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.
Method uses Seq2Seq learning to automatically generate recovery commands for ICT systems.
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…
Generative adversarial network system improves ECG arrhythmia classification.
Automatically generates a deep RL curriculum for faster and more stable learning.
Existing automatic music generation approaches that feature deep learning can be broadly classified into two types: raw audio models and symbolic models. Symbolic models, which train and generate at the note level, are currently the more prevalent approach; these models can capture long-range dependencies of melodic st…
New deep learning method validated across multiple sleep staging databases.
ADIGen: Automatic, Debiased, and Invariant Counterfactual Generation
Automatic differentiation (AD) is an essential primitive for machine learning programming systems. Tangent is a new library that performs AD using source code transformation (SCT) in Python. It takes numeric functions written in a syntactic subset of Python and NumPy as input, and generates new Python functions which c…
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…
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). …
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…
AutoInit automatically finds good neural network initialization.
Python package automates causal parameter estimation using Riesz regression.
While great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets. In this work, we take a step towards general scene-level image editing by developing an automatic interaction-free object removal model. Our model learns to …
Automatic continuity of polynomial maps and cocycles proved.
Proposes an automatic cyclical scheduling for gradient-based discrete sampling.
The theory of automatic groups is developed, including properties and practical algorithms.
Classifies surfaces for pure mapping class groups with automatic continuity.
This paper proposes automatic tuning of Bayesian Optimization's acquisition function.
AD-HOC simplifies high-order derivative calculations in C++.
Clustering analysis has become a ubiquitous information retrieval tool in a wide range of domains, but a more automatic framework is still lacking. Though internal metrics are the key players towards a successful retrieval of clusters, their effectiveness on real-world datasets remains not fully understood, mainly beca…
This paper deals with the computation of second or higher order greeks of financial securities. It combines two methods, Vibrato and automatic differentiation and compares with other methods. We show that this combined technique is faster than standard finite difference, more stable than automatic differentiation of se…
Automatic computation speeds up crosscap number calculation for alternating knots.
The rise of non-linear and interactive media such as video games has increased the need for automatic movement animation generation. In this survey, we review and analyze different aspects of building automatic movement generation systems using machine learning techniques and motion capture data. We cover topics such a…
The progress of deep convolutional neural networks has been successfully exploited in various real-time computer vision tasks such as image classification and segmentation. Owing to the development of computational units, availability of digital datasets, and improved performance of deep learning models, fully automati…
SAFE automates feature engineering for industrial tasks efficiently and scalably.
This paper works out fair values of stock loan model with automatic termination clause, cap and margin. This stock loan is treated as a generalized perpetual American option with possibly negative interest rate and some constraints. Since it helps a bank to control the risk, the banks charge less service fees compared …
A new optimisation framework for neural networks without hyperparameters.
A method makes particle filters differentiable without altering their forward pass.
In this technical report I present my method for automatic synthetic dataset generation for object detection and demonstrate it on the video game League of Legends. This report furthermore serves as a handbook on how to automatically generate datasets and as an introduction on the dataset generation part of the LeagueA…
ACL improves DRL by adapting task difficulty to agent's capabilities.
RAD estimates gradients with less memory, faster than small batch sizes.
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.
CoLA automates efficient numerical linear algebra for complex matrix structures.
A new method for automatic gradient tree boosting using information theory.
Develops a mathematical model for automatic differentiation in machine learning.
New models extrapolate false alarms in ASV without new data.
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…
SIC detects elbows in error curves automatically.
This paper introduces a spline-based method for nonparametric ADVI that handles complex posterior distributions.
MARS automatically selects tensor decomposition ranks, improving performance in neural network tasks.
Multiple generalized additive models (GAMs) are a type of distributional regression wherein parameters of probability distributions depend on predictors through smooth functions, with selection of the degree of smoothness via regularization. Multiple GAMs allow finer statistical inference by incorporating explana…
Automatically identifies RRLyrae stars from VVV survey data.
Paper proposes NNAFC for automatic financial factor construction.
In this note we derive the backward (automatic) differentiation (adjoint [automatic] differentiation) for an algorithm containing a conditional expectation operator. As an example we consider the backward algorithm as it is used in Bermudan product valuation, but the method is applicable in full generality. The method …
This work uses image generation models to find vision model bugs.