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Found 18 papers, 3 papers with code
BIT’s system for AutoSimulTrans2021
In this paper we introduce our Chinese-English simultaneous translation system participating in AutoSimulTrans2021.
PE-MVCNet: Multi-view and Cross-modal Fusion Network for Pulmonary Embolism Prediction
In a clinical setting, physicians tend to rely on the contextual information provided by Electronic Medical Records (EMR) to interpret medical imaging.
Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies
Causal emergence theory aims to bridge these two concepts and even employs measures of causality to quantify emergence.
Market Misconduct in Decentralized Finance (DeFi): Analysis, Regulatory Challenges and Policy Implications
The rise of blockchain and Decentralized Finance (DeFi) underscores this intertwined evolution of technology and finance.
Leverage Staking with Liquid Staking Derivatives (LSDs): Opportunities and Risks
Lido, the leading Liquid Staking Derivative (LSD) provider on Ethereum, allows users to stake an arbitrary amount of ETH to receive stETH, which can be integrated with Decentralized Finance (DeFi) protocols such as Aave.
An Approach for Multi-Object Tracking with Two-Stage Min-Cost Flow
Specifically, we employ the minimum network flow algorithm with high-confidence detections as input in the first stage to obtain the candidate tracklets that need correction.
Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor Configuration
The proposed method simplifies fall detection data acquisition experiments, provides novel venue for generating low cost synthetic data in scenario where acquiring data for machine learning is challenging and paves the way for customizing machine learning configurations.
zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning
Federated Learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator.
Finding emergence in data by maximizing effective information
Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to the lack of direct observations at the micro-level.
Defending Against Poisoning Attacks in Federated Learning with Blockchain
In the era of deep learning, federated learning (FL) presents a promising approach that allows multi-institutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy.
Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning
We evaluate our method both in closed-loop simulation and real world driving, and demonstrate the neural network planner has outstanding performance in complex urban autonomous driving scenarios.
FLock: Defending Malicious Behaviors in Federated Learning with Blockchain
Federated learning (FL) is a promising way to allow multiple data owners (clients) to collaboratively train machine learning models without compromising data privacy.
Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval
Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achieve retrieval, which neglect the problems of the domain gap between sketches and natural images and the large intra-class diversity in sketches.
Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement
Fine-tuning deep neural networks pre-trained on large scale datasets is one of the most practical transfer learning paradigm given limited quantity of training samples.
Dynamic Fusion based Federated Learning for COVID-19 Detection
To improve communication efficiency and model performance, in this paper, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections.
Nonparametric Density Estimation for High-Dimensional Data - Algorithms and Applications
Density Estimation is widely adopted in the domain of unsupervised learning especially for the application of clustering.
Scale Aggregation Network for Accurate and Efficient Crowd Counting
In this paper, we propose a novel encoder-decoder network, called extit{Scale Aggregation Network (SANet)}, for accurate and efficient crowd counting.
Ranked #6 on
Crowd Counting
on WorldExpo’10
Reinforcement Learning applied to Single Neuron
In summary, we took the initial endeavor to study the reinforcement learning for multi-agents system.
