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.
New tool improves scalability of data-driven invariant inference.
Kernel matrices (e.g. Gram or similarity matrices) are essential for many state-of-the-art approaches to classification, clustering, and dimensionality reduction. For large datasets, the cost of forming and factoring such kernel matrices becomes intractable. To address this challenge, we introduce a new adaptive sampli…
Entity resolution (ER) presents unique challenges for evaluation methodology. While crowdsourcing platforms acquire ground truth, sound approaches to sampling must drive labelling efforts. In ER, extreme class imbalance between matching and non-matching records can lead to enormous labelling requirements when seeking s…
In this paper we develop the theory of parametric polynomial regression in Riemannian manifolds and Lie groups. We show application of Riemannian polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Book…
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of AD. However, they are difficult to reproduce because key components of the validation are often not readily available. These components include selected participants and input data, image prepr…
DiffeoCFM efficiently generates realistic brain connectivity matrices using pullback metrics.
Enhances classification accuracy on low data sets using synthetic data.
Recent years have witnessed the emergence and increasing popularity of 3D medical imaging techniques with the development of 3D sensors and technology. However, achieving geometric invariance in the processing of 3D medical images is computationally expensive but nonetheless essential due to the presence of possible er…
Skull stripping is usually the first step for most brain analysisprocess in magnetic resonance images. A lot of deep learn-ing neural network based methods have been developed toachieve higher accuracy. Since the 3D deep learning modelssuffer from high computational cost and are subject to GPUmemory limit challenge, a …
This paper proposes a principled information theoretic analysis of classification for deep neural network structures, e.g. convolutional neural networks (CNN). The output of convolutional filters is modeled as a random variable Y conditioned on the object class C and network filter bank F. The conditional entropy (CENT…
LATM framework uses LLMs to create and reuse tools for efficient problem-solving.
UniFeat is an open-source Java tool for feature selection.
New findings show tool-augmented models can recall unlimited facts, outperforming purely memorized models.
We propose a tool-use model that can detect the features of tools, target objects, and actions from the provided effects of object manipulation. We construct a model that enables robots to manipulate objects with tools, using infant learning as a concept. To realize this, we train sensory-motor data recorded during a t…
A study ranks critical Lean Six Sigma tools for implementation in Portuguese companies.
Interactive tool helps RL researchers debug and understand their models.
Survey of tools for studying hierarchical hyperbolic spaces.
Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and thus properly grasping and manipulating the tool to achieve the task. Task-agnostic grasping optimizes for grasp robustness while ignoring crucial task-specific constrain…
ToolChain-CRC addresses the risk-control problem for retrieval-augmented and tool-using agents under drift.
New methods improve tool-to-tool matching in semiconductor manufacturing.
Survey of software developers' experience with Github Copilot tool.
In manufacture, steel and other metals are mainly cut and shaped during the fabrication process by computer numerical control (CNC) machines. To keep high productivity and efficiency of the fabrication process, engineers need to monitor the real-time process of CNC machines, and the lifetime management of machine tools…
TIR expands LLM capabilities by enabling problem-solving strategies.
New L0 norm added to TDA for market analysis.
Interview study reveals considerations for designing semi-automated bias detection tools.
We examined the use of three conventional anomaly detection methods and assess their potential for on-line tool wear monitoring. Through efficient data processing and transformation of the algorithm proposed here, in a real-time environment, these methods were tested for fast evaluation of cutting tools on CNC machines…
Develops tools to audit ML models for bias and unfairness.
FiNCAT tool automatically identifies financial numerals in documents.
Third part of a study on liquidity risk in asset management, focusing on managing the asset-liability liquidity risk.
Data science teams collaborate extensively, using various tools and stakeholders.
Teaching tool simplifies Monte Carlo simulation for project risk analysis.
Digital tools may hinder or facilitate multidisciplinary collaboration in occupational health.
A key challenge in developing and deploying Machine Learning (ML) systems is understanding their performance across a wide range of inputs. To address this challenge, we created the What-If Tool, an open-source application that allows practitioners to probe, visualize, and analyze ML systems, with minimal coding. The W…
One of the open challenges in designing robots that operate successfully in the unpredictable human environment is how to make them able to predict what actions they can perform on objects, and what their effects will be, i.e., the ability to perceive object affordances. Since modeling all the possible world interactio…
Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. RNA-seq based transcriptome sequencing has been extensively used for identification of lncRNAs. However, accurate identification of lncRNAs in RNA-seq datasets is crucial for exploring their char…
Python tools for 3D shape analysis on Kendall's space.
In this paper we explore the richness of information captured by the latent space of a vision-based generative model. The model combines unsupervised generative learning with a task-based performance predictor to learn and to exploit task-relevant object affordances given visual observations from a reaching task, invol…
Deep neural nets classify tool wear in blanking processes.
Probabilistic models can handle causal inference without special tools.
Gaussian processes (GP) are powerful tools for probabilistic modeling purposes. They can be used to define prior distributions over latent functions in hierarchical Bayesian models. The prior over functions is defined implicitly by the mean and covariance function, which determine the smoothness and variability of the …
Homological stability aids in computing group homology.
SafePILCO is a Python tool for safe reinforcement learning.
New tools explain FRF model predictions in high-dimensional ECG data.
Intro to Poisson geometry, focusing on basics and recent tools.
The paper examines how macroeconomic control tools lost effectiveness, leading to a 'dark ages' period.
Develops a tool to identify abnormal blood smear results based on CBC tests.
We highlight a very simple statistical tool for the analysis of financial bubbles, which has already been studied in [1]. We provide extensive empirical tests of this statistical tool and investigate analytically its link with stocks correlation structure.
In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep…