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Found 20 papers, 5 papers with code
Optimistic Model Rollouts for Pessimistic Offline Policy Optimization
ORPO generates Optimistic model Rollouts for Pessimistic offline policy Optimization.
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).
Intelligent Computing: The Latest Advances, Challenges and Future
In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications.
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.
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.
Trusted Multi-Scale Classification Framework for Whole Slide Image
Leveraging the Vision Transformer as the backbone for multi branches, our framework can jointly classification modeling, estimating the uncertainty of each magnification of a microscope and integrate the evidence from different magnification.
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.
Unsupervised Voice-Face Representation Learning by Cross-Modal Prototype Contrast
We present an approach to learn voice-face representations from the talking face videos, without any identity labels.
Multi-task Pre-training Language Model for Semantic Network Completion
To demonstrate the effectiveness of our method, we conduct extensive experiments on three widely-used datasets, WN18RR, FB15k-237, and UMLS.
Ranked #1 on
Link Prediction
on UMLS
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
FedH2L: Federated Learning with Model and Statistical Heterogeneity
Federated learning (FL) enables distributed participants to collectively learn a strong global model without sacrificing their individual data privacy.
Audio Tagging by Cross Filtering Noisy Labels
Yet, it is labor-intensive to accurately annotate large amount of audio data, and the dataset may contain noisy labels in the practical settings.
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods
Off-Policy Actor-Critic (Off-PAC) methods have proven successful in a variety of continuous control tasks.
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
Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural network
A challenge in speech production research is to predict future tongue movements based on a short period of past tongue movements.
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.
Collaborative Deep Learning Across Multiple Data Centers
Valuable training data is often owned by independent organizations and located in multiple data centers.
