Search Results for author:
Found 26 papers, 9 papers with code
Strength in Numbers: Averaging and Clustering Effects in Mixture of Experts for Graph-Based Dependency Parsing
We review two features of mixture of experts (MoE) models which we call averaging and clustering effects in the context of graph-based dependency parsers learned in a supervised probabilistic framework.
Robust Communicative Multi-Agent Reinforcement Learning with Active Defense
We propose an active defense strategy, where agents automatically reduce the impact of potentially harmful messages on the final decision.
Data Augmentation of Bridging the Delay Gap for DL-based Massive MIMO CSI Feedback
In massive multiple-input multiple-output (MIMO) systems under the frequency division duplexing (FDD) mode, the user equipment (UE) needs to feed channel state information (CSI) back to the base station (BS).
SGL: Structure Guidance Learning for Camera Localization
Camera localization is a classical computer vision task that serves various Artificial Intelligence and Robotics applications.
Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help
Multi-agent reinforcement learning (MARL) has achieved great progress in cooperative tasks in recent years.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Deep Learning for Hybrid Beamforming with Finite Feedback in GSM Aided mmWave MIMO Systems
Hybrid beamforming is widely recognized as an important technique for millimeter wave (mmWave) multiple input multiple output (MIMO) systems.
Low Entropy Communication in Multi-Agent Reinforcement Learning
Communication in multi-agent reinforcement learning has been drawing attention recently for its significant role in cooperation.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
SimCGNN: Simple Contrastive Graph Neural Network for Session-based Recommendation
Session-based recommendation (SBR) problem, which focuses on next-item prediction for anonymous users, has received increasingly more attention from researchers.
Towards Efficient Subarray Hybrid Beamforming: Attention Network-based Practical Feedback in FDD Massive MU-MIMO Systems
Channel state information (CSI) feedback is necessary for the frequency division duplexing (FDD) multiple input multiple output (MIMO) systems due to the channel non-reciprocity.
Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction
The area of transfer learning comprises supervised machine learning methods that cope with the issue when the training and testing data have different input feature spaces or distributions.
Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback
In this paper, we propose an adaptor-assisted quantization strategy for bit-level DL-based CSI feedback.
Better Lightweight Network for Free: Codeword Mimic Learning for Massive MIMO CSI feedback
In this paper, a cost free distillation technique named codeword mimic (CM) is proposed to train better feedback networks with the practical lightweight encoder.
MMFL-Net: Multi-scale and Multi-granularity Feature Learning for Cross-domain Fashion Retrieval
To this end, we propose a novel multi-scale and multi-granularity feature learning network (MMFL-Net), which can jointly learn global-local aggregation feature representations of clothing images in a unified framework, aiming to train a cross-domain model for C2S fashion visual similarity.
A Real-Time Fusion Framework for Long-term Visual Localization
In this paper, we present an efficient client-server visual localization architecture that fuses global and local pose estimations to realize promising precision and efficiency.
Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control
Flocking control is a challenging problem, where multiple agents, such as drones or vehicles, need to reach a target position while maintaining the flock and avoiding collisions with obstacles and collisions among agents in the environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control
By pretraining with non-expert demonstrations, PwD-MARL improves sample efficiency in the process of online MARL with a warm start.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning to Advise and Learning from Advice in Cooperative Multi-Agent Reinforcement Learning
In this paper, we explore the spatiotemporal structure of agents' decisions and consider the hierarchy of coordination from the perspective of multilevel emergence dynamics, based on which a novel approach, Learning to Advise and Learning from Advice (LALA), is proposed to improve MARL.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Targeted Data Poisoning Attack on News Recommendation System by Content Perturbation
To address this, we introduce a notion of the exposure risk and propose a novel problem of attacking a history news dataset by means of perturbations where the goal is to maximize the manipulation of the target news rank while keeping the risk of exposure under a given budget.
Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessor
Segmentation and labeling of liver tumors and blood vessels in CT images can provide convenience for doctors in liver tumor diagnosis and surgical intervention.
Pose Refinement with Joint Optimization of Visual Points and Lines
High-precision camera re-localization technology in a pre-established 3D environment map is the basis for many tasks, such as Augmented Reality, Robotics and Autonomous Driving.
Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning
In multi-agent deep reinforcement learning, extracting sufficient and compact information of other agents is critical to attain efficient convergence and scalability of an algorithm.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Retrieval and Localization with Observation Constraints
The localization pipeline is designed as a coarse-to-fine paradigm.
SR-HetGNN:Session-based Recommendation with Heterogeneous Graph Neural Network
The current studies generally learn user preferences according to the transitions of items in the user's session sequence.
Supervised Off-Policy Ranking
Inspired by the two observations, in this work, we study a new problem, supervised off-policy ranking (SOPR), which aims to rank a set of target policies based on supervised learning by leveraging off-policy data and policies with known performance.
Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System
Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system.
Robust Reinforcement Learning under model misspecification
Reinforcement learning has achieved remarkable performance in a wide range of tasks these days.
