github.com/learn-co-curriculum/phase-1-adding-behavior-with-methods
learn-co-curriculum/phase-1-adding-behavior-with-methods
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arxiv.org/abs/1805.06439v1
Prediction Rule Reshaping
Two methods are proposed for high-dimensional shape-constrained regression and classification. These methods reshape pre-trained prediction rules to satisfy shape constraints like monotonicity and convexity. The first method can be applied to any pre...
arxiv.org/abs/2211.08081v1
Autonomous Golf Putting with Data-Driven and Physics-Based Methods
We are developing a self-learning mechatronic golf robot using combined data-driven and physics-based methods, to have the robot autonomously learn to putt the ball from an arbitrary point on the green. Apart from the mechatronic control design of th...
arxiv.org/abs/1906.03359v1
Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional…
Medical image analysis using supervised deep learning methods remains problematic because of the reliance of deep learning methods on large amounts of labelled training data. Although medical imaging data repositories continue to expand there has not...
arxiv.org/abs/2508.10782v1
Dimension-Free Bounds for Generalized First-Order Methods via Gaussian Coupling
We establish non-asymptotic bounds on the finite-sample behavior of generalized first-order iterative algorithms -- including gradient-based optimization methods and approximate message passing (AMP) -- with Gaussian data matrices and full-memory, no...
arxiv.org/abs/2502.02021v2
Multi-illuminant Color Constancy via Multi-scale Illuminant Estimation and Fusio…
Multi-illuminant color constancy methods aim to eliminate local color casts within an image through pixel-wise illuminant estimation. Existing methods mainly employ deep learning to establish a direct mapping between an image and its illumination map...
www.bing.com/ck/a?!&&p=57295e5d0f2cebe5f0b5e0274f78290f92a805e0c0e45dc951c1e799e8e2667bJmltdHM9MTc3MjQ5NjAwMA&ptn=3&ver=2&hsh=4&fclid=397d2902-bb80-69f4-0c6e-3e10ba1e687e&u=a1aHR0cHM6Ly9zdGFja292ZXJmbG93LmNvbS9xdWVzdGlvbnMvNjI1MDgzL3doYXQtZG8taW5pdC1hbmQtc2VsZi1kby1pbi1weXRob24&ntb=1
oop - What do __init__ and self do in Python? - Stack Overflow
Jul 8, 2017 · In this case, there are some benefits to allowing this: 1) Methods are just functions that happen defined in a class, and need to be callable either as bound methods with implicit self passing â¦
arxiv.org/abs/2102.02019v2
High-order simulations of isothermal flows using the local anisotropic basis fun…
Mesh-free methods have significant potential for simulations of flows in complex geometries, with the difficulties of domain discretisation greatly reduced. However, many mesh-free methods are limited to low order accuracy. In order to compete with c...
arxiv.org/abs/2307.08308v1
A Novel Multi-Task Model Imitating Dermatologists for Accurate Differential Diag…
Skin diseases are among the most prevalent health issues, and accurate computer-aided diagnosis methods are of importance for both dermatologists and patients. However, most of the existing methods overlook the essential domain knowledge required for...
arxiv.org/abs/2102.00818v1
Proceedings of the DATE Friday Workshop on System-level Design Methods for Deep…
This volume contains the papers accepted at the first DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous Architectures (SLOHA 2021), held virtually on February 5, 2021. SLOHA 2021 was co-located with the Conference...
arxiv.org/abs/1502.07241v2
Proceedings of the DATE Friday Workshop on Heterogeneous Architectures and Desig…
This volume contains the papers accepted at the DATE Friday Workshop on Heterogeneous Architectures and Design Methods for Embedded Image Systems (HIS 2015), held in Grenoble, France, March 13, 2015. HIS 2015 was co-located with the Conference on Des...
arxiv.org/abs/1503.06058v3
Sequential Monte Carlo Methods for System Identification
One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo (SMC) methods, such as the particle filter (introduced more than two d...
arxiv.org/abs/1806.00804v2
NAM: Non-Adversarial Unsupervised Domain Mapping
Several methods were recently proposed for the task of translating images between domains without prior knowledge in the form of correspondences. The existing methods apply adversarial learning to ensure that the distribution of the mapped source dom...
arxiv.org/abs/2509.21022v1
Actor-Critic without Actor
Actor-critic methods constitute a central paradigm in reinforcement learning (RL), coupling policy evaluation with policy improvement. While effective across many domains, these methods rely on separate actor and critic networks, which makes training...
arxiv.org/abs/2305.13504v1
Neural Machine Translation for Code Generation
Neural machine translation (NMT) methods developed for natural language processing have been shown to be highly successful in automating translation from one natural language to another. Recently, these NMT methods have been adapted to the generation...
arxiv.org/abs/2602.03673v1
Referring Industrial Anomaly Segmentation
Industrial Anomaly Detection (IAD) is vital for manufacturing, yet traditional methods face significant challenges: unsupervised approaches yield rough localizations requiring manual thresholds, while supervised methods overfit due to scarce, imbalan...
arxiv.org/abs/2008.02546v1
UBER-GNN: A User-Based Embeddings Recommendation based on Graph Neural Networks
The problem of session-based recommendation aims to predict user next actions based on session histories. Previous methods models session histories into sequences and estimate user latent features by RNN and GNN methods to make recommendations. Howev...
arxiv.org/abs/2312.16457v2
City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web
Existing neural radiance field-based methods can achieve real-time rendering of small scenes on the web platform. However, extending these methods to large-scale scenes still poses significant challenges due to limited resources in computation, memor...
arxiv.org/abs/2207.14650v3
SYNTA: A novel approach for deep learning-based image analysis in muscle histopa…
Artificial intelligence (AI), machine learning, and deep learning (DL) methods are becoming increasingly important in the field of biomedical image analysis. However, to exploit the full potential of such methods, a representative number of experimen...
arxiv.org/abs/1809.01566v1
Divisor package for Macaulay2
This note describes a Macaulay2 package for handling divisors. Group operations for divisors are included. There are methods for converting divisors to reflexive or invertible sheaves. Additionally, there are methods for checking whether divisors are...