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Found 12 papers, 3 papers with code
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning
To address this limitation, we propose Imagine, Initialize, and Explore (IIE), a novel method that offers a promising solution for efficient multi-agent exploration in complex scenarios.
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids.
Spatiotemporally adaptive compression for scientific dataset with feature preservation -- a case study on simulation data with extreme climate events analysis
Additionally, we integrate spatiotemporal feature detection with data compression and demonstrate that performing adaptive error-bounded compression in higher dimensional space enables greater compression ratios, leveraging the error propagation theory of a transformation-based compressor.
High-performance Data Management for Whole Slide Image Analysis in Digital Pathology
The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario ("CPU scenario"), and (2) a GPU-based deep learning framework scenario ("GPU scenario").
An Accelerated Pipeline for Multi-label Renal Pathology Image Segmentation at the Whole Slide Image Level
Our proposed method's innovative contribution is two-fold: (1) a Docker is released for an end-to-end slide-wise multi-tissue segmentation for WSIs; and (2) the pipeline is deployed on a GPU to accelerate the prediction, achieving better segmentation quality in less time.
MMRDN: Consistent Representation for Multi-View Manipulation Relationship Detection in Object-Stacked Scenes
Manipulation relationship detection (MRD) aims to guide the robot to grasp objects in the right order, which is important to ensure the safety and reliability of grasping in object stacked scenes.
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning
To ensure optimal consistency, the optimal node is required to be the unique STN.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Greedy-based Value Representation for Efficient Coordination in Multi-agent Reinforcement Learning
Due to the representation limitation of the joint Q value function, multi-agent reinforcement learning (MARL) methods with linear or monotonic value decomposition can not ensure the optimal consistency (i. e. the correspondence between the individual greedy actions and the maximal true Q value), leading to instability and poor coordination.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation
General methods for policy based multi-agent reinforcement learning to solve the challenge introduce differentiate value functions or advantage functions for individual agents.
FTRANS: Energy-Efficient Acceleration of Transformers using FPGA
In natural language processing (NLP), the "Transformer" architecture was proposed as the first transduction model replying entirely on self-attention mechanisms without using sequence-aligned recurrent neural networks (RNNs) or convolution, and it achieved significant improvements for sequence to sequence tasks.
ADIOS 2: The Adaptable Input Output System. A framework for high-performance data management
We present ADIOS 2, the latest version of the Adaptable Input Output (I/O) System.
A Multi-task Convolutional Neural Network for Autonomous Robotic Grasping in Object Stacking Scenes
Autonomous robotic grasping plays an important role in intelligent robotics.
Robotics
