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Found 8 papers, 0 papers with code
Progressive Open-Domain Response Generation with Multiple Controllable Attributes
More specifically, PHED deploys Conditional Variational AutoEncoder (CVAE) on Transformer to include one aspect of attributes at one stage.
A Stochastic Time Series Model for Predicting Financial Trends using NLP
We propose a novel deep learning model called ST-GAN, or Stochastic Time-series Generative Adversarial Network, that analyzes both financial news texts and financial numerical data to predict stock trends.
Distributed Pinning Control Design for Probabilistic Boolean Networks
Owing to the stochasticity, the uniform state feedback controllers, which is independent of switching signal, might be out of work.
Sensors Design for Large-Scale Boolean Networks via Pinning Observability
In this paper, a set of sensors is constructed via the pinning observability approach with the help of observability criteria given in [1] and [2], in order to make the given Boolean network (BN) be observable.
Zero-Shot Imitating Collaborative Manipulation Plans from YouTube Cooking Videos
Building upon this theory of language for action, we propose a system for understanding and executing demonstrated action sequences from full-length, real-world cooking videos on the web.
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI
In this paper, we focus on the AutoML problem from the \emph{service provider's perspective}, motivated by the following practical consideration: When an AutoML service needs to serve {\em multiple users} with {\em multiple devices} at the same time, how can we allocate these devices to users in an efficient way?
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads
We ask, as a "service provider" that manages a shared cluster of machines among all our users running machine learning workloads, what is the resource allocation strategy that maximizes the global satisfaction of all our users?
Asynchronous Parallel Empirical Variance Guided Algorithms for the Thresholding Bandit Problem
This paper proposes an asynchronous parallel thresholding algorithm and its parameter-free version to improve the efficiency and the applicability.
