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Found 51 papers, 20 papers with code
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.
Disentangled Attention as Intrinsic Regularization for Bimanual Multi-Object Manipulation
To tackle these two issues, we propose a novel technique called disentangled attention, which provides an intrinsic regularization for two robots to focus on separate sub-tasks and objects.
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
Proximal Policy Optimization (PPO) is a popular on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent settings.
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 #157 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.
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 #6 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.
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.