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Found 79 papers, 29 papers with code
Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased
A crucial limitation of this framework is that every policy in the pool is optimized w. r. t.
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
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.
AlphaSnake: Policy Iteration on a Nondeterministic NP-hard Markov Decision Process
Reinforcement learning has recently been used to approach well-known NP-hard combinatorial problems in graph theory.
PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation
In this paper, we focus on two key questions in knowledge distillation for ranking models: 1) how to ensemble knowledge from multi-teacher; 2) how to utilize the label information of data in the distillation process.
FedBA: Non-IID Federated Learning Framework in UAV Networks
This is because most drones still use centralized cloud-based data processing, which may lead to leakage of data collected by drones.
Hand-Assisted Expression Recognition Method from Synthetic Images at the Fourth ABAW Challenge
Learning from synthetic images plays an important role in facial expression recognition task due to the difficulties of labeling the real images, and it is challenging because of the gap between the synthetic images and real images.
Multi-Task Learning for Emotion Descriptors Estimation at the fourth ABAW Challenge
The 4th competition on affective behavior analysis in the wild (ABAW) provided images with valence/arousal, expression and action unit labels.
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.
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning
Despite all the advantages, we revisit these two principles and show that in certain scenarios, e. g., environments with a highly multi-modal reward landscape, value decomposition, and parameter sharing can be problematic and lead to undesired outcomes.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition
However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information.
Self-Calibrated Efficient Transformer for Lightweight Super-Resolution
Recently, deep learning has been successfully applied to the single-image super-resolution (SISR) with remarkable performance.
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?
E^2TAD: An Energy-Efficient Tracking-based Action Detector
Video action detection (spatio-temporal action localization) is usually the starting point for human-centric intelligent analysis of videos nowadays.
Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization
We present Reward-Switching Policy Optimization (RSPO), a paradigm to discover diverse strategies in complex RL environments by iteratively finding novel policies that are both locally optimal and sufficiently different from existing ones.
Understanding Curriculum Learning in Policy Optimization for Solving Combinatorial Optimization Problems
For a canonical online CO problem, Secretary Problem, we formally prove that distribution shift is reduced exponentially with curriculum learning even if the curriculum is randomly generated.
Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones
This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model.
Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination
We study the problem of training a Reinforcement Learning (RL) agent that is collaborative with humans without using any human data.
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension
We present Native Chinese Reader (NCR), a new machine reading comprehension (MRC) dataset with particularly long articles in both modern and classical Chinese.
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.
Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward
These scenarios indeed correspond to the vulnerabilities of the under-test driving policies, thus are meaningful for their further improvements.
NovelD: A Simple yet Effective Exploration Criterion
We analyze NovelD thoroughly in MiniGrid and found that empirically it helps the agent explore the environment more uniformly with a focus on exploring beyond the boundary.
Nonlinear Intensity Sonar Image Matching based on Deep Convolution Features
In the field of deep-sea exploration, sonar is presently the only efficient long-distance sensing device.
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems
We motivate our paradigm through a variational perspective, where the learning objective can be decomposed into two terms: task learning on the current task distribution, and curriculum update to a new task distribution.
PhyloTransformer: A Discriminative Model for Mutation Prediction Based on a Multi-head Self-attention Mechanism
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused an ongoing pandemic infecting 219 million people as of 10/19/21, with a 3. 6% mortality rate.
Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design
In this paper, we present a robust semi-supervised FL system design, where the system aims to solve the problem of data availability and non-IID in FL.
SDWNet: A Straight Dilated Network with Wavelet Transformation for Image Deblurring
On this basis, we reduce the number of up-sampling and down-sampling and design a simple network structure.
Ranked #1 on
Image Deblurring
on RealBlur-R(trained on GoPro)
Learning Efficient Multi-Agent Cooperative Visual Exploration
In this paper, we extend the state-of-the-art single-agent visual navigation method, Active Neural SLAM (ANS), to the multi-agent setting by introducing a novel RL-based planning module, Multi-agent Spatial Planner (MSP). MSP leverages a transformer-based architecture, Spatial-TeamFormer, which effectively captures spatial relations and intra-agent interactions via hierarchical spatial self-attentions.
Sequence Level Contrastive Learning for Text Summarization
In this paper, we propose a contrastive learning model for supervised abstractive text summarization, where we view a document, its gold summary and its model generated summaries as different views of the same mean representation and maximize the similarities between them during training.
Temporal Induced Self-Play for Stochastic Bayesian Games
One practical requirement in solving dynamic games is to ensure that the players play well from any decision point onward.
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.
BLCUFIGHT at SemEval-2021 Task 10: Novel Unsupervised Frameworks For Source-Free Domain Adaptation
Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase.
DAIR: Disentangled Attention Intrinsic Regularization for Safe and Efficient Bimanual Manipulation
We address the problem of safely solving complex bimanual robot manipulation tasks with sparse rewards.
FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia
Under the FL framework and Differentially Private thinking, we propose a FederatedDifferentially Private Generative Adversarial Network (FedDPGAN) to detectCOVID-19 pneumonia for sustainable smart cities.
Classifying herbal medicine origins by temporal and spectral data mining of electronic nose
In this study, we classified different origins of three categories of herbal medicines with different feature extraction methods: manual feature extraction, mathematical transformation, deep learning algorithms.
Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels
However, the inability of GNNs to model substructures in graphs remains a significant drawback.
Reference-Aided Part-Aligned Feature Disentangling for Video Person Re-Identification
Due to the inaccurate person detections and pose changes, pedestrian misalignment significantly increases the difficulty of feature extraction and matching.
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.
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization
We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games.
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Growth, Electronic Structure and Superconductivity of Ultrathin Epitaxial CoSi2 Films
We report growth, electronic structure and superconductivity of ultrathin epitaxial CoSi2 films on Si(111).
Mesoscale and Nanoscale Physics
Deep Q-Learning with Low Switching Cost
We initiate the study on deep reinforcement learning problems that require low switching cost, i. e., small number of policy switches during training.
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms
We benchmark commonly used multi-agent deep reinforcement learning (MARL) algorithms on a variety of cooperative multi-agent games.
BeBold: Exploration Beyond the Boundary of Explored Regions
In this paper, we analyze the pros and cons of each method and propose the regulated difference of inverse visitation counts as a simple but effective criterion for IR.
Charge density wave and weak Kondo effect in a Dirac semimetal CeSbTe
Using angle-resolved photoemission spectroscopy (ARPES) and low-energy electron diffraction (LEED), together with density-functional theory (DFT) calculation, we report the formation of charge density wave (CDW) and its interplay with the Kondo effect and topological states in CeSbTe.
Strongly Correlated Electrons Materials Science
Multi-Agent Collaboration via Reward Attribution Decomposition
In this work, we propose Collaborative Q-learning (CollaQ) that achieves state-of-the-art performance in the StarCraft multi-agent challenge and supports ad hoc team play.
Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers
We also find in experiments that our model is less dependent on sentence positions.
Streaming Graph Neural Networks via Continual Learning
In this paper, we propose a streaming GNN model based on continual learning so that the model is trained incrementally and up-to-date node representations can be obtained at each time step.
Hybrid-Attention Guided Network with Multiple Resolution Features for Person Re-Identification
Finally, we reconstruct the feature extractor to ensure that our model can obtain more richer and robust features.
Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation
In fact, due to the limitation of the receptive field, the 3 x 3 kernel has difficulty in covering the corresponding features at high/original resolution.
Multi-Task Reinforcement Learning with Soft Modularization
While training multiple tasks jointly allow the policies to share parameters across different tasks, the optimization problem becomes non-trivial: It remains unclear what parameters in the network should be reused across tasks, and how the gradients from different tasks may interfere with each other.
Ranked #1 on
Meta-Learning
on MT50
SaccadeNet: A Fast and Accurate Object Detector
Object detection is an essential step towards holistic scene understanding.
Ranked #205 on
Object Detection
on COCO test-dev
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning
In multi-agent games, the complexity of the environment can grow exponentially as the number of agents increases, so it is particularly challenging to learn good policies when the agent population is large.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
SQLFlow: A Bridge between SQL and Machine Learning
Previous database systems extended their SQL dialect to support ML.
Influence-Based Multi-Agent Exploration
We present two exploration methods: exploration via information-theoretic influence (EITI) and exploration via decision-theoretic influence (EDTI), by exploiting the role of interaction in coordinated behaviors of agents.
PPGAN: Privacy-preserving Generative Adversarial Network
To address this issue, we propose a Privacy-preserving Generative Adversarial Network (PPGAN) model, in which we achieve differential privacy in GANs by adding well-designed noise to the gradient during the model learning procedure.
Emergent Tool Use From Multi-Agent Autocurricula
Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocurriculum inducing multiple distinct rounds of emergent strategy, many of which require sophisticated tool use and coordination.
Bayesian Relational Memory for Semantic Visual Navigation
We introduce a new memory architecture, Bayesian Relational Memory (BRM), to improve the generalization ability for semantic visual navigation agents in unseen environments, where an agent is given a semantic target to navigate towards.
Fairness in Recommendation Ranking through Pairwise Comparisons
Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information.
Deep Reinforcement Learning for Green Security Games with Real-Time Information
Green Security Games (GSGs) have been proposed and applied to optimize patrols conducted by law enforcement agencies in green security domains such as combating poaching, illegal logging and overfishing.
Learning and Planning with a Semantic Model
Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI.
Robust Fuzzy-Learning For Partially Overlapping Channels Allocation In UAV Communication Networks
This allows the system to achieve a smoother and more robust performance by optimizing in an alternate space.
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Despite the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for random variables whose distributions combine discrete and continuous elements.
Near-Linear Time Local Polynomial Nonparametric Estimation with Box Kernels
Local polynomial regression (Fan and Gijbels 1996) is an important class of methods for nonparametric density estimation and regression problems.
Building Generalizable Agents with a Realistic and Rich 3D Environment
To generalize to unseen environments, an agent needs to be robust to low-level variations (e. g. color, texture, object changes), and also high-level variations (e. g. layout changes of the environment).
Egocentric Gesture Recognition Using Recurrent 3D Convolutional Neural Networks With Spatiotemporal Transformer Modules
Gesture is a natural interface in interacting with wearable devices such as VR/AR helmet and glasses.
Adversarial Training for Relation Extraction
Adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data.
Meta-Learning MCMC Proposals
The learned neural proposals generalize to occurrences of common structural motifs across different models, allowing for the construction of a library of learned inference primitives that can accelerate inference on unseen models with no model-specific training required.
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection.
Ranked #4 on
Object Detection
on PASCAL VOC 2007
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
We explore deep reinforcement learning methods for multi-agent domains.
Ranked #1 on
SMAC+
on Def_Infantry_sequential
Swift: Compiled Inference for Probabilistic Programming Languages
A probabilistic program defines a probability measure over its semantic structures.
Improving the Annotation of Sentence Specificity
We found that the lack of specificity distributes evenly among immediate prior context, long distance prior context and no prior context.
Towards Practical Bayesian Parameter and State Estimation
Joint state and parameter estimation is a core problem for dynamic Bayesian networks.
Value Iteration Networks
We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within.
Adaptive Compressive Tracking via Online Vector Boosting Feature Selection
Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that results in less discriminative features.
Tractability and Decompositions of Global Cost Functions
A global cost function is called tractable projection-safe when applying an EPT to it is tractable and does not break the tractability property.
Robust Visual Tracking via Convolutional Networks
In this paper we present that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to develop a robust representation for visual tracking.
Online Object Tracking: A Benchmark
Object tracking is one of the most important components in numerous applications of computer vision.
Dual-Space Analysis of the Sparse Linear Model
In contrast, for analyses of update rules and sparsity properties of local and global solutions, as well as extensions to more general likelihood models, we can leverage coefficient-space techniques developed for Type I and apply them to Type II.
