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Found 20 papers, 3 papers with code
Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category.
Robust Replication of Volatility and Hybrid Derivatives on Jump Diffusions
We price and replicate a variety of claims written on the log price $X$ and quadratic variation $[X]$ of a risky asset, modeled as a positive semimartingale, subject to stochastic volatility and jumps.
Semi-closed form prices of barrier options in the time-dependent CEV and CIR models
The second one is the method of generalized integral transform, which is also extended to the Bessel process.
Semi-closed form solutions for barrier and American options written on a time-dependent Ornstein Uhlenbeck process
In this paper we develop a semi-closed form solutions for the barrier (perhaps, time-dependent) and American options written on the underlying stock which follows a time-dependent OU process with a log-normal drift.
Argoverse: 3D Tracking and Forecasting with Rich Maps
In our baseline experiments, we illustrate how detailed map information such as lane direction, driveable area, and ground height improves the accuracy of 3D object tracking and motion forecasting.
A lognormal type stochastic volatility model with quadratic drift
This paper presents a novel one-factor stochastic volatility model where the instantaneous volatility of the asset log-return is a diffusion with a quadratic drift and a linear dispersion function.
A model-free backward and forward nonlinear PDEs for implied volatility
We derive a backward and forward nonlinear PDEs that govern the implied volatility of a contingent claim whenever the latter is well-defined.
ADOL - Markovian approximation of rough lognormal model
In this paper we apply Markovian approximation of the fractional Brownian motion (BM), known as the Dobric-Ojeda (DO) process, to the fractional stochastic volatility model where the instantaneous variance is modelled by a lognormal process with drift and fractional diffusion.
Geometric Local Variance Gamma model
This paper describes another extension of the Local Variance Gamma model originally proposed by P. Carr in 2008, and then further elaborated on by Carr and Nadtochiy, 2017 (CN2017), and Carr and Itkin, 2018 (CI2018).
Domain Adaptation through Synthesis for Unsupervised Person Re-identification
To achieve better accuracy in unseen illumination conditions we propose a novel domain adaptation technique that takes advantage of our synthetic data and performs fine-tuning in a completely unsupervised way.
Ranked #13 on
Person Re-Identification
on PRID2011
Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification
As a result, the network learns latent representations of the face, torso and other body parts using the best available image patches from the entire video sequence.
An Expanded Local Variance Gamma model
The paper proposes an expanded version of the Local Variance Gamma model of Carr and Nadtochiy by adding drift to the governing underlying process.
One-Shot Metric Learning for Person Re-Identification
The proposed one-shot learning achieves performance that is competitive with supervised methods, but uses only a single example rather than the hundreds required for the fully supervised case.
Factorized Variational Autoencoders for Modeling Audience Reactions to Movies
Matrix and tensor factorization methods are often used for finding underlying low-dimensional patterns from noisy data.
Coordinated Multi-Agent Imitation Learning
We study the problem of imitation learning from demonstrations of multiple coordinating agents.
Smooth Imitation Learning for Online Sequence Prediction
We study the problem of smooth imitation learning for online sequence prediction, where the goal is to train a policy that can smoothly imitate demonstrated behavior in a dynamic and continuous environment in response to online, sequential context input.
Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees
We study the problem of online prediction for realtime camera planning, where the goal is to predict smooth trajectories that correctly track and frame objects of interest (e. g., players in a basketball game).
Robust replication of barrier-style claims on price and volatility
We show how to price and replicate a variety of barrier-style claims written on the $\log$ price $X$ and quadratic variation $\langle X \rangle$ of a risky asset.
Tracking Sports Players with Context-Conditioned Motion Models
Instead, we introduce a set of Game Context Features extracted from noisy detections to describe the current state of the match, such as how the players are spatially distributed.
Representing and Discovering Adversarial Team Behaviors Using Player Roles
In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain.
