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Found 19 papers, 7 papers with code
The Case for Controls: Identifying outbreak risk factors through case-control comparisons
Investigations of infectious disease outbreaks often focus on identifying place- and context-dependent factors responsible for emergence and spread, resulting in phenomenological narratives ill-suited to developing generalizable predictive and preventive measures.
Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising
Note that we use LDCT images based on the noisy-as-clean strategy for corruption instead of NDCT images.
Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning
In ERC, we propose a regularizer that guides the approximation error tending towards the 1-eigensubspace, resulting in a more efficient and stable path of value approximation.
PGPointNovo: an efficient neural network-based tool for parallel de novo peptide sequencing
De novo peptide sequencing for tandem mass spectrometry data is not only a key technology for novel peptide identification, but also a precedent task for many downstream tasks, such as vaccine and antibody studies.
Quadratic Graph Attention Network (Q-GAT) for Robust Construction of Gene Regulatory Networks
The former informs that nonlinear aggregation of quadratic neurons can amplify useful signals and suppress unwanted noise, thereby facilitating robustness, while the latter reveals that Q-GAT can leverage more features in prediction thanks to the dual attention mechanism, which endows Q-GAT with the ability to confront adversarial perturbation.
Centralized Cooperative Exploration Policy for Continuous Control Tasks
To explicitly encourage exploration in continuous control tasks, we propose CCEP (Centralized Cooperative Exploration Policy), which utilizes underestimation and overestimation of value functions to maintain the capacity of exploration.
Adaptive Weighted Nonnegative Matrix Factorization for Robust Feature Representation
Nonnegative matrix factorization (NMF) has been widely used to dimensionality reduction in machine learning.
Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning
In this work, we demonstrate that the learned representation of the $Q$-network and its target $Q$-network should, in theory, satisfy a favorable distinguishable representation property.
Improving the Level of Autism Discrimination through GraphRNN Link Prediction
This paper is based on the latter technique, which learns the edge distribution of real brain network through GraphRNN, and generates the synthetic data which has incentive effect on the discriminant model.
An Entropy Weighted Nonnegative Matrix Factorization Algorithm for Feature Representation
Nonnegative matrix factorization (NMF) has been widely used to learn low-dimensional representations of data.
The $f$-Divergence Reinforcement Learning Framework
In FRL, the policy evaluation and policy improvement phases are simultaneously performed by minimizing the $f$-divergence between the learning policy and sampling policy, which is distinct from conventional DRL algorithms that aim to maximize the expected cumulative rewards.
LDC-VAE: A Latent Distribution Consistency Approach to Variational AutoEncoders
Variational autoencoders (VAEs), as an important aspect of generative models, have received a lot of research interests and reached many successful applications.
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning
During the training of a reinforcement learning (RL) agent, the distribution of training data is non-stationary as the agent's behavior changes over time.
Hypernetwork Dismantling via Deep Reinforcement Learning
Network dismantling aims to degrade the connectivity of a network by removing an optimal set of nodes.
Session-based Social and Dependency-aware Software Recommendation
With the increase of complexity of modern software, social collaborative coding and reuse of open source software packages become more and more popular, which thus greatly enhances the development efficiency and software quality.
POPO: Pessimistic Offline Policy Optimization
Offline reinforcement learning (RL), also known as batch RL, aims to optimize policy from a large pre-recorded dataset without interaction with the environment.
WD3: Taming the Estimation Bias in Deep Reinforcement Learning
To obtain a more precise estimation for value function, we unify these two opposites and propose a novel algorithm \underline{W}eighted \underline{D}elayed \underline{D}eep \underline{D}eterministic Policy Gradient (WD3), which can eliminate the estimation bias and further improve the performance by weighting a pair of critics.
PA-Cache: Evolving Learning-Based Popularity-Aware Content Caching in Edge Networks
Compared to the conventional content delivery networks, caches in edge networks with smaller sizes usually have to accommodate more bursty requests.
Cutting the Unnecessary Long Tail: Cost-Effective Big Data Clustering in the Cloud
A novel approach is proposed to achieve cost-effective big data clustering in the cloud.
