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Found 26 papers, 5 papers with code
Fréchet Statistics Based Change Point Detection in Multivariate Hawkes Process
This paper proposes a new approach for change point detection in causal networks of multivariate Hawkes processes using Frechet statistics.
Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance
We compared the model performances among our approach, baseline approach, and 3 alternative approaches to leverage semantic features.
Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution
To construct a dynamic player interaction graph, we leverage player statistics and their interactions during gameplay.
Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers
We embed prior information of strongly lensed arcs at cluster-scale into the training data through simulation and then train the detection algorithm with simulated images.
DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing
In the field of intelligent education, knowledge tracing (KT) has attracted increasing attention, which estimates and traces students' mastery of knowledge concepts to provide high-quality education.
Anomalous Edge Detection in Edge Exchangeable Social Network Models
This paper studies detecting anomalous edges in directed graphs that model social networks.
Revisiting the Characteristics of Stochastic Gradient Noise and Dynamics
In this paper, we characterize the noise of stochastic gradients and analyze the noise-induced dynamics during training deep neural networks by gradient-based optimizers.
More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching
Cross-modal attention mechanisms have been widely applied to the image-text matching task and have achieved remarkable improvements thanks to its capability of learning fine-grained relevance across different modalities.
Affordance-Based Mobile Robot Navigation Among Movable Obstacles
Avoiding obstacles in the perceived world has been the classical approach to autonomous mobile robot navigation.
Motion Planning
Robot Navigation
Robotics
Replica-Exchange Nos\'e-Hoover Dynamics for Bayesian Learning on Large Datasets
In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated modes in the presence of mini-batch noise.
Learning to Infer User Hidden States for Online Sequential Advertising
To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.
On the FRB luminosity function -- II. Event rate density
Assuming a Schechter luminosity function form, we infer (at the 95% confidence level) that the characteristic FRB event rate density at the upper cut-off luminosity $L^*=2. 9_{-1. 7}^{+11. 9}\times10^{44}\,\rm erg\, s^{-1}$ is $\phi^*=339_{-313}^{+1074}\,\rm Gpc^{-3}\, yr^{-1}$, the power-law index is $\alpha=-1. 79_{-0. 35}^{+0. 31}$, and the lower cut-off luminosity is $L_0\le9. 1\times10^{41}\,\rm erg\, s^{-1}$.
High Energy Astrophysical Phenomena Cosmology and Nongalactic Astrophysics
A weakly supervised adaptive triplet loss for deep metric learning
We address the problem of distance metric learning in visual similarity search, defined as learning an image embedding model which projects images into Euclidean space where semantically and visually similar images are closer and dissimilar images are further from one another.
Wasserstein Robust Reinforcement Learning
Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world.
Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment
In addition, in order to enrich the decoder with descriptive semantics and enforce the correctness of the deterministic medical-related contents such as mentions of organs or diagnoses, we extract medical concepts based on the radiology reports in the training data and fine-tune the encoder to extract the most frequent medical concepts from the x-ray images.
Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets
In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated modes in the presence of mini-batch noise.
Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning
Though limited in real-world decision making, most multi-agent reinforcement learning (MARL) models assume perfectly rational agents -- a property hardly met due to individual's cognitive limitation and/or the tractability of the decision problem.
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
Our methods are tested on both the matrix game and the differential game, which have a non-trivial equilibrium where common gradient-based methods fail to converge.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Parallel-tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling
We propose a new sampler that integrates the protocol of parallel tempering with the Nos\'e-Hoover (NH) dynamics.
Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series
As an indicator of the level of risk or the degree of variation, volatility is important to analyse the financial market, and it is taken into consideration in various decision-making processes in financial activities.
On the normalised FRB luminosity function
In this paper, we measure the normalised luminosity function of FRBs.
High Energy Astrophysical Phenomena
Mean Field Multi-Agent Reinforcement Learning
Existing multi-agent reinforcement learning methods are limited typically to a small number of agents.
A Neural Stochastic Volatility Model
In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance.
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
We propose a new sampling method, the thermostat-assisted continuously-tempered Hamiltonian Monte Carlo, for Bayesian learning on large datasets and multimodal distributions.
Adversarial Variational Bayes Methods for Tweedie Compound Poisson Mixed Models
Mixed models with random effects account for the covariance structure related to the grouping hierarchy in the data.
Learning text representation using recurrent convolutional neural network with highway layers
Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks.
