFuture Trajectory Prediction
This paper studies the problem of predicting the distribution over multi...
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Unsupervised Learning of Object Structure and Dynamics from Videos
Extracting and predicting object structure and dynamics from videos with...
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Language as an Abstraction for Hierarchical Deep Reinforcement Learning
Solving complex, temporallyextended tasks is a longstanding problem in...
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Floors are Flat: Leveraging Semantics for RealTime Surface Normal Prediction
We propose 4 insights that help to significantly improve the performance...
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Contrastive Bidirectional Transformer for Temporal Representation Learning
This paper aims at learning representations for long sequences of contin...
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A view of Estimation of Distribution Algorithms through the lens of ExpectationMaximization
We show that under mild conditions, Estimation of Distribution Algorithm...
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Relational Action Forecasting
This paper focuses on multiperson action forecasting in videos. More pr...
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VideoBERT: A Joint Model for Video and Language Representation Learning
Selfsupervised learning has become increasingly important to leverage t...
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Unsupervised Discovery of Parts, Structure, and Dynamics
Humans easily recognize object parts and their hierarchical structure by...
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Stochastic Prediction of MultiAgent Interactions from Partial Observations
We present a method that learns to integrate temporal information, from ...
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NASBench101: Towards Reproducible Neural Architecture Search
Recent advances in neural architecture search (NAS) demand tremendous co...
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Composing Text and Image for Image Retrieval  An Empirical Odyssey
In this paper, we study the task of image retrieval, where the input que...
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Modeling Uncertainty with Hedged Instance Embedding
Instance embeddings are an efficient and versatile image representation ...
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ActorCentric Relation Network
Current stateoftheart approaches for spatiotemporal action localizat...
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Tracking Emerges by Colorizing Videos
We use large amounts of unlabeled video to learn models for visual track...
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PersonLab: Person Pose Estimation and Instance Segmentation with a BottomUp, PartBased, Geometric Embedding Model
We present a boxfree bottomup approach for the tasks of pose estimatio...
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Rethinking Spatiotemporal Feature Learning For Video Understanding
In this paper we study 3D convolutional networks for video understanding...
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Progressive Neural Architecture Search
We propose a method for learning CNN structures that is more efficient t...
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XGAN: Unsupervised ImagetoImage Translation for ManytoMany Mappings
Style transfer usually refers to the task of applying color and texture ...
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An InformationTheoretic Analysis of Deep LatentVariable Models
We present an informationtheoretic framework for understanding tradeof...
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Generative Models of Visually Grounded Imagination
It is easy for people to imagine what a man with pink hair looks like, e...
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PixColor: Pixel Recursive Colorization
We propose a novel approach to automatically produce multiple colorized ...
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Deep Probabilistic Programming
We propose Edward, a Turingcomplete probabilistic programming language....
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Contextaware Captions from Contextagnostic Supervision
We introduce an inference technique to produce discriminative contextaw...
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Towards Accurate Multiperson Pose Estimation in the Wild
We propose a method for multiperson detection and 2D pose estimation t...
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Deep Metric Learning via Facility Location
Learning the representation and the similarity metric in an endtoend f...
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Improved Image Captioning via Policy Gradient optimization of SPIDEr
Current image captioning methods are usually trained via (penalized) max...
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Speed/accuracy tradeoffs for modern convolutional object detectors
The goal of this paper is to serve as a guide for selecting a detection ...
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
In this work we address the task of semantic image segmentation with Dee...
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Semantic Image Segmentation with TaskSpecific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform
Deep convolutional neural networks (CNNs) are the backbone of stateofa...
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Detecting events and key actors in multiperson videos
Multiperson event recognition is a challenging task, often with many pe...
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Generation and Comprehension of Unambiguous Object Descriptions
We propose a method that can generate an unambiguous description (known ...
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Nonlinear functional mapping of the human brain
The field of neuroimaging has truly become data rich, and novel analytic...
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Bayesian Dark Knowledge
We consider the problem of Bayesian parameter estimation for deep neural...
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What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision
We present a novel method for aligning a sequence of instructions to a v...
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A Review of Relational Machine Learning for Knowledge Graphs
Relational machine learning studies methods for the statistical analysis...
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Weakly and SemiSupervised Learning of a DCNN for Semantic Image Segmentation
Deep convolutional neural networks (DCNNs) trained on a large number of ...
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Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Deep Convolutional Neural Networks (DCNNs) have recently shown state of ...
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Learning the Structure of Dynamic Probabilistic Networks
Dynamic probabilistic networks are a compact representation of complex s...
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Loopy Belief Propagation for Approximate Inference: An Empirical Study
Recently, researchers have demonstrated that loopy belief propagation  ...
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A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables
We show how to use a variational approximation to the logistic function ...
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Proceedings of the TwentyEighth Conference on Uncertainty in Artificial Intelligence (2012)
This is the Proceedings of the TwentyEighth Conference on Uncertainty i...
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RaoBlackwellised Particle Filtering for Dynamic Bayesian Networks
Particle filters (PFs) are powerful samplingbased inference/learning al...
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The Factored Frontier Algorithm for Approximate Inference in DBNs
The Factored Frontier (FF) algorithm is a simple approximate inferenceal...
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Bayesian structure learning using dynamic programming and MCMC
MCMC methods for sampling from the space of DAGs can mix poorly due to t...
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Modeling Discrete Interventional Data using Directed Cyclic Graphical Models
We outline a representation for discrete multivariate distributions in t...
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