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Found 16 papers, 6 papers with code
Regulating Intermediate 3D Features for Vision-Centric Autonomous Driving
To resolve this problem, we propose to regulate intermediate dense 3D features with the help of volume rendering.
Deep Transfer Convolutional Neural Network and Extreme Learning Machine for Lung Nodule Diagnosis on CT images
Some content of the article needs to be kept secret
Generalization in Visual Reinforcement Learning with the Reward Sequence Distribution
The appealing features of RSD-OA include that: (1) RSD-OA is invariant to visual distractions, as it is conditioned on the predefined subsequent action sequence without task-irrelevant information from transition dynamics, and (2) the reward sequence captures long-term task-relevant information in both rewards and transition dynamics.
Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions
To tackle this problem, we propose a novel clustering-based approach, namely Clustering with Bisimulation Metrics (CBM), which learns robust representations by grouping visual observations in the latent space.
Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization
In this paper, We propose a Policy Optimization method with Model-Based Uncertainty (POMBU)---a novel model-based approach---that can effectively improve the asymptotic performance using the uncertainty in Q-values.
Model-based Reinforcement Learning
reinforcement-learning
+1
EvaLDA: Efficient Evasion Attacks Towards Latent Dirichlet Allocation
As one of the most powerful topic models, Latent Dirichlet Allocation (LDA) has been used in a vast range of tasks, including document understanding, information retrieval and peer-reviewer assignment.
Facial Aging and Rejuvenation by Conditional Multi-Adversarial Autoencoder with Ordinal Regression
The latent vector preserves personalized face features and the age controls facial aging and rejuvenation.
Ranked #1 on
Age Estimation
on MORPH
Intelligent Bandwidth Allocation for Latency Management in NG-EPON using Reinforcement Learning Methods
A novel intelligent bandwidth allocation scheme in NG-EPON using reinforcement learning is proposed and demonstrated for latency management.
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method
However, in continuous action spaces, integrating entropy regularization with expressive policies is challenging and usually requires complex inference procedures.
Evidence for Bosonization in a three-dimensional gas of SU($N$) fermions
Blurring the boundary between bosons and fermions lies at the heart of a wide range of intriguing quantum phenomena in multiple disciplines, ranging from condensed matter physics and atomic, molecular and optical physics to high energy physics.
Quantum Gases Other Condensed Matter Quantum Physics
Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization
Many existing algorithms learn robust policies by modeling the disturbance and applying it to source environments during training, which usually requires prior knowledge about the disturbance and control of simulators.
Efficient Exploration in Resource-Restricted Reinforcement Learning
In many real-world applications of reinforcement learning (RL), performing actions requires consuming certain types of resources that are non-replenishable in each episode.
Harnessing Transparent Learning Analytics for Individualized Support through Auto-detection of Engagement in Face-to-Face Collaborative Learning
Using learning analytics to investigate and support collaborative learning has been explored for many years.
FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution
To expose this potential vulnerability, we aim to build an adversarial attack forcing SDEdit to generate a specific data distribution aligned with a specified attribute (e. g., female), without changing the input's attribute characteristics.
MPIPN: A Multi Physics-Informed PointNet for solving parametric acoustic-structure systems
The framework is generalized to deal with new parametric conditions of systems.
Dynamic Gaussian Graph Operator: Learning parametric partial differential equations in arbitrary discrete mechanics problems
Metric vectors are regarded as located on latent uniform domain, wherein spatial and spectral transformation offer highly regular constraints on solution space.
