Search Results for author:
Found 17 papers, 4 papers with code
Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness
Instruction tuning fine-tunes pre-trained Multi-modal Large Language Models (MLLMs) to handle real-world tasks.
SleepNetZero: Zero-Burden Zero-Shot Reliable Sleep Staging With Neural Networks Based on Ballistocardiograms
However, reliable BCG-based sleep staging is challenging due to the limited sleep monitoring data available for BCG.
DPL: Cross-quality DeepFake Detection via Dual Progressive Learning
These varying qualities diversify the pattern of forgery traces, significantly increasing the difficulty of DeepFake detection.
Bridging Local Details and Global Context in Text-Attributed Graphs
Representation learning on text-attributed graphs (TAGs) is vital for real-world applications, as they combine semantic textual and contextual structural information.
Texture, Shape and Order Matter: A New Transformer Design for Sequential DeepFake Detection
Our method features four major improvements: \ding{182} we describe a new texture-aware branch that effectively captures subtle manipulation traces with a Diversiform Pixel Difference Attention module.
Variational Bayesian Learning based Joint Localization and Path Loss Exponent with Distance-dependent Noise in Wireless Sensor Network
To surmount the intractable nonlinear and non-convex objective function inherent in the problem, we introduce a variational Bayesian learning-based framework that enables the joint optimization of localization, path loss exponent, and reference noise parameters by leveraging an effective approximation to the true posterior distribution.
Variational Bayesian Learning Based Localization and Channel Reconstruction in RIS-aided Systems
As opposed to existing works that treat channel estimation and localization independently, this paper exploits the intrinsic coupling and nonlinear relationships between the channel parameters and user location for enhancement of both localization and channel reconstruction.
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning
In this work, we present a novel subgame curriculum learning framework for zero-sum games.
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration
Simply waiting for every robot being ready for the next action can be particularly time-inefficient.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning
It has been a recent trend to leverage the power of supervised learning (SL) towards more effective reinforcement learning (RL) methods.
Learning Design and Construction with Varying-Sized Materials via Prioritized Memory Resets
Can a robot autonomously learn to design and construct a bridge from varying-sized blocks without a blueprint?
A Benchmark for Low-Switching-Cost Reinforcement Learning
A ubiquitous requirement in many practical reinforcement learning (RL) applications, including medical treatment, recommendation system, education and robotics, is that the deployed policy that actually interacts with the environment cannot change frequently.
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization
Matrix multiplication is the bedrock in Deep Learning inference application.
Learning to Design and Construct Bridge without Blueprint
In this task, the robot needs to first design a feasible bridge architecture for arbitrarily wide cliffs and then manipulate the blocks reliably to construct a stable bridge according to the proposed design.
Solving Compositional Reinforcement Learning Problems via Task Reduction
We propose a novel learning paradigm, Self-Imitation via Reduction (SIR), for solving compositional reinforcement learning problems.
D3PG: Deep Differentiable Deterministic Policy Gradients
Over the last decade, two competing control strategies have emerged for solving complex control tasks with high efficacy.
Pretrain Soft Q-Learning with Imperfect Demonstrations
Pretraining reinforcement learning methods with demonstrations has been an important concept in the study of reinforcement learning since a large amount of computing power is spent on online simulations with existing reinforcement learning algorithms.
