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Found 10 papers, 0 papers with code
Intrinsic uncertainties and where to find them
We introduce a framework for uncertainty estimation that both describes and extends many existing methods.
Explanatory Masks for Neural Network Interpretability
Neural network interpretability is a vital component for applications across a wide variety of domains.
Metric-Based Few-Shot Learning for Video Action Recognition
In this work, we address the task of few-shot video action recognition with a set of two-stream models.
Sparse hierarchical representation learning on molecular graphs
Architectures for sparse hierarchical representation learning have recently been proposed for graph-structured data, but so far assume the absence of edge features in the graph.
Predicting Foreign Language Usage from English-Only Social Media Posts
We rely on this corpus to build predictive models to infer non-English languages that users speak exclusively from their English tweets.
Few-Shot Learning with Metric-Agnostic Conditional Embeddings
Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning.
Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams
Language in social media is a dynamic system, constantly evolving and adapting, with words and concepts rapidly emerging, disappearing, and changing their meaning.
Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks
Increasingly, cognitive scientists have demonstrated interest in applying tools from deep learning.
