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.
Survival analysis models research reproducibility, offering new insights.
Clarifies the scope of 'reproducibility' in AI and ML.
This chapter introduces reproducibility in machine learning for medical imaging.
NeurIPS 2019 program improves reproducibility in machine learning.
Teaches reproducible research to medical students and postgrads.
In this paper, we discuss the approaches we took and trade-offs involved in making a paper on a conceptual topic in pattern recognition research fully reproducible. We discuss our definition of reproducibility, the tools used, how the analysis was set up, show some examples of alternative analyses the code enables and …
learn2learn simplifies meta-learning research by providing a library and standardized interfaces.
A modular framework for knowledge distillation simplifies experiments and reproducibility.
FairGround offers a diverse dataset corpus for fair ML research.
Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. This requirement warrants a stricter attention to issues of reproducibility than other fields of machine …
CK simplifies ML model deployment and reproducibility with open APIs and DevOps.
Torch-Points3D simplifies 3D deep learning research and reproducibility.
Benchopt automates machine learning benchmarking across languages and hardware.
LLM-based trading systems vary in execution realism and reproducibility.
Open dataset and pipeline for realistic OPE research.
In this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. We show that the reproduction of their method is indeed feasible with some minor assumptions. We further investigate the robustness of their m…
The field of deep learning is experiencing a trend towards producing reproducible research. Nevertheless, it is still often a frustrating experience to reproduce scientific results. This is especially true in the machine learning community, where it is considered acceptable to have black boxes in your experiments. We p…
NetML provides datasets and challenges for network traffic analysis.
Fine-tuned open-source LLMs match or exceed closed-source models in social science research.
What makes a paper independently reproducible? Debates on reproducibility center around intuition or assumptions but lack empirical results. Our field focuses on releasing code, which is important, but is not sufficient for determining reproducibility. We take the first step toward a quantifiable answer by manually att…
African languages are numerous, complex and low-resourced. The datasets required for machine translation are difficult to discover, and existing research is hard to reproduce. Minimal attention has been given to machine translation for African languages so there is scant research regarding the problems that arise when …
As reinforcement learning (RL) achieves more success in solving complex tasks, more care is needed to ensure that RL research is reproducible and that algorithms herein can be compared easily and fairly with minimal bias. RL results are, however, notoriously hard to reproduce due to the algorithms' intrinsic variance, …
In this paper, we introduce ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning framework. ChainerRL implements a comprehensive set of DRL algorithms and techniques drawn from state-of-the-art research in the field. To foster reproducible research, and fo…
The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data sharing re…
Study compares 29 emulators across 60 test functions and 40 datasets.
Recent advances in neural architecture search (NAS) demand tremendous computational resources, which makes it difficult to reproduce experiments and imposes a barrier-to-entry to researchers without access to large-scale computation. We aim to ameliorate these problems by introducing NAS-Bench-101, the first public arc…
This paper identifies and addresses biases in medical imaging research.
Big data repositories from online learning platforms such as Massive Open Online Courses (MOOCs) represent an unprecedented opportunity to advance research on education at scale and impact a global population of learners. To date, such research has been hindered by poor reproducibility and a lack of replication, largel…
Study improves LLMs for PPI analysis by addressing uncertainty.
Despite the recent progress in deep reinforcement learning field (RL), and, arguably because of it, a large body of work remains to be done in reproducing and carefully comparing different RL algorithms. We present catalyst.RL, an open source framework for RL research with a focus on reproducibility and flexibility. Ma…
This study connects Gaussian processes and RKHS, bridging two machine learning communities.
Targeted Learning uses robust statistics for reproducible research.
CogDL simplifies graph deep learning experiments and benchmarks.
We present CodeReef - an open platform to share all the components necessary to enable cross-platform MLOps (MLSysOps), i.e. automating the deployment of ML models across diverse systems in the most efficient way. We also introduce the CodeReef solution - a way to package and share models as non-virtualized, portable, …
AlphaForgeBench evaluates LLMs as quantitative researchers, not trading agents, to address instability in financial decision-making.
Algorithm generates realistic metaorders from public trade data.
Generating molecules with desired chemical properties is important for drug discovery. The use of generative neural networks is promising for this task. However, from visual inspection, it often appears that generated samples lack diversity. In this paper, we quantify this internal chemical diversity, and we raise the …
This paper presents a large Bitcoin transaction graph dataset for research.
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…
We tackle the problem of protein secondary structure prediction using a common task framework. This lead to the introduction of multiple ideas for neural architectures based on state of the art building blocks, used in this task for the first time. We take a principled machine learning approach, which provides genuine,…
FedML aims to improve FL research by providing a library and benchmark.
This study assesses the reproducibility of 1H-MRS scans across different vendors and sessions.
PettingZoo library accelerates multi-agent reinforcement learning research.
New framework promotes reproducible, domain-agnostic reinforcement learning algorithms.
New benchmarks provide full training data for NAS research.
The paper advocates for interpretable, accountable, reproducible machine learning in medicine.
This dissertation tackles challenges in reliable machine learning measurement.
Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large traini…