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.
1-D CNNs classify pupil size variations in scotopic conditions.
We first, introduce a deep learning based framework named as DeepIrisNet2 for visible spectrum and NIR Iris representation. The framework can work without classical iris normalization step or very accurate iris segmentation; allowing to work under non-ideal situation. The framework contains spatial transformer layers t…
We present a large scale data set, OpenEDS: Open Eye Dataset, of eye-images captured using a virtual-reality (VR) head mounted display mounted with two synchronized eyefacing cameras at a frame rate of 200 Hz under controlled illumination. This dataset is compiled from video capture of the eye-region collected from 152…
In our previous study, we introduced stable specification search for cross-sectional data (S3C). It is an exploratory causal method that combines stability selection concept and multi-objective optimization to search for stable and parsimonious causal structures across the entire range of model complexities. In this st…
Eye tracking is handled as one of the key technologies for applications that assess and evaluate human attention, behavior, and biometrics, especially using gaze, pupillary, and blink behaviors. One of the challenges with regard to the social acceptance of eye tracking technology is however the preserving of sensitive …
Recent advances in deep learning have facilitated the demand of neural models for real applications. In practice, these applications often need to be deployed with limited resources while keeping high accuracy. This paper touches the core of neural models in NLP, word embeddings, and presents a new embedding distillati…
Unlike machines, humans learn through rapid, abstract model-building. The role of a teacher is not simply to hammer home right or wrong answers, but rather to provide intuitive comments, comparisons, and explanations to a pupil. This is what the Learning Under Privileged Information (LUPI) paradigm endeavors to model b…
The ability to accurately predict the fit of fashion items and recommend the correct size is key to reducing merchandise returns in e-commerce. A critical prerequisite of fit prediction is size normalization, the mapping of product sizes across brands to a common space in which sizes can be compared. At present, size n…
A new scaling law predicts optimal batch size for training models.
Training deep neural networks with Stochastic Gradient Descent, or its variants, requires careful choice of both learning rate and batch size. While smaller batch sizes generally converge in fewer training epochs, larger batch sizes offer more parallelism and hence better computational efficiency. We have developed a n…
Implicit Q-learning and SARSA adjust step-sizes automatically, improving stability and performance.
Riemannian stochastic gradient descent converges faster with increasing batch size.
Study on size and depth of neural networks for approximating benign functions, showing barriers and explicit results.
When using active learning, smaller batch sizes are typically more efficient from a learning efficiency perspective. However, in practice due to speed and human annotator considerations, the use of larger batch sizes is necessary. While past work has shown that larger batch sizes decrease learning efficiency from a lea…
SGD's performance improves with critical batch size, minimizing SFO complexity.
Convolutional neural networks (CNNs) are commonly trained using a fixed spatial image size predetermined for a given model. Although trained on images of aspecific size, it is well established that CNNs can be used to evaluate a wide range of image sizes at test time, by adjusting the size of intermediate feature maps.…
A tick size is the smallest increment of a security price. It is clear that at the shortest time scale on which individual orders are placed the tick size has a major role which affects where limit orders can be placed, the bid-ask spread, etc. This is the realm of market microstructure and there is a vast literature o…
Adaptive batch size schedules improve language model training efficiency and generalization.
A new method SEBS optimizes SGD batch size for better performance.
The sizes of Markov equivalence classes of directed acyclic graphs play important roles in measuring the uncertainty and complexity in causal learning. A Markov equivalence class can be represented by an essential graph and its undirected subgraphs determine the size of the class. In this paper, we develop a method to …
New convergence results for NGVI with various step sizes and sample sizes.
Large batch sizes reduce gradient variance in DP-SGD, improving privacy.
Determining the appropriate batch size for mini-batch gradient descent is always time consuming as it often relies on grid search. This paper considers a resizable mini-batch gradient descent (RMGD) algorithm based on a multi-armed bandit for achieving best performance in grid search by selecting an appropriate batch s…
We make policy optimization algorithms batch size-invariant by decoupling proximal and behavior policies.
The paper analyzes fixed step-size SA schemes on Riemannian manifolds.
A simple block configures optimal kernel sizes for time series classification.
Revisits granular models explaining firm growth rates and sizes.
Study finds optimal vocabulary size for neural machine translation.
We determine the critical batch size for large language models and find it scales with data size, not model size.
Polyak step size GD reaches final radius of convergence after log iterations.
The paper analyzes the expected size of conformal prediction sets.
The paper analyzes and validates two step size schedules for SGD: exponential and cosine, proving their adaptivity and performance.
Adaptive batch sizes improve local gradient methods in distributed training.
Using detailed statistical analyses of the size distribution of a universe of equity exchange-traded funds (ETFs), we discover a discrete hierarchy of sizes, which imprints a log-periodic structure on the probability distribution of ETF sizes that dominates the details of the asymptotic tail. This allows us to propose …
AutoStep MCMC adapts step size locally for better sampling efficiency.
This work studies scaling laws for low-precision training in high-dimensional linear regression.
In this paper, we introduce a method for adapting the step-sizes of temporal difference (TD) learning. The performance of TD methods often depends on well chosen step-sizes, yet few algorithms have been developed for setting the step-size automatically for TD learning. An important limitation of current methods is that…
In this paper we address the question of the size distribution of firms. To this aim, we use the Bloomberg database comprising multinational firms within the years 1995-2003, and analyze the data of the sales and the total assets of the separate financial statement of the Japanese and the US companies, and make a compa…
This paper compares Transformers and RNNs in various tasks, showing size differences.
New analysis reveals batch size effects on stochastic conditional gradient methods.
Exchange improves liquidity by using different bid and ask tick sizes.
Mini-batch stochastic gradient descent and variants thereof have become standard for large-scale empirical risk minimization like the training of neural networks. These methods are usually used with a constant batch size chosen by simple empirical inspection. The batch size significantly influences the behavior of the …
New algorithm improves stability of optimization algorithms by adapting step-size.
Proposes a differentiable hypergeometric distribution for learning group importance.
Seesaw optimizes training by balancing learning rate and batch size, accelerating model pretraining.
The paper analyzes the risk of bagging regularized M-estimators under proportional asymptotics.
New neural network models learn symmetric functions of varying input sizes.
Firm size data usually do not show the normality that is often assumed in statistical analysis such as regression analysis. In this study we focus on two firm size data: the number of employees and sale. Those data deviate considerably from a normal distribution. To improve the normality of those data we transform them…