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Found 20 papers, 4 papers with code
Settling Constant Regrets in Linear Markov Decision Processes
To the best of our knowledge, Cert-LSVI-UCB is the first algorithm to achieve a constant, instance-dependent, high-probability regret bound in RL with linear function approximation for infinite runs without relying on prior distribution assumptions.
Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems
Real-world multi-agent systems are often dynamic and continuous, where the agents co-evolve and undergo changes in their trajectories and interactions over time.
Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance
The advancement of Large Vision-Language Models (LVLMs) has increasingly highlighted the critical issue of their tendency to hallucinate non-existing objects in the images.
Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
While it is widely acknowledged that the quality of a prompt, such as a question, significantly impacts the quality of the response provided by LLMs, a systematic method for crafting questions that LLMs can better comprehend is still underdeveloped.
LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation
Data augmentation has become a de facto component of deep learning-based medical image segmentation methods.
M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry Optimization
The proposed method, named Medical vision-language pre-training with Frozen language models and Latent spAce Geometry optimization (M-FLAG), leverages a frozen language model for training stability and efficiency and introduces a novel orthogonality loss to harmonize the latent space geometry.
DNAGPT: A Generalized Pre-trained Tool for Versatile DNA Sequence Analysis Tasks
Pre-trained large language models demonstrate potential in extracting information from DNA sequences, yet adapting to a variety of tasks and data modalities remains a challenge.
Pay Attention to the Atlas: Atlas-Guided Test-Time Adaptation Method for Robust 3D Medical Image Segmentation
In addition, different from most existing TTA methods which restrict the adaptation to batch normalization blocks in the segmentation network only, we further exploit the use of channel and spatial attention blocks for improved adaptability at test time.
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs
Recent studies have shown that episodic reinforcement learning (RL) is no harder than bandits when the total reward is bounded by $1$, and proved regret bounds that have a polylogarithmic dependence on the planning horizon $H$.
DynamicKD: An Effective Knowledge Distillation via Dynamic Entropy Correction-Based Distillation for Gap Optimizing
This paper proposes a novel knowledge distillation algorithm based on dynamic entropy correction to reduce the gap by adjusting the student instead of the teacher.
A Multi-objective Complex Network Pruning Framework Based on Divide-and-conquer and Global Performance Impairment Ranking
To this end, a multi-objective complex network pruning framework based on divide-and-conquer and global performance impairment ranking (EMO-DIR) is proposed in this paper.
Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs
The sample complexity of our algorithm only has a polylogarithmic dependence on the planning horizon and therefore is "horizon-free".
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits
We show that, when the misspecification level $\zeta$ is dominated by $\tilde O (\Delta / \sqrt{d})$ with $\Delta$ being the minimal sub-optimality gap and $d$ being the dimension of the contextual vectors, our algorithm enjoys the same gap-dependent regret bound $\tilde O (d^2/\Delta)$ as in the well-specified setting up to logarithmic factors.
Learning Neural Contextual Bandits Through Perturbed Rewards
Thanks to the power of representation learning, neural contextual bandit algorithms demonstrate remarkable performance improvement against their classical counterparts.
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation
By constructing a special class of linear Mixture MDPs, we also prove that for any reward-free algorithm, it needs to sample at least $\tilde \Omega(H^2d\epsilon^{-2})$ episodes to obtain an $\epsilon$-optimal policy.
Model-based Reinforcement Learning
reinforcement-learning
+1
Provably Efficient Representation Selection in Low-rank Markov Decision Processes: From Online to Offline RL
For the offline counterpart, ReLEX-LCB, we show that the algorithm can find the optimal policy if the representation class can cover the state-action space and achieves gap-dependent sample complexity.
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods
In this work, we provide a non-asymptotic analysis for two time-scale actor-critic methods under non-i. i. d.
Neural Thompson Sampling
Thompson Sampling (TS) is one of the most effective algorithms for solving contextual multi-armed bandit problems.
A Finite Time Analysis of Two Time-Scale Actor Critic Methods
In this work, we provide a non-asymptotic analysis for two time-scale actor-critic methods under non-i. i. d.
Characters Detection on Namecard with faster RCNN
We apply Faster R-CNN to the detection of characters in namecard, in order to solve the problem of a small amount of data and the inbalance between different class, we designed the data augmentation and the 'fake' data generalizer to generate more data for the training of network.
