Search Results for author:
Found 13 papers, 1 papers with code
Uncertainty-Penalized Reinforcement Learning from Human Feedback with Diverse Reward LoRA Ensembles
Reinforcement learning from human feedback (RLHF) emerges as a promising paradigm for aligning large language models (LLMs).
MyPortrait: Morphable Prior-Guided Personalized Portrait Generation
We incorporate personalized prior in a monocular video and morphable prior in 3D face morphable space for generating personalized details under novel controllable parameters.
NicePIM: Design Space Exploration for Processing-In-Memory DNN Accelerators with 3D-Stacked-DRAM
As one of the feasible processing-in-memory(PIM) architectures, 3D-stacked-DRAM-based PIM(DRAM-PIM) architecture enables large-capacity memory and low-cost memory access, which is a promising solution for DNN accelerators with better performance and energy efficiency.
Task modules Partitioning, Scheduling and Floorplanning for Partially Dynamically Reconfigurable Systems Based on Modern Heterogeneous FPGAs
To better solve the problems in the automation process of FPGA-PDRS and narrow the gap between algorithm and application, in this paper, we propose a complete workflow including three parts, pre-processing to generate the list of task modules candidate shapes according to the resources requirements, exploration process to search the solution of task modules partitioning, scheduling, and floorplanning, and post-optimization to improve the success rate of floorplan.
Self-Supervised Exploration via Temporal Inconsistency in Reinforcement Learning
Our method involves training a self-supervised prediction model, saving snapshots of the model parameters, and using nuclear norm to evaluate the temporal inconsistency between the predictions of different snapshots as intrinsic rewards.
Dynamic Memory-based Curiosity: A Bootstrap Approach for Exploration
The curiosity arouses if memorized information can not deal with the current state, and the information gap between dual learners can be formulated as the intrinsic reward for agents, and then such state information can be consolidated into the dynamic memory.
Nuclear Norm Maximization Based Curiosity-Driven Learning
To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed intrinsic reward which enables the agent to learn the skills that might come in handy for pursuing the rewards in the future, such as encouraging the agent to visit novel states.
KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning
In this paper, we present an adaptation method of the majority of multi-agent reinforcement learning (MARL) algorithms called KnowSR which takes advantage of the differences in learning between agents.
Knowledge Distillation
Multi-agent Reinforcement Learning
+2
KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning
In this paper, we propose a method, named "KnowRU" for knowledge reusing which can be easily deployed in the majority of the multi-agent reinforcement learning algorithms without complicated hand-coded design.
Knowledge Distillation
Multi-agent Reinforcement Learning
+2
Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems
The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach
Furthermore, we successfully exploit our unsupervised learning framework to assist the traditional ORB-SLAM system when the initialization module of ORB-SLAM method could not match enough features.
Learning data augmentation policies using augmented random search
AutoAugment searches for the augmentation polices in the discrete search space, which may lead to a sub-optimal solution.
General audio tagging with ensembling convolutional neural network and statistical features
Audio tagging is challenging due to the limited size of data and noisy labels.
