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.
Classifies when homeomorphism groups of stable surfaces have automatic continuity.
The theory of automatic groups is developed, including properties and practical algorithms.
Classifies surfaces for pure mapping class groups with automatic continuity.
Automatic computation speeds up crosscap number calculation for alternating knots.
Automatic differentiation---the mechanical transformation of numeric computer programs to calculate derivatives efficiently and accurately---dates to the origin of the computer age. Reverse mode automatic differentiation both antedates and generalizes the method of backwards propagation of errors used in machine learni…
Method uses Seq2Seq learning to automatically generate recovery commands for ICT systems.
Spoken language translation (SLT) has become very important in an increasingly globalized world. Machine translation (MT) for automatic speech recognition (ASR) systems is a major challenge of great interest. This research investigates that automatic sentence segmentation of speech that is important for enriching speec…
This paper presents a novel approach for automatic rule learning applicable to an autonomous driving system using real driving data.
Develops a mathematical model for automatic differentiation in machine learning.
We present a system for the automatic differentiation of a higher-order functional array-processing language. The core functional language underlying this system simultaneously supports both source-to-source automatic differentiation and global optimizations such as loop transformations. Thanks to this feature, we demo…
Paper proposes NNAFC for automatic financial factor construction.
agtboost speeds up gradient tree boosting with automatic complexity adjustment.
Making sense of a dataset in an automatic and unsupervised fashion is a challenging problem in statistics and AI. Classical approaches for {exploratory data analysis} are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models an…
Procedure groups nonparametric regression curves automatically.
AutoSGD automatically adjusts learning rates for SGD.
Super-efficient automatic differentiation outperforms analytic methods in min-min optimization.
D-Adaptation automatically sets optimal learning rates without manual tuning.
GP model for time series forecasting with priors.
The performance of automatic speech recognition systems(ASR) degrades in the presence of noisy speech. This paper demonstrates that using electroencephalography (EEG) can help automatic speech recognition systems overcome performance loss in the presence of noise. The paper also shows that distillation training of auto…
This paper describes the data collection effort that is part of the project Sprekend Nederland (The Netherlands Talking), and discusses its potential use in Automatic Accent Location. We define Automatic Accent Location as the task to describe the accent of a speaker in terms of the location of the speaker and its hist…
Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it difficult to automate. We propose an automatic variational inference algorithm, automatic differentiation variational inference (ADVI). …
A new method, InfoGuide, improves automatic clustering analysis.
Meta-SAC automatically tunes SAC's entropy temperature for better exploration.
Proposes an automatic cyclical scheduling for gradient-based discrete sampling.
In this paper we study automatically recognized trends and investigate their statistics. To do that we introduce the notion of a wavelength for time series via cross correlation and use this wavelength to calibrate the 1-2-3 trend indicator of Maier-Paape [Automatic One Two Three, Quantitative Finance, 2013] to automat…
This paper proposes automatic tuning of Bayesian Optimization's acquisition function.
We show that any homomorphism from the homeomorphism group of a compact 2-manifold, with the compact-open topology, or equivalently, with the topology of uniform convergence, into a separable topological group is automatically continuous.
Automatic continuity of polynomial maps and cocycles proved.
The paper analyzes sports commentary to automatically recognize events and extract insights.
Purpose: Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge…
New algorithm selects variables from large datasets.
We discuss a problem posed by Gersten: Is every automatic group which does not contain Z+Z subgroup, hyperbolic? To study this question, we define the notion of "n-tracks of length n", which is a structure like Z+Z, and prove its existence in the non-hyperbolic automatic groups with mild conditions. As an application, …
We propose a novel algorithm for efficiently computing a sparse directed adjacency matrix from a group of time series following a causal graph process. Our solution is scalable for both dense and sparse graphs and automatically selects the LASSO coefficient to obtain an appropriate number of edges in the adjacency matr…
We use AD to compute gradients for complex functionals in stochastic model calibration.
ARDA automatically augments datasets for machine learning models.
AutoFIS automatically selects important feature interactions for CTR prediction models.
This work presents an approach to automatically induction for non-greedy decision trees constructed from neural network architecture. This construction can be used to transfer weights when growing or pruning a decision tree, allowing non-greedy decision tree algorithms to automatically learn and adapt to the ideal arch…
Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidime…
In this note, we report the back propagation formula for complex valued singular value decompositions (SVD). This formula is an important ingredient for a complete automatic differentiation(AD) infrastructure in terms of complex numbers, and it is also the key to understand and utilize AD in tensor networks.
Generative adversarial network system improves ECG arrhythmia classification.
AutoInit automatically finds good neural network initialization.
Automatically generates a deep RL curriculum for faster and more stable learning.
AutoGD automatically adjusts learning rates for gradient descent.
Learn to automatically plug domain-specific modules into a common network.
We consider options that pay the complexity deficiency of a sequence of up and down ticks of a stock upon exercise. We study the price of European and American versions of this option numerically for automatic complexity, and theoretically for Kolmogorov complexity. We also consider run complexity, which is a restricte…
New deep learning method validated across multiple sleep staging databases.
Structured weight pruning is a representative model compression technique of DNNs to reduce the storage and computation requirements and accelerate inference. An automatic hyperparameter determination process is necessary due to the large number of flexible hyperparameters. This work proposes AutoCompress, an automatic…
A recurring problem when building probabilistic latent variable models is regularization and model selection, for instance, the choice of the dimensionality of the latent space. In the context of belief networks with latent variables, this problem has been adressed with Automatic Relevance Determination (ARD) employing…