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Found 10 papers, 1 papers with code
Influence Diagram Bandits
We experiment with three structured bandit problems: cascading bandits, online learning to rank in the position-based model, and rank-1 bandits.
Customizing Graph Neural Networks using Path Reweighting
Specifically, the proposed CustomGNN can automatically learn the high-level semantics for specific downstream tasks to highlight semantically relevant paths as well to filter out task-irrelevant noises in a graph.
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems
We propose a novel framework for structured bandits, which we call an influence diagram bandit.
Customized Graph Embedding: Tailoring Embedding Vectors to different Applications
For these applications, graph embedding is crucial as it provides vector representations of the graph.
Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning
Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars.
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention
To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention.
Semi-Supervised Convolutional Neural Networks for Human Activity Recognition
Labeled data used for training activity recognition classifiers are usually limited in terms of size and diversity.
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models
Moreover, we report encouraging response selection performance of the proposed neural bandit model using the Recall@k metric for a small set of online training samples.
SpectralLeader: Online Spectral Learning for Single Topic Models
We study the problem of learning a latent variable model from a stream of data.
Interpretable Categorization of Heterogeneous Time Series Data
In particular, when a grammar based on temporal logic is used, we show that GBDTs can be used for the interpretable classi cation of high-dimensional and heterogeneous time series data.
