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Found 19 papers, 4 papers with code
An Empirical Implementation of the Shadow Riskless Rate
We address the problem of asset pricing in a market where there is no risky asset.
Viewpoint Integration and Registration with Vision Language Foundation Model for Image Change Understanding
Therefore, they cannot be directly applied to cope with image change understanding (ICU), which requires models to capture actual changes between multiple images and describe them in language.
RSGPT: A Remote Sensing Vision Language Model and Benchmark
This dataset consists of human-annotated captions and visual question-answer pairs, allowing for a comprehensive assessment of VLMs in the context of RS.
The Implied Views of Bond Traders on the Spot Equity Market
This paper introduces a pricing algorithm for this bond and presents a formula that can be used to ascertain its real value.
SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting
Recurrent predictions for future atmospheric fields are firstly performed at 1. 40625-degree resolution, and then a diffusion-based super-resolution model is leveraged to recover the high spatial resolution and finer-scale atmospheric details.
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
Existing AI-related research in remote sensing primarily focuses on visual understanding tasks while neglecting the semantic understanding of the objects and their relationships.
Unifying Market Microstructure and Dynamic Asset Pricing
We introduce a discrete binary tree for pricing contingent claims with the underlying security prices exhibiting history dependence characteristic of that induced by market microstructure phenomena.
Option pricing using a skew random walk pricing tree
Motivated by the Corns-Satchell, continuous time, option pricing model, we develop a binary tree pricing model with underlying asset price dynamics following It\^o-Mckean skew Brownian motion.
PolyBuilding: Polygon Transformer for End-to-End Building Extraction
Given a set of polygon queries, the model learns the relations among them and encodes context information from the image to predict the final set of building polygons with fixed vertex numbers.
ESG-valued discrete option pricing in complete markets
We consider option pricing using replicating binomial trees, with a two fold purpose.
SwinVRNN: A Data-Driven Ensemble Forecasting Model via Learned Distribution Perturbation
We also compare four categories of perturbation methods for ensemble forecasting, i. e. fixed distribution perturbation, learned distribution perturbation, MC dropout, and multi model ensemble.
Market Complete Option Valuation using a Jarrow-Rudd Pricing Tree with Skewness and Kurtosis
Applying the Cherny-Shiryaev-Yor invariance principle, we introduce a generalized Jarrow-Rudd (GJR) option pricing model with uncertainty driven by a skew random walk.
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one.
Portfolio Optimization Constrained by Performance Attribution
This paper investigates performance attribution measures as a basis for constraining portfolio optimization.
Option Pricing Incorporating Factor Dynamics in Complete Markets
Using the Donsker-Prokhorov invariance principle we extend the Kim-Stoyanov-Rachev-Fabozzi option pricing model to allow for variably-spaced trading instances, an important consideration for short-sellers of options.
GBCNs: Genetic Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs
The BGA method is proposed to modify the binary process of GBCNs to alleviate the local minima problem, which can significantly improve the performance of 1-bit DCNNs.
RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs
Binarized convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips based applications.
Panoptic Edge Detection
In this work, we address a new finer-grained task, termed panoptic edge detection (PED), which aims at predicting semantic-level boundaries for stuff categories and instance-level boundaries for instance categories, in order to provide more comprehensive and unified scene understanding from the perspective of edges. We then propose a versatile framework, Panoptic Edge Network (PEN), which aggregates different tasks of object detection, semantic and instance edge detection into a single holistic network with multiple branches.
Dynamic Feature Fusion for Semantic Edge Detection
In this work, we propose a novel dynamic feature fusion strategy that assigns different fusion weights for different input images and locations adaptively.
