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Found 9 papers, 3 papers with code
Dataless Quadratic Neural Networks for the Maximum Independent Set Problem
More specifically, the graph structure and constraints of the MIS instance are used to define the structure and parameters of the neural network such that training it on a fixed input provides a solution to the problem, thereby setting it apart from traditional supervised or reinforcement learning approaches.
Improving Training Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architecture
This is due to the necessity of tracking extensive forward and reverse diffusion trajectories and employing a large model with numerous parameters across multiple timesteps (i. e. noise levels).
Improving Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architectures
This is due to the necessity of tracking extensive forward and reverse diffusion trajectories, and employing a large model with numerous parameters across multiple timesteps (i. e., noise levels).
Robust MRI Reconstruction by Smoothed Unrolling (SMUG)
To address this problem, we propose a novel image reconstruction framework, termed Smoothed Unrolling (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning approach.
Robust Physics-based Deep MRI Reconstruction Via Diffusion Purification
In particular, we present a robustification strategy that improves the resilience of DL-based MRI reconstruction methods by utilizing pretrained diffusion models as noise purifiers.
On the Robustness of AlphaFold: A COVID-19 Case Study
To measure the robustness of the predicted structures, we utilize (i) the root-mean-square deviation (RMSD) and (ii) the Global Distance Test (GDT) similarity measure between the predicted structure of the original sequence and the structure of its adversarially perturbed version.
Controller Synthesis for Omega-Regular and Steady-State Specifications
Given a Markov decision process (MDP) and a linear-time ($\omega$-regular or LTL) specification, the controller synthesis problem aims to compute the optimal policy that satisfies the specification.
Steady-State Planning in Expected Reward Multichain MDPs
In this paper, we explore this steady-state planning problem that consists of deriving a decision-making policy for an agent such that constraints on its steady-state behavior are satisfied.
Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks
A new paradigm for large-scale spectrum occupancy learning based on long short-term memory (LSTM) recurrent neural networks is proposed.
