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Found 9 papers, 1 papers with code
Deep Reinforcement Learning with Smooth Policy
In contrast to policy parameterized by linear/reproducing kernel functions, where simple regularization techniques suffice to control smoothness, for neural network based reinforcement learning algorithms, there is no readily available solution to learn a smooth policy.
State-space models are accurate and efficient neural operators for dynamical systems
We demonstrate Mamba's superior performance in both interpolation and challenging extrapolation tasks.
Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization
Bi-level optimization (BO) has become a fundamental mathematical framework for addressing hierarchical machine learning problems.
FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models
Overparametrized transformer networks are the state-of-the-art architecture for Large Language Models (LLMs).
The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright Breaches Without Adjusting Finetuning Pipeline
Our method strategically embeds connections between pieces of copyrighted information and text references in poisoning data while carefully dispersing that information, making the poisoning data inconspicuous when integrated into a clean dataset.
VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Models
The booming use of text-to-image generative models has raised concerns about their high risk of producing copyright-infringing content.
State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning
The key idea of SA-PP is leveraging discounted stationary state distribution ratios between the learning policy and the offline dataset to modulate the degree of behavior regularization in a state-wise manner, so that pessimism can be implemented in a more appropriate way.
GFlowOut: Dropout with Generative Flow Networks
These methods face two important challenges: (a) the posterior distribution over masks can be highly multi-modal which can be difficult to approximate with standard variational inference and (b) it is not trivial to fully utilize sample-dependent information and correlation among dropout masks to improve posterior estimation.
Deep Reinforcement Learning with Robust and Smooth Policy
Deep reinforcement learning (RL) has achieved great empirical successes in various domains.
