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Found 8 papers, 4 papers with code
Deep Optimal Timing Strategies for Time Series
Deciding the best future execution time is a critical task in many business activities while evolving time series forecasting, and optimal timing strategy provides such a solution, which is driven by observed data.
EasyTPP: Towards Open Benchmarking Temporal Point Processes
In this paper, we present EasyTPP, the first central repository of research assets (e. g., data, models, evaluation programs, documentations) in the area of event sequence modeling.
Automatic Deduction Path Learning via Reinforcement Learning with Environmental Correction
To this end, we formulate the problem as a partially observable Markov decision problem (POMDP) and employ an environment correction algorithm based on the characteristics of the business.
A positive feedback method based on F-measure value for Salient Object Detection
The majority of current salient object detection (SOD) models are focused on designing a series of decoders based on fully convolutional networks (FCNs) or Transformer architectures and integrating them in a skillful manner.
Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement
To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model.
Ranked #3 on
Low-Light Image Enhancement
on LOLv2
SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies
Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only, we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e. g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks.
Joint-optimization of Node placement and UAV's Trajectory for Self-sustaining Air-Ground IoT system
Due to the sustainable power supply and environment-friendly features, self-powered IoT devices have been increasingly employed in various fields such as providing observation data in remote areas, especially in rural areas or post-disaster scenarios.
Enabling Fast Deep Learning on Tiny Energy-Harvesting IoT Devices
Energy harvesting (EH) IoT devices that operate intermittently without batteries, coupled with advances in deep neural networks (DNNs), have opened up new opportunities for enabling sustainable smart applications.
