A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Automated graphics testing detects novel corruptions without manual labeling.
We explore the perspective of a bug living on the two-dimensional surface of a polyhedron. Images of various kinds of effects like lensing and cloaking are shown via color pictures of three viewpoints: the first person perspective of the bug, a map of the bug's viewpoint, and a look at the bug on the embedded polyhedro…
Develops a deep metric learning approach for detecting bugs in video games.
Training set bugs are flaws in the data that adversely affect machine learning. The training set is usually too large for man- ual inspection, but one may have the resources to verify a few trusted items. The set of trusted items may not by itself be adequate for learning, so we propose an algorithm that uses these ite…
Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques harness neural networks to learn directly from examples of buggy programs and their fixes. In this work, we consider a recently identified class of bugs called va…
We explore solutions for automated labeling of content in bug trackers and customer support systems. In order to do that, we classify content in terms of several criteria, such as priority or product area. In the first part of the paper, we provide an overview of existing methods used for text classification. These met…
Develops Kleinian Sphere Packings and Bugs, proving their arithmetic origins.
This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a system, called SequenceR, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary probl…
This paper generates natural-looking perturbations to fool classifiers.
This work uses image generation models to find vision model bugs.
Advanced driver assistance systems (ADAS) can be significantly improved with effective driver action prediction (DAP). Predicting driver actions early and accurately can help mitigate the effects of potentially unsafe driving behaviors and avoid possible accidents. In this paper, we formulate driver action prediction a…
New method identifies drivers from car logs without reverse-engineering CAN protocol.
Vacant taxi drivers' passenger seeking process in a road network generates additional vehicle miles traveled, adding congestion and pollution into the road network and the environment. This paper aims to employ a Markov Decision Process (MDP) to model idle e-hailing drivers' optimal sequential decisions in passenger-se…
The potential positive impact of autonomous driving and driver assistance technolo- gies have been a major impetus over the last decade. On the flip side, it has been a challenging problem to analyze the performance of human drivers or autonomous driving agents quantitatively. In this work, we propose a generic method …
The increasing inclusion of Machine Learning (ML) models in safety critical systems like autonomous cars have led to the development of multiple model-based ML testing techniques. One common denominator of these testing techniques is their assumption that training programs are adequate and bug-free. These techniques on…
CPCMs integrate causal drivers for robust portfolio optimization.
Optimizes portfolios using neural network approximations of asset sensitivities to common drivers.
Deep learning had been used in program analysis for the prediction of hidden software defects using software defect datasets, security vulnerabilities using generative adversarial networks as well as identifying syntax errors by learning a trained neural machine translation on program codes. However, all these approach…
TNDE quantifies dynamic gene drivers from single-cell snapshots.
ProMoD models human race drivers with probabilistic movement primitives and neural networks.
In this work, we propose a method for learning driver models that account for variables that cannot be observed directly. When trained on a synthetic dataset, our models are able to learn encodings for vehicle trajectories that distinguish between four distinct classes of driver behavior. Such encodings are learned wit…
Paper proposes personalized climate control for driver comfort.
Driver identification has emerged as a vital research field, where both practitioners and researchers investigate the potential of driver identification to enable a personalized driving experience. Within recent years, a selection of studies have reported that individuals could be perfectly identified based on their dr…
Deep RL tackles fleet management and dispatching for ride-sharing platforms.
This paper optimizes driver repositioning using MARL and reward design for better service and traffic management.
Study uses LCRN to detect driver distraction from EEG signals.
Bayes-optimal classifiers are robust to adversarial attacks, unlike CNNs trained on the same data.
Paper tackles drowsy driving by learning from weakly labeled car acceleration data.
This research simplifies verification of machine learning systems using reparameterization.
We consider the problem of numerical approximation for forward-backward stochastic differential equations with drivers of quadratic growth (qgFBSDE). To illustrate the significance of qgFBSDE, we discuss a problem of cross hedging of an insurance related financial derivative using correlated assets. For the convergence…
As automotive electronics continue to advance, cars are becoming more and more reliant on sensors to perform everyday driving operations. These sensors are omnipresent and help the car navigate, reduce accidents, and provide comfortable rides. However, they can also be used to learn about the drivers themselves. In thi…
Affective states have a critical role in driving performance and safety. They can degrade driver situation awareness and negatively impact cognitive processes, severely diminishing road safety. Therefore, detecting and assessing drivers' affective states is crucial in order to help improve the driving experience, and i…
Urban traffic systems worldwide are suffering from severe traffic safety problems. Traffic safety is affected by many complex factors, and heavily related to all drivers' behaviors involved in traffic system. Drivers with aggressive driving behaviors increase the risk of traffic accidents. In order to manage the safety…
Through deep learning and computer vision techniques, driving manoeuvres can be predicted accurately a few seconds in advance. Even though adapting a learned model to new drivers and different vehicles is key for robust driver-assistance systems, this problem has received little attention so far. This work proposes to …
Develops geometric BSDEs for modeling dynamic return risk measures.
The paper develops methods to identify and correct buggy data in linear regression models.
Study dynamic portfolio choice under rotating drivers, revealing a new geometric structure.
Bitcoin's price direction is better predicted without additional drivers during high volatility.
Modeling driver trajectories using inverse reinforcement learning and random utility.
AI helps Vancouver identify where off-street parking saves time and space.
We decompose the squared price-of-risk premium into three components: intervention-stable premium, confounding wedge, and information loss.
New model identifies anticyclonic patterns causing drought and heat.
New formula for portfolio risk management using conditional PDEs.
Paper proposes a capsule attention mechanism for EEG-EOG vigilance estimation.
In this paper, we study a class of Anticipated Backward Stochastic Differential Equations (ABSDE) with jumps. The solution of the ABSDE is a triple where is a semimartingale, and are the diffusion and jump coefficients. We allow the driver of the ABSDE to have linear growth on the uniform norm of …
The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a smal…
New machine learning model identifies key drivers of market troughs.
Improved risk assessment for UBI using telematics data and AdaBoost.