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Found 8 papers, 0 papers with code
Reward-Free Attacks in Multi-Agent Reinforcement Learning
We investigate how effective an attacker can be when it only learns from its victim's actions, without access to the victim's reward.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Prototypical Region Proposal Networks for Few-Shot Localization and Classification
Recently proposed few-shot image classification methods have generally focused on use cases where the objects to be classified are the central subject of images.
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.
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems
The Koopman operator has recently garnered much attention for its value in dynamical systems analysis and data-driven model discovery.
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.
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter
In this work we build predictive models to classify 130 thousand news posts as suspicious or verified, and predict four sub-types of suspicious news {--} satire, hoaxes, clickbait and propaganda.
Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks
Increasingly, cognitive scientists have demonstrated interest in applying tools from deep learning.
