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.
Julia accelerates machine learning in various fields with balance of efficiency and simplicity.
Julia package for Gaussian processes offers fast, flexible, and scalable tools.
DynamicPPL speeds up probabilistic modeling in Julia.
This paper describes Convex, a convex optimization modeling framework in Julia. Convex translates problems from a user-friendly functional language into an abstract syntax tree describing the problem. This concise representation of the global structure of the problem allows Convex to infer whether the problem complies …
JuliaConnectoR integrates Julia functions into R for deep learning.
MLJ offers a Julia package for composing machine learning models.
Google's Cloud TPUs are a promising new hardware architecture for machine learning workloads. They have powered many of Google's milestone machine learning achievements in recent years. Google has now made TPUs available for general use on their cloud platform and as of very recently has opened them up further to allow…
Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results. To date, Celeste has been scaled to at most hundreds of megabytes of astronomical images: Bayesian posterior inference is notoriously demanding computationally. In this paper, we report on a scalable, parallel v…
Extends DCP framework to Hadamard manifolds for geodesically convex functions.
Bayesian approach infers signaling pathways from data.
We present POLO --- a C++ library for large-scale parallel optimization research that emphasizes ease-of-use, flexibility and efficiency in algorithm design. It uses multiple inheritance and template programming to decompose algorithms into essential policies and facilitate code reuse. With its clear separation between…
NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia
Efficient packages solve SLOPE problem in multiple languages.
Improved MUSE boosts performance and reduces error in Bayesian inference.
Study shows Julia sets and gasket limit sets are quasiconformally different.
The presentation of supergravity theories of our previous paper "Super-Poincare' algebras, space-times and supergravities (I)" is re-formulated in the language of Berezin-Leites-Kostant theory of supermanifolds. It is also shown that the equations of Cremmer, Julia and Scherk's theory of 11D-supergravity are equivalent…
CoinTossX is a low-latency, open-source matching engine for financial trading.
The task of translating between programming languages differs from the challenge of translating natural languages in that programming languages are designed with a far more rigid set of structural and grammatical rules. Previous work has used a tree-to-tree encoder/decoder model to take advantage of the inherent tree s…
The Basilica Julia set is universally equivalent to other complex dynamics sets.
Deployable probabilistic programming for Go and other languages.
Automates API mapping across languages with minimal manual effort.
We investigate the dynamics of semigroups generated by a family of polynomial maps on the Riemann sphere such that the postcritical set in the complex plane is bounded. The Julia set of such a semigroup may not be connected in general. We show that for such a polynomial semigroup, if and are two connected compo…
Determining the programming language of a source code file has been considered in the research community; it has been shown that Machine Learning (ML) and Natural Language Processing (NLP) algorithms can be effective in identifying the programming language of source code files. However, determining the programming lang…
Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have …
Scorio.jl ranks systems from repeated tasks using various methods.
JULIA combines multi-linear and nonlinear models for tensor completion.
This work offers a broad perspective on probabilistic modeling and inference in light of recent advances in probabilistic programming, in which models are formally expressed in Turing-complete programming languages. We consider a typical workflow and how probabilistic programming languages can help to automate this wor…
Self-taught optimizer improves code generation using language models.
This book is a graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. It is aimed at people who have an undergraduate-level understandi…
Stack Overflow is the most popular Q&A website among software developers. As a platform for knowledge sharing and acquisition, the questions posted in Stack Overflow usually contain a code snippet. Stack Overflow relies on users to properly tag the programming language of a question and it simply assumes that the progr…
SLM models code syntax as trees to generate any programming language code.
Paper introduces a new Poisson kernel for strongly pseudoconvex domains.
We extend a result regarding the Random Backward Iteration algorithm for drawing Julia sets (known to work for certain rational semigroups containing a non-Möbius element) to a class of Möbius semigroups which includes certain settings not yet been dealt with in the literature, namely, when the Julia set is not a thick…
DiffTaichi enables fast, differentiable physical simulations with shorter code.
New method for efficient probabilistic inference using masked language modeling.
We show that if is a quadratic polynomial with a fixed Cremer point and Julia set , then for any monotone map $\ph:J\to A$ from onto a locally connected continuum , is a single point.
PATOIS synthesizes code from natural language using learned code idioms.
Seq2Seq models perform well in generating If-Then programs from natural language.
TreeCaps improves code comprehension for software developers.
Hamiltonian Monte Carlo (HMC) is arguably the dominant statistical inference algorithm used in most popular "first-order differentiable" Probabilistic Programming Languages (PPLs). However, the fact that HMC uses derivative information causes complications when the target distribution is non-differentiable with respect…
Study dynamics of automorphisms on cubic surfaces and their connection to Painlevé 6.
New methodology controls synthetic data bias for neural program synthesis.
We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do "compilation of infer…
Stan is a probabilistic programming language that is popular in the statistics community, with a high-level syntax for expressing probabilistic models. Stan differs by nature from generative probabilistic programming languages like Church, Anglican, or Pyro. This paper presents a comprehensive compilation scheme to com…
Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects. However, to make practical inference possible, the language sacrifices some of its usability by adopting a block syntax, which lacks compositionality and flexible user-defined functions. Moreover, the semantic…
Neural machine translation used to convert CUDA to OpenCL.
This paper deals with both complex dynamical systems and conformal iterated function systems. We study finitely generated expanding semigroups of rational maps with overlaps on the Riemann sphere. We show that if a -parameter family of such semigroups satisfies the transversality condition, then for almost every par…
It is time-consuming and error-prone to implement inference procedures for each new probabilistic model. Probabilistic programming addresses this problem by allowing a user to specify the model and having a compiler automatically generate an inference procedure for it. For this approach to be practical, it is important…