A natural oriented (2k+2)-chain in CP^{2k+1} with boundary twice RP^{2k+1}, its complex shade, is constructed. Via intersection numbers with the shade, a new invariant, the shade number of k-dimensional subvarieties with normal vector fields along their real part, is introduced. For an even-dimensional real variety, th…
On-device research index
arXiv research
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.
168,786 papers · 148 categories
Trend · papers per month
25 results for “shading”
A closed Riemannian manifold is said to have cross blocking if whenever distinct points p and q are at distance less than the diameter, all light rays from p can be shaded away from q with at most two point shades. Similarly, a closed Riemannian manifold is said to have sphere blocking if for each point p, all the ligh…
Learning Concave Bid Shading Strategies in Online Auctions via Measure-valued Proximal Optimizationcs.LG
This work optimizes bid strategies for online auctions using measure-valued optimization.
problem Optimizing bid strategies in first-price auctions to maximize expected surplus.
method Formulates the problem as convex optimization over the joint distribution of shading parameters, adapts the distribution after each auction using a Wasserstein-proximal update.
result The proposed algorithm encourages bids on values with high expected surplus.
New bid shading algorithm reduces costs by 55%.
problem Minimizing costs in online advertising auctions.
method Win-rate prediction and surplus maximization using logistic regression.
result 7% more profit for advertisers compared to benchmarks.
Study proposes a machine learning method for bid shading in first-price auctions.
problem Maintaining strategy equilibrium in first-price auctions.
method Machine learning approach to model optimal bid shading.
result Demonstrates superiority and robustness of new approach across various metrics.
We use braids and linking number to explain why automobile shades fold into an odd number of loops.
Regularization is a big issue for training deep neural networks. In this paper, we propose a new information-theory-based regularization scheme named SHADE for SHAnnon DEcay. The originality of the approach is to define a prior based on conditional entropy, which explicitly decouples the learning of invariant represent…
Regularization is a big issue for training deep neural networks. In this paper, we propose a new information-theory-based regularization scheme named SHADE for SHAnnon DEcay. The originality of the approach is to define a prior based on conditional entropy, which explicitly decouples the learning of invariant represent…
Deep network optimizes ad bidding for first-price auctions.
problem Optimizing bid prices for first-price auctions in online advertising.
method Introduced a deep distribution network for optimal bidding.
result Algorithm outperforms previous methods in terms of surplus and eCPX metrics.
Improves synthetic data for deep model training and adaptation.
problem Evaluating and improving synthetic data for deep learning models.
method Proposes a novel learned synthesis technique using generative models for shading and rendering, and uses an ensemble of models to generate datasets.
result Improves classifier performance on real data compared to state-of-the-art methods.
We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds---e.g. the characteristic silverlining and the "whiteness" …
We study compact Riemannian manifolds for which the light between any pair of points is blocked by finitely many point shades. Compact flat Riemannian manifolds are known to have this finite blocking property. We conjecture that amongst compact Riemannian manifolds this finite blocking property characterizes the flat m…
We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires monocular endoscopic videos and a multi-view stereo method, e.g., structure from motion, to supervise learnin…
We consider the problem of finding critical points of functions that are non-convex and non-smooth. Studying a fairly broad class of such problems, we analyze the behavior of three gradient-based methods (gradient descent, proximal update, and Frank-Wolfe update). For each of these methods, we establish rates of conver…
Hill-ADAM optimizes loss landscapes by exploring state space deterministically.
problem Escaping local minima in loss landscapes.
method Hill-ADAM alternates between minimizing and maximizing error to explore the loss space.
result Hill-ADAM finds the global minimum state in loss landscapes.
Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications. However, recent studies show that DNNs are vulnerable to adversarial examples, which are carefully crafted inputs aiming to mislead the predictions. Currently, the majority of these studies have focused on per…
Some tasks, such as surface normals or single-view depth estimation, require per-pixel ground truth that is difficult to obtain on real images but easy to obtain on synthetic. However, models learned on synthetic images often do not generalize well to real images due to the domain shift. Our key idea to improve domain …
Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Taskscs.LG
Conventional collaborative filtering techniques treat a top-n recommendations problem as a task of generating a list of the most relevant items. This formulation, however, disregards an opposite - avoiding recommendations with completely irrelevant items. Due to that bias, standard algorithms, as well as commonly used …
Paper calculates braid indices for reverse parallel links of alternating knots.
problem Determining braid indices for arbitrary knots is challenging.
method Developed a precise formula for braid indices of reverse parallel links of alternating knots.
result A formula to calculate braid indices of reverse parallel links of alternating knots.
The paper explores group presentations for links in thickened surfaces, proving their relationship and introducing new invariants.
problem Proving the relationship between group presentations for links in thickened surfaces.
method Combining combinatorial arguments and homological information from surfaces to establish the relationship and introduce new invariants.
result The relationship between Dehn presentations and abelian Dehn coloring groups, and the introduction of the module as a stronger invariant.
Sunshine trading theory predicts lower execution costs and liquidity provision through explicit preannouncements, but evidence is scarce in traditional markets.
problem Adverse selection on liquidity provision
method Reconstructing metaorders and comparing them with visible TWAP executions
result Visible TWAPs face lower execution costs and leave a smaller permanent price impact compared to hidden metaorders.
Learning the structure of dependencies among multiple random variables is a problem of considerable theoretical and practical interest. Within the context of Bayesian Networks, a practical and surprisingly successful solution to this learning problem is achieved by adopting score-functions optimisation schema, augmente…
Introduces PAC-Bayes bounds for understanding learning procedures.
problem Understanding the generalization ability of learning procedures.
method PAC-Bayesian bounds and their applications to neural networks.
result Simplified version of localization technique described.
The paper tackles optimal level set estimation in crowdsourcing and tournaments.
problem Deciphering small entries in a partially observed matrix of expert correctness.
method Constructs an efficient polynomial-time algorithm for recovering level sets up to a precision.
result The algorithm is minimax optimal for the classification problem, contrasting with existing literature.
Learning the distribution of natural images is one of the hardest and most important problems in machine learning. The problem remains open, because the enormous complexity of the structures in natural images spans all length scales. We break down the complexity of the problem and show that the hierarchy of structures …