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Found 10 papers, 1 papers with code
Jellyfish: A Large Language Model for Data Preprocessing
This paper explores the utilization of LLMs for data preprocessing (DP), a crucial step in the data mining pipeline that transforms raw data into a clean format conducive to easy processing.
Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning
The DRL policy aims to optimize trading fees earned by LPs against associated costs, such as gas fees and hedging expenses, which is referred to as loss-versus-rebalancing (LVR).
Large Language Models as Data Preprocessors
Large Language Models (LLMs), typified by OpenAI's GPT series and Meta's LLaMA variants, have marked a significant advancement in artificial intelligence.
Computational Pathology: A Survey Review and The Way Forward
Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images.
Resilient Distribution System Restoration with Communication Recovery by Drone Small Cells
Distribution system (DS) restoration after natural disasters often faces the challenge of communication failures to feeder automation (FA) facilities, resulting in prolonged load pick-up process.
Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective
A popular attempts towards the challenge is unpaired generative adversarial networks, which generate "real" LR counterparts from real HR images using image-to-image translation and then perform super-resolution from "real" LR->SR.
Learning by Passing Tests, with Application to Neural Architecture Search
We propose a multi-level optimization framework to formulate LPT, where the tester learns to create difficult and meaningful tests and the learner learns to pass these tests.
Is There Tradeoff between Spatial and Temporal in Video Super-Resolution?
Recent advances of deep learning lead to great success of image and video super-resolution (SR) methods that are based on convolutional neural networks (CNN).
On The Classification-Distortion-Perception Tradeoff
In this paper, we extend the previous perception-distortion tradeoff to the case of classification-distortion-perception (CDP) tradeoff, where we introduced the classification error rate of the restored signal in addition to distortion and perceptual difference.
Two-Stream Action Recognition-Oriented Video Super-Resolution
Tailored for two-stream action recognition networks, we propose two video SR methods for the spatial and temporal streams respectively.
