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.
This paper tackles co-design of neural hardware and software to improve efficiency.
The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and …
Paper proposes a faster method for evaluating DNN hardware and software designs.
TomOpt optimizes muon detector designs using differentiable programming.
This research categorizes AMM designs for secure token exchanges.
A new method for verifying deep learning architectures on FPGAs is proposed.
This work presents MeKDDaM-SAGA, computer-aided automation software for implementing a novel knowledge discovery and data mining process model that was designed for performing justifiable, traceable and reproducible metabolomics data analysis. The process model focuses on achieving metabolomics analytical objectives an…
PARyOpt is a python based implementation of the Bayesian optimization routine designed for remote and asynchronous function evaluations. Bayesian optimization is especially attractive for computational optimization due to its low cost function footprint as well as the ability to account for uncertainties in data. A key…
Software estimates inequality in random systems with changing communities.
Optical ESNs enable flexible, efficient machine learning with reduced energy.
Developing active inference agents for edge devices with limited resources.
Co-designing efficient machine learning based systems across the whole hardware/software stack to trade off speed, accuracy, energy and costs is becoming extremely complex and time consuming. Researchers often struggle to evaluate and compare different published works across rapidly evolving software frameworks, hetero…
PyTorch Geometric Signed Directed fills the gap for GNNs on signed and directed graphs.
Models of complex systems are often formalized as sequential software simulators: computationally intensive programs that iteratively build up probable system configurations given parameters and initial conditions. These simulators enable modelers to capture effects that are difficult to characterize analytically or su…
Virtual reality explores non-Euclidean Sol geometry.
The use of machine learning (ML) is on the rise in many sectors of software development, and automotive software development is no different. In particular, Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are two areas where ML plays a significant role. In automotive development, safety is…
We describe GTApprox - a new tool for medium-scale surrogate modeling in industrial design. Compared to existing software, GTApprox brings several innovations: a few novel approximation algorithms, several advanced methods of automated model selection, novel options in the form of hints. We demonstrate the efficiency o…
In financial field, a robust software system is of vital importance to ensure the smooth operation of financial transactions. However, many financial corporations still depend on operators to identify and eliminate the system failures when financial software systems break down. This traditional operation method is time…
Reinforcement learning frameworks have introduced abstractions to implement and execute algorithms at scale. They assume standardized simulator interfaces but are not concerned with identifying suitable task representations. We present Wield, a first-of-its kind system to facilitate task design for practical reinforcem…
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But ML training presents three unique benchmarking challenges absent from other domains: optimizations that improve training throughput can incre…
Recently software development companies started to embrace Machine Learning (ML) techniques for introducing a series of advanced functionality in their products such as personalisation of the user experience, improved search, content recommendation and automation. The technical challenges for tackling these problems ar…
SLM Lab is a framework for reproducible RL research with modular algorithms.
TinyCNN accelerates CNN models on embedded FPGA with 15x speedup.
BoFire optimizes chemistry experiments using Bayesian Optimization.
fmeffects package interprets non-linear models in plain language.
Accelerates data loading in deep neural network training by 30x.
We introduce the simulation tool SABCEMM (Simulator for Agent-Based Computational Economic Market Models) for agent-based computational economic market (ABCEM) models. Our simulation tool is implemented in C++ and we can easily run ABCEM models with several million agents. The object-oriented software design enables th…
A Semi-Hidden Markov Model (SHMM) for bursty error channels is defined by a state transition probability matrix , a prior probability vector , and the state dependent output symbol error probability matrix . Several processes are utilized for estimating , and from a given empirically obtained or sim…
We propose design guidelines for a probabilistic programming facility suitable for deployment as a part of a production software system. As a reference implementation, we introduce Infergo, a probabilistic programming facility for Go, a modern programming language of choice for server-side software development. We argu…
Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility. Changes in algorithms, models, operators, or numerical systems threaten the viability of specialized hardware…
VALAN is a framework for navigation agents in photo-realistic environments.
Industry lacks tools to secure ML systems, study finds.
XLA compiler extension improves memory efficiency for machine learning.
DRIFT uses RL to automate functional software testing efficiently.
Multimodal deep learning improves flaw detection in software programs.
Adaptive RL optimizes testing resource allocation for dynamic software environments.
VegasFlow accelerates complex simulations across various hardware platforms.
CK simplifies ML model deployment and reproducibility with open APIs and DevOps.
KnotPlot helps beginners and veterans use software for visualizing knots.
Research proposes an ensemble learning model for efficient software defect prediction.
Novel framework for ML-assisted inference valid for any statistical task.
d3p package enables efficient Bayesian inference with differential privacy.
Improving software quality through effective organizational learning.
ModSSC unifies semi-supervised classification for various data types.
Flexible VHDL design for multiple neural networks on FPGAs.
New GPU algorithm speeds up Gaussian Process analysis.
Improved software flaw detection using NAS on multimodal DL models.
This paper highlights new opportunities for designing large-scale machine learning systems as a consequence of blurring traditional boundaries that have allowed algorithm designers and application-level practitioners to stay -- for the most part -- oblivious to the details of the underlying hardware-level implementatio…