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07/27/2023
Automatic Parallelization of Software Network Functions
Software network functions (NFs) trade-off flexibility and ease of deplo...
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08/09/2022
Representation learning of rare temporal conditions for travel time prediction
Predicting travel time under rare temporal conditions (e.g., public holi...
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05/02/2022
VICE: Variational Interpretable Concept Embeddings
A central goal in the cognitive sciences is the development of numerical...
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06/22/2020
Understanding Object Affordances Through Verb Usage Patterns
In order to interact with objects in our environment, we rely on an unde...
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04/23/2020
Evaluating Adversarial Robustness for Deep Neural Network Interpretability using fMRI Decoding
While deep neural networks (DNNs) are being increasingly used to make pr...
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06/10/2019
Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models
Specifying utility functions is a key step towards applying the discrete...
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01/09/2019
Revealing interpretable object representations from human behavior
To study how mental object representations are related to behavior, we e...
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12/03/2018
Knowing what you know in brain segmentation using deep neural networks
In this paper, we describe a deep neural network trained to predict Free...
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08/17/2018
Learning Supervised Topic Models for Classification and Regression from Crowds
The growing need to analyze large collections of documents has led to gr...
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08/16/2018
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach
Accurate time-series forecasting is vital for numerous areas of applicat...
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05/28/2018
Parallel Weight Consolidation: A Brain Segmentation Case Study
Collecting the large datasets needed to train deep neural networks can b...
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02/05/2018
Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings
The words of a language reflect the structure of the human mind, allowin...
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09/06/2017