Search Results for author:
Found 16 papers, 5 papers with code
Testing for context-dependent changes in neural encoding in naturalistic experiments
We propose a decoding-based approach to detect context effects on neural codes in longitudinal neural recording data.
Representation learning of rare temporal conditions for travel time prediction
We present a vector-space model for encoding rare temporal conditions, that allows coherent representation learning across different temporal conditions.
VICE: Variational Interpretable Concept Embeddings
This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task.
Understanding Mental Representations Of Objects Through Verbs Applied To Them
Finally, we show that the dimensions can be used to predict a state-of-the-art mental representation of objects, derived purely from human judgements of object similarity.
A Deep Neural Network Tool for Automatic Segmentation of Human Body Parts in Natural Scenes
This short article describes a deep neural network trained to perform automatic segmentation of human body parts in natural scenes.
Mental representations of objects reflect the ways in which we interact with them
In order to interact with objects in our environment, humans rely on an understanding of the actions that can be performed on them, as well as their properties.
Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness
We introduce two quantitative evaluation procedures for saliency map methods in fMRI, applicable whenever a DNN or linear model is being trained to decode some information from imaging data.
Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models
Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices.
Revealing interpretable object representations from human behavior
To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images representative of 1, 854 object categories.
Knowing what you know in brain segmentation using Bayesian deep neural networks
In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours.
Learning Supervised Topic Models for Classification and Regression from Crowds
The growing need to analyze large collections of documents has led to great developments in topic modeling.
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 application such as transportation, energy, finance, economics, etc.
Distributed Weight Consolidation: A Brain Segmentation Case Study
Collecting the large datasets needed to train deep neural networks can be very difficult, particularly for the many applications for which sharing and pooling data is complicated by practical, ethical, or legal concerns.
Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings
Because related words appear in similar contexts, such spaces - called "word embeddings" - can be learned from patterns of lexical co-occurrences in natural language.
Deep learning from crowds
Over the last few years, deep learning has revolutionized the field of machine learning by dramatically improving the state-of-the-art in various domains.
A systematic approach to extracting semantic information from functional MRI data
This paper introduces a novel classification method for functional magnetic resonance imaging datasets with tens of classes.
