Similar: Submodular information measures based active learning in realistic scenarios
S Kothawade, N Beck… - Advances in Neural …, 2021 - proceedings.neurips.cc
Active learning has proven to be useful for minimizing labeling costs by selecting the most
informative samples. However, existing active learning methods do not work well in realistic …
informative samples. However, existing active learning methods do not work well in realistic …
Conditional gradient methods
G Braun, A Carderera, CW Combettes… - arXiv preprint arXiv …, 2022 - arxiv.org
The purpose of this survey is to serve both as a gentle introduction and a coherent overview
of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithms, for …
of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithms, for …
Talisman: targeted active learning for object detection with rare classes and slices using submodular mutual information
Deep neural networks based object detectors have shown great success in a variety of
domains like autonomous vehicles, biomedical imaging, etc., however their success …
domains like autonomous vehicles, biomedical imaging, etc., however their success …
Submodular reinforcement learning
In reinforcement learning (RL), rewards of states are typically considered additive, and
following the Markov assumption, they are $\textit {independent} $ of states visited …
following the Markov assumption, they are $\textit {independent} $ of states visited …
An efficient particle swarm optimization with evolutionary multitasking for stochastic area coverage of heterogeneous sensors
This paper investigates the stochastic area coverage problem of sensors with uncertain
detection probability. The risk associated with uncertain parameters is managed using the …
detection probability. The risk associated with uncertain parameters is managed using the …
Reconfigurable intelligent surface assisted massive MIMO with antenna selection
Antenna selection is capable of reducing the hardware complexity of massive multiple-input
multiple-output (MIMO) networks at the cost of certain performance degradation …
multiple-output (MIMO) networks at the cost of certain performance degradation …
Dynamic constrained submodular optimization with polylogarithmic update time
Maximizing a monotone submodular function under cardinality constraint $ k $ is a core
problem in machine learning and database with many basic applications, including video …
problem in machine learning and database with many basic applications, including video …
Near-optimal los and orientation aware intelligent reflecting surface placement
Due to their passive nature and thus low energy consumption, intelligent reflecting surfaces
(IRSs) have shown promise as means of extending coverage as a proxy for connection …
(IRSs) have shown promise as means of extending coverage as a proxy for connection …
Deep learning algorithms for the work function fluctuation of random nanosized metal grains on gate-all-around silicon nanowire MOSFETs
Device simulation has been explored and industrialized for over 40 years; however, it still
requires huge computational cost. Therefore, it can be further advanced using deep learning …
requires huge computational cost. Therefore, it can be further advanced using deep learning …
Diverse approximations for monotone submodular maximization problems with a matroid constraint
Finding diverse solutions to optimization problems has been of practical interest for several
decades, and recently enjoyed increasing attention in research. While submodular …
decades, and recently enjoyed increasing attention in research. While submodular …