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Found 45 papers, 17 papers with code
Heterogeneous Federated Learning with Splited Language Model
Federated Split Learning (FSL) is a promising distributed learning paradigm in practice, which gathers the strengths of both Federated Learning (FL) and Split Learning (SL) paradigms, to ensure model privacy while diminishing the resource overhead of each client, especially on large transformer models in a resource-constrained environment, e. g., Internet of Things (IoT).
Global-guided Focal Neural Radiance Field for Large-scale Scene Rendering
The global stage obtains a continuous representation of the entire scene while the focal stage decomposes the scene into multiple blocks and further processes them with distinct sub-encoders.
GAgent: An Adaptive Rigid-Soft Gripping Agent with Vision Language Models for Complex Lighting Environments
This paper introduces GAgent: an Gripping Agent designed for open-world environments that provides advanced cognitive abilities via VLM agents and flexible grasping abilities with variable stiffness soft grippers.
PaddingFlow: Improving Normalizing Flows with Padding-Dimensional Noise
To implement PaddingFlow, only the dimension of normalizing flows needs to be modified.
Ranked #1 on
Density Estimation
on MNIST
(MMD-L2 metric)
Offline Goal-Conditioned Reinforcement Learning for Safety-Critical Tasks with Recovery Policy
Offline goal-conditioned reinforcement learning (GCRL) aims at solving goal-reaching tasks with sparse rewards from an offline dataset.
Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping
To further exploit the capabilities of bootstrapping, we investigate and adjust the training order of data, which yields improved performance of the model.
PPNet: A Two-Stage Neural Network for End-to-end Path Planning
The results show that PPNet can find a near-optimal solution in 15. 3ms, which is much shorter than the state-of-the-art path planners.
Replay-enhanced Continual Reinforcement Learning
On the other hand, offline learning on replayed tasks while learning a new task may induce a distributional shift between the dataset and the learned policy on old tasks, resulting in forgetting.
The Road to On-board Change Detection: A Lightweight Patch-Level Change Detection Network via Exploring the Potential of Pruning and Pooling
In addition, our method reduces more than 60% memory costs of the subsequent pixel-level CD processing stage.
Learning visual-based deformable object rearrangement with local graph neural networks
Our method reaches much higher success rates on a variety of deformable rearrangement tasks (96. 3% on average) than state-of-the-art method in simulation experiments.
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages
Plasticity, the ability of a neural network to evolve with new data, is crucial for high-performance and sample-efficient visual reinforcement learning (VRL).
DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning
Constrained policy search (CPS) is a fundamental problem in offline reinforcement learning, which is generally solved by advantage weighted regression (AWR).
Efficient Federated Prompt Tuning for Black-box Large Pre-trained Models
With the blowout development of pre-trained models (PTMs), the efficient tuning of these models for diverse downstream applications has emerged as a pivotal research concern.
Are Large Language Models Really Robust to Word-Level Perturbations?
Notably, we are surprised to discover that robustness tends to decrease as fine-tuning (SFT and RLHF) is conducted.
DFWLayer: Differentiable Frank-Wolfe Optimization Layer
Differentiable optimization has received a significant amount of attention due to its foundational role in the domain of machine learning based on neural networks.
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training
Personalized federated learning (PFL) aims to produce the greatest personalized model for each client to face an insurmountable problem--data heterogeneity in real FL systems.
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing
However, it suffers from issues in terms of communication, resource of MTs, and privacy.
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy
To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy protection by clipping local updates and adding random noise.
COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial Networks
For this problem, an unsupervised change detection method has been proposed recently based on the image translation technique of Cycle-Consistent Adversarial Networks (CycleGANs), where one image is translated from its original modality to the modality of the other image so that the difference map can be obtained by performing arithmetical subtraction.
NN-Copula-CD: A Copula-Guided Interpretable Neural Network for Change Detection in Heterogeneous Remote Sensing Images
Change detection (CD) in heterogeneous remote sensing images is a practical and challenging issue for real-life emergencies.
Make Landscape Flatter in Differentially Private Federated Learning
Specifically, DP-FedSAM integrates Sharpness Aware Minimization (SAM) optimizer to generate local flatness models with better stability and weight perturbation robustness, which results in the small norm of local updates and robustness to DP noise, thereby improving the performance.
Graph-Transporter: A Graph-based Learning Method for Goal-Conditioned Deformable Object Rearranging Task
Rearranging deformable objects is a long-standing challenge in robotic manipulation for the high dimensionality of configuration space and the complex dynamics of deformable objects.
Deep Reinforcement Learning Based on Local GNN for Goal-conditioned Deformable Object Rearranging
Some research has been attempting to design a general framework to obtain more advanced manipulation capabilities for deformable rearranging tasks, with lots of progress achieved in simulation.
Improving the Model Consistency of Decentralized Federated Learning
To mitigate the privacy leakages and communication burdens of Federated Learning (FL), decentralized FL (DFL) discards the central server and each client only communicates with its neighbors in a decentralized communication network.
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning
Offline safe RL is of great practical relevance for deploying agents in real-world applications.
Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention
Foldformer can complete multi-step cloth manipulation tasks even when configurations of the cloth (e. g., size and pose) vary from configurations in the general demonstrations.
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
Hence, we propose Pre-trained Image Encoder for Generalizable visual reinforcement learning (PIE-G), a simple yet effective framework that can generalize to the unseen visual scenarios in a zero-shot manner.
Safety Correction from Baseline: Towards the Risk-aware Policy in Robotics via Dual-agent Reinforcement Learning
On the other hand, the safe agent mimics the baseline agent for policy improvement and learns to fulfill safety constraints via off-policy RL tuning.
Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks
Despite a large number of reinforcement learning (RL) methods focusing on safety-critical tasks, there is still a lack of high-quality evaluation of those algorithms that adheres to safety constraints at each decision step under complex and unknown dynamics.
Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds
Here, a visual-tactile fusion framework for transparent object grasping under complex backgrounds and variant light conditions is proposed, including the grasping position detection, tactile calibration, and visual-tactile fusion based classification.
MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images
For the task of change detection (CD) in remote sensing images, deep convolution neural networks (CNNs)-based methods have recently aggregated transformer modules to improve the capability of global feature extraction.
A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning
Visual reinforcement learning (RL), which makes decisions directly from high-dimensional visual inputs, has demonstrated significant potential in various domains.
USEEK: Unsupervised SE(3)-Equivariant 3D Keypoints for Generalizable Manipulation
Can a robot manipulate intra-category unseen objects in arbitrary poses with the help of a mere demonstration of grasping pose on a single object instance?
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization
A key challenge of continual reinforcement learning (CRL) in dynamic environments is to promptly adapt the RL agent's behavior as the environment changes over its lifetime, while minimizing the catastrophic forgetting of the learned information.
Polarimetric Inverse Rendering for Transparent Shapes Reconstruction
We build a polarization dataset for multi-view transparent shapes reconstruction to verify our method.
SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving
Safe reinforcement learning (RL) has achieved significant success on risk-sensitive tasks and shown promise in autonomous driving (AD) as well.
Penalized Proximal Policy Optimization for Safe Reinforcement Learning
Safe reinforcement learning aims to learn the optimal policy while satisfying safety constraints, which is essential in real-world applications.
Transparent Shape from a Single View Polarization Image
To train and test our method, we construct a dataset for transparent shape from polarization with paired polarization images and ground-truth normal maps.
Data-Driven Robust Control for Discrete Linear Time-Invariant Systems: A Descriptor System Approach
Given the recent surge of interest in data-driven control, this paper proposes a two-step method to study robust data-driven control for a parameter-unknown linear time-invariant (LTI) system that is affected by energy-bounded noises.
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement Learning
One of the key challenges in visual Reinforcement Learning (RL) is to learn policies that can generalize to unseen environments.
A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning
The integration of Reinforcement Learning (RL) and Evolutionary Algorithms (EAs) aims at simultaneously exploiting the sample efficiency as well as the diversity and robustness of the two paradigms.
Probability Density Estimation Based Imitation Learning
In general, IL methods can be categorized into Behavioral Cloning (BC) and Inverse Reinforcement Learning (IRL).
Data-Driven Controllability Analysis and Stabilization for Linear Descriptor Systems
For a parameter-unknown linear descriptor system, this paper proposes data-driven methods to testify the system's type and controllability and then to stabilize it.
Value Penalized Q-Learning for Recommender Systems
To alleviate the action distribution shift problem in extracting RL policy from static trajectories, we propose Value Penalized Q-learning (VPQ), an uncertainty-based offline RL algorithm.
Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation
In this paper, we present IQ, i. e., interference-aware deep Q-learning, to mitigate catastrophic interference in single-task deep reinforcement learning.
