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Found 17 papers, 5 papers with code
Synthetic Dialogue Dataset Generation using LLM Agents
We conduct human and automatic evaluations, including an evaluation approach that uses GPT-4 to mimic the human evaluation metrics.
Ask One More Time: Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios
Although chain-of-thought (CoT) prompting combined with language models has achieved encouraging results on complex reasoning tasks, the naive greedy decoding used in CoT prompting usually causes the repetitiveness and local optimality.
Comparative study of microgrid optimal scheduling under multi-optimization algorithm fusion
As global attention on renewable and clean energy grows, the research and implementation of microgrids become paramount.
Improvement and Enhancement of YOLOv5 Small Target Recognition Based on Multi-module Optimization
In this paper, the limitations of YOLOv5s model on small target detection task are deeply studied and improved.
Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systems
However, building a user simulator with no reality-gap, i. e., can predict user's feedback exactly, is unrealistic because the users' reaction patterns are complex and historical logs for each user are limited, which might mislead the simulator-based recommendation policy.
Fewer is More: Efficient Object Detection in Large Aerial Images
Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not.
Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning
Motivated by this observation, we make an attempt to optimize the distribution of demand to handle this problem by learning the long-term spatio-temporal values as a guideline for pricing strategy.
Offline Model-based Adaptable Policy Learning
Current offline reinforcement learning methods commonly learn in the policy space constrained to in-support regions by the offline dataset, in order to ensure the robustness of the outcome policies.
Offline Adaptive Policy Leaning in Real-World Sequential Recommendation Systems
Instead of increasing the fidelity of models for policy learning, we handle the distortion issue via learning to adapt to diverse simulators generated by the offline dataset.
Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation
In this paper, we propose a hierarchical adaptive contextual bandit method (HATCH) to conduct the policy learning of contextual bandits with a budget constraint.
Weak Edge Identification Nets for Ocean Front Detection
The method uses the stochastic gradient descent and the correlation loss function to obtain a good ocean front image output.
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation
DEMER also derives a recommendation policy with a significantly improved performance in the test phase of the real application.
Long Short-Term Attention
Attention is an important cognition process of humans, which helps humans concentrate on critical information during their perception and learning.
Multi-task Dictionary Learning based Convolutional Neural Network for Computer aided Diagnosis with Longitudinal Images
Firstly, we pre-train CNN on the ImageNet dataset and transfer the knowledge from the pre-trained model to the medical imaging progression representation, generating the features for different tasks.
Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's Disease via Distributed Group Lasso Regression
Then we select the relevant group features by performing the group Lasso feature selection process in a sequence of parameters.
Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions
To the best of our knowledge, this is the first successful run of the computationally intensive model selection procedure to learn a consistent model across different institutions without compromising their privacy while ranking the SNPs that may collectively affect AD.
Stochastic Coordinate Coding and Its Application for Drosophila Gene Expression Pattern Annotation
The effectiveness of gene expression pattern annotation relies on the quality of feature representation.
