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Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks
There is a lot of research interest in encoding variable length sentences into fixed length vectors, in a way that preserves the sentence meanings. Two common methods include representations based onExpand
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Tradeoffs and Constraints on Neural Representation in Networks of Cortical Neurons
Neural representation is pivotal in neuroscience. Yet, the large number and variance of underlying determinants make it difficult to distinguish general physiologic constraints on representation.Expand
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Order-Based Representation in Random Networks of Cortical Neurons
The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on aExpand
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On the Precarious Path of Reverse Neuro-Engineering
In this perspective we provide an example for the limits of reverse engineering in neuroscience. We demonstrate that application of reverse engineering to the study of the design principle of aExpand
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Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications
Existing applications include a huge amount of knowledge that is out of reach for deep neural networks. This paper presents a novel approach for integrating calls to existing applications into deepExpand
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Analysis of advanced meter infrastructure data of water consumption in apartment buildings
We present our experience of using machine learning techniques over data originating from advanced meter infrastructure (AMI) systems for water consumption in a medium-size city. We focus on two newExpand
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Analysis of sentence embedding models using prediction tasks in natural language processing
The tremendous success of word embeddings in improving the ability of computers to perform natural language tasks has shifted the research on language representation from word representation to focusExpand
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Neural network gradient-based learning of black-box function interfaces
Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods. At the same time, there is a vast amount of existing functionsExpand
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The Verification Cockpit - Creating the Dream Playground for Data Analytics over the Verification Process
The Verification Cockpit (VC) is a consolidated platform for planning, tracking, analysis, and optimization of large scale verification projects. Its prime role is to provide decision support fromExpand
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Template Aware Coverage - taking coverage analysis to the next level
Understanding the relationship between coverage and test-templates (a generic term we use to describe the inputs for the random stimuli generator) is an important layer in understanding the state andExpand
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