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Found 12 papers, 3 papers with code
3rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Results
The 3rd Workshop on Maritime Computer Vision (MaCVi) 2025 addresses maritime computer vision for Unmanned Surface Vehicles (USV) and underwater.
Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach
By leveraging this feature, C4 can rapidly identify the faulty components, swiftly isolate the anomaly, and restart the task, thereby avoiding resource wastage caused by delays in anomaly detection.
Pelvic floor MRI segmentation based on semi-supervised deep learning
Recently, deep learning-enabled semantic segmentation has facilitated the three-dimensional geometric reconstruction of pelvic floor organs, providing clinicians with accurate and intuitive diagnostic results.
Neuro-Dynamic State Estimation for Networked Microgrids
We devise neuro-dynamic state estimation (Neuro-DSE), a learning-based dynamic state estimation (DSE) algorithm for networked microgrids (NMs) under unknown subsystems.
Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm
The algorithm consists of a Fourier accumulation SAFT (FA-SAFT) and a directional model-based (D-MB) deconvolution method.
Provably Correct Optimization and Exploration with Non-linear Policies
Policy optimization methods remain a powerful workhorse in empirical Reinforcement Learning (RL), with a focus on neural policies that can easily reason over complex and continuous state and/or action spaces.
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning
Motivated by the prevailing paradigm of using unsupervised learning for efficient exploration in reinforcement learning (RL) problems [tang2017exploration, bellemare2016unifying], we investigate when this paradigm is provably efficient.
Adaptive Distraction Context Aware Tracking Based on Correlation Filter
In the response map obtained for the previous frame by the CF algorithm, we adaptively find the image blocks that are similar to the target and use them as negative samples.
How Does an Approximate Model Help in Reinforcement Learning?
In particular, we provide an algorithm that uses $\widetilde{O}(N/(1-\gamma)^3/\varepsilon^2)$ samples in a generative model to learn an $\varepsilon$-optimal policy, where $\gamma$ is the discount factor and $N$ is the number of near-optimal actions in the approximate model.
CSSegNet: Fine-Grained Cardiac Structures Segmentation Using Dilated Pyramid Pooling in U-net
To address this difficult problem, we presented a novel network structure which embedded dilated pyramid pooling block in the skip connections between networks' encoding and decoding stage.
A2BCD: Asynchronous Acceleration with Optimal Complexity
In this paper, we propose the Asynchronous Accelerated Nonuniform Randomized Block Coordinate Descent algorithm (A2BCD).
AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity
In this paper, we propose AsyncQVI, an asynchronous-parallel Q-value iteration for discounted Markov decision processes whose transition and reward can only be sampled through a generative model.
