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08/13/2023
Fréchet Statistics Based Change Point Detection in Multivariate Hawkes Process
This paper proposes a new approach for change point detection in causal ...
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05/09/2023
Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance
Fine-grained multi-label classification models have broad applications i...
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03/29/2023
Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution
This study presents a novel deep learning method, called GATv2-GCN, for ...
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03/08/2023
Team Northeastern's Approach to ANA XPRIZE Avatar Final Testing: A Holistic Approach to Telepresence and Lessons Learned
This paper reports on Team Northeastern's Avatar system for telepresence...
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11/11/2022
Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers
Strong lensing in galaxy clusters probes properties of dense cores of da...
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10/18/2022
DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing
In the field of intelligent education, knowledge tracing (KT) has attrac...
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03/30/2022
Hawkes Process Modeling of Block Arrivals in Bitcoin Blockchain
The paper constructs a multi-variate Hawkes process model of Bitcoin blo...
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09/27/2021
Anomalous Edge Detection in Edge Exchangeable Social Network Models
This paper studies detecting anomalous edges in directed graphs that mod...
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05/20/2021
More Than Just Attention: Learning Cross-Modal Attentions with Contrastive Constraints
Attention mechanisms have been widely applied to cross-modal tasks such ...
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02/09/2021
Affordance-Based Mobile Robot Navigation Among Movable Obstacles
Avoiding obstacles in the perceived world has been the classical approac...
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09/03/2020
Learning to Infer User Hidden States for Online Sequential Advertising
To drive purchase in online advertising, it is of the advertiser's great...
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09/27/2019
A weakly supervised adaptive triplet loss for deep metric learning
We address the problem of distance metric learning in visual similarity ...
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07/30/2019
Wasserstein Robust Reinforcement Learning
Reinforcement learning algorithms, though successful, tend to over-fit t...
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07/22/2019
Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment
Generating radiology reports is time-consuming and requires extensive ex...
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05/29/2019
Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets
In this paper, we propose a new sampler for Bayesian learning that can e...
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01/26/2019
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
Humans are capable of attributing latent mental contents such as beliefs...
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12/04/2018
Parallel-tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling
We propose a new sampler that integrates the protocol of parallel temper...
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11/08/2018
Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series
Volatility is a quantity of measurement for the price movements of stock...
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02/15/2018
Mean Field Multi-Agent Reinforcement Learning
Existing multi-agent reinforcement learning methods are limited typicall...
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11/30/2017
Thermostat-assisted Continuous-tempered Hamiltonian Monte Carlo for Multimodal Posterior Sampling
In this paper, we propose a new sampling method named as the thermostat-...
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11/30/2017
A Neural Stochastic Volatility Model
In this paper, we show that the recent integration of statistical models...
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06/22/2016