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07/09/2020
Wandering Within a World: Online Contextualized Few-Shot Learning
We aim to bridge the gap between typical human and machine-learning envi...
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10/02/2019
Efficient Graph Generation with Graph Recurrent Attention Networks
We propose a new family of efficient and expressive deep generative mode...
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01/06/2019
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
We propose the Lanczos network (LanczosNet), which uses the Lanczos algo...
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10/16/2018
Incremental Few-Shot Learning with Attention Attractor Networks
Machine learning classifiers are often trained to recognize a set of pre...
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03/02/2018
Meta-Learning for Semi-Supervised Few-Shot Classification
In few-shot classification, we are interested in learning algorithms tha...
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03/15/2017
Prototypical Networks for Few-shot Learning
We propose prototypical networks for the problem of few-shot classificat...
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11/14/2016
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes
Normalization techniques have only recently begun to be exploited in sup...
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05/30/2016
End-to-End Instance Segmentation with Recurrent Attention
While convolutional neural networks have gained impressive success recen...
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06/22/2015
Skip-Thought Vectors
We describe an approach for unsupervised learning of a generic, distribu...
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11/10/2014
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
Inspired by recent advances in multimodal learning and machine translati...
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02/05/2014
Input Warping for Bayesian Optimization of Non-stationary Functions
Bayesian optimization has proven to be a highly effective methodology fo...
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10/19/2012
Efficient Parametric Projection Pursuit Density Estimation
Product models of low dimensional experts are a powerful way to avoid th...
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10/19/2012
Active Collaborative Filtering
Collaborative filtering (CF) allows the preferences of multiple users to...
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10/16/2012
Fast Exact Inference for Recursive Cardinality Models
Cardinality potentials are a generally useful class of high order potent...
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06/20/2012
Collaborative Filtering and the Missing at Random Assumption
Rating prediction is an important application, and a popular research to...
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02/14/2012
A Framework for Optimizing Paper Matching
At the heart of many scientific conferences is the problem of matching s...
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07/09/2011
Loss-sensitive Training of Probabilistic Conditional Random Fields
We consider the problem of training probabilistic conditional random fie...
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06/09/2011
Ranking via Sinkhorn Propagation
It is of increasing importance to develop learning methods for ranking. ...
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05/05/2011