Search Results for author:
Found 16 papers, 1 papers with code
IRef-VLA: A Benchmark for Interactive Referential Grounding with Imperfect Language in 3D Scenes
With this benchmark, we aim to provide a resource for 3D scene understanding that aids the development of robust, interactive navigation systems.
Gap-Dependent Bounds for Federated $Q$-learning
We present the first gap-dependent analysis of regret and communication cost for on-policy federated $Q$-Learning in tabular episodic finite-horizon Markov decision processes (MDPs).
Topology-Preserving Image Segmentation with Spatial-Aware Persistent Feature Matching
Topological correctness is critical for segmentation of tubular structures.
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayesian Theory
Lifelong reinforcement learning (RL) has been developed as a paradigm for extending single-task RL to more realistic, dynamic settings.
Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition
To our knowledge, this paper presents the first gap-dependent regret analysis for Q-learning using variance estimators and reference-advantage decomposition and also provides the first gap-dependent analysis on policy switching cost for Q-learning.
Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost
In this paper, we consider model-free federated reinforcement learning for tabular episodic Markov decision processes.
Jellyfish: A Large Language Model for Data Preprocessing
We select a collection of datasets across four representative DP tasks and construct instruction tuning data using data configuration, knowledge injection, and reasoning data distillation techniques tailored to DP.
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, 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.
