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Found 15 papers, 3 papers with code
PACE: A Large-Scale Dataset with Pose Annotations in Cluttered Environments
We introduce PACE (Pose Annotations in Cluttered Environments), a large-scale benchmark designed to advance the development and evaluation of pose estimation methods in cluttered scenarios.
Primitive-based 3D Human-Object Interaction Modelling and Programming
To explore an effective embedding of HAOI for the machine, we build a new benchmark on 3D HAOI consisting of primitives together with their images and propose a task requiring machines to recover 3D HAOI using primitives from images.
Over-the-Counter Market Making via Reinforcement Learning
The over-the-counter (OTC) market is characterized by a unique feature that allows market makers to adjust bid-ask spreads based on order size.
Market Making of Options via Reinforcement Learning
Market making of options with different maturities and strikes is a challenging problem due to its high dimensional nature.
Continuous-Time Path-Dependent Exploratory Mean-Variance Portfolio Construction
In this paper, we present an extended exploratory continuous-time mean-variance framework for portfolio management.
Electricity Virtual Bidding Strategy Via Entropy-Regularized Stochastic Control Method
We propose a virtual bidding strategy by modeling the price differences between the day-ahead market and the real-time market as Brownian motion with drift, where the drift rate and volatility are functions of meteorological variables.
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing
On the other hand, we design a constrained contrastive strategy on the hierarchical structure to directly model the type differences, which can simultaneously perceive the distinguishability between types at different granularity.
Homotopy Based Reinforcement Learning with Maximum Entropy for Autonomous Air Combat
In this paper, we propose a homotopy-based soft actor-critic method (HSAC) which focuses on addressing these problems via following the homotopy path between the original task with sparse reward and the auxiliary task with artificial prior experience reward.
Convergence of regularized particle filters for stochastic reaction networks
In this case, the regularized particle filter (RPF) is preferred to the conventional ones, as the RPF can mitigate sample degeneracy by perturbing particles with artificial noise.
Fighting Game Commentator with Pitch and Loudness Adjustment Utilizing Highlight Cues
This paper presents a commentator for providing real-time game commentary in a fighting game.
Stochastic filtering for multiscale stochastic reaction networks based on hybrid approximations
As a result, we are able to develop efficient particle filters to solve the filtering problem for the original model by applying particle filters to the reduced model.
Incorporating planning intelligence into deep learning: A planning support tool for street network design
Deep learning applications in shaping ad hoc planning proposals are limited by the difficulty in integrating professional knowledge about cities with artificial intelligence.
DeepStreet: A deep learning powered urban street network generation module
Specifically, the CNN is firstly trained to detect, recognize and capture the local features as well as the patterns of the existing street network sourced from the OpenStreetMap.
Towards An Angry-Birds-like Game System for Promoting Mental Well-being of Players Using Art-Therapy-embedded PCG
This paper presents an integration of a game system and the art therapy concept for promoting the mental well-being of video game players.
Efficacy of regularized multi-task learning based on SVM models
This paper investigates the efficacy of a regularized multi-task learning (MTL) framework based on SVM (M-SVM) to answer whether MTL always provides reliable results and how MTL outperforms independent learning.
