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Found 12 papers, 5 papers with code
Pathological Semantics-Preserving Learning for H&E-to-IHC Virtual Staining
To address these issues, we propose the Pathological Semantics-Preserving Learning method for Virtual Staining (PSPStain), which directly incorporates the molecular-level semantic information and enhances semantics interaction despite any spatial inconsistency.
Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning
This enables agents to effectively balance their individual interests with the collective benefit.
OpenTensor: Reproducing Faster Matrix Multiplication Discovering Algorithms
OpenTensor is a reproduction of AlphaTensor, which discovered a new algorithm that outperforms the state-of-the-art methods for matrix multiplication by Deep Reinforcement Learning (DRL).
AutoSAT: Automatically Optimize SAT Solvers via Large Language Models
Conflict-Driven Clause Learning (CDCL) is the mainstream framework for solving the Satisfiability problem (SAT), and CDCL solvers typically rely on various heuristics, which have a significant impact on their performance.
OpenRL: A Unified Reinforcement Learning Framework
We present OpenRL, an advanced reinforcement learning (RL) framework designed to accommodate a diverse array of tasks, from single-agent challenges to complex multi-agent systems.
Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation
Goal-conditioned hierarchical reinforcement learning (HRL) provides a promising direction to tackle this challenge by introducing a hierarchical structure to decompose the search space, where the low-level policy predicts primitive actions in the guidance of the goals derived from the high-level policy.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
+2
A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility
We present a brand-new and general machine learning method for material property prediction.
Road Network Metric Learning for Estimated Time of Arrival
To address the data sparsity problem, we propose the Road Network Metric Learning framework for ETA (RNML-ETA).
FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attention
Estimated time of arrival (ETA) is one of the most important services in intelligent transportation systems and becomes a challenging spatial-temporal (ST) data mining task in recent years.
Fusion Recurrent Neural Network
Furthermore, in order to evaluate Fusion RNN's sequence feature extraction capability, we choose a representative data mining task for sequence data, estimated time of arrival (ETA) and present a novel model based on Fusion RNN.
Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting
Recently, deep learning based methods have achieved promising results by adopting graph convolutional network (GCN) to extract the spatial correlations and recurrent neural network (RNN) to capture the temporal dependencies.
Dynamic Hierarchical Empirical Bayes: A Predictive Model Applied to Online Advertising
Predicting keywords performance, such as number of impressions, click-through rate (CTR), conversion rate (CVR), revenue per click (RPC), and cost per click (CPC), is critical for sponsored search in the online advertising industry.
