🤖 Category

cs.LG

23609 papers

The spectrum from Cesium to Lutetium encompasses a fascinating group of elements that bridge the gap between the reactive alkali metals and the complex chemistry of the transition series. These atoms, sitting in the middle of the periodic table, drive innovations in atomic clocks, quantum computing, and advanced medical imaging. Their unique electron configurations allow scientists to probe the fundamental laws of physics with extraordinary precision, offering a window into how matter behaves under extreme conditions.

Gist.Science is dedicated to making the latest discoveries in this niche accessible to everyone. We process every new preprint in this category from arXiv as soon as it appears, translating dense academic findings into clear, plain-language summaries alongside detailed technical breakdowns. This dual approach ensures that both curious readers and specialists can grasp the significance of these rapid scientific advances without getting lost in the jargon.

Below are the latest papers covering these heavy elements and their groundbreaking applications.

🤖 machine learning

Optimizing Three Critical Factors for Practical and Effective OOD Detection Fine-Tuning

This paper proposes a practical and effective OOD detection framework that optimizes model reminder, data sampling, and representation learning through Self-Knowledge Distillation, Semi-hard Outlier Sampling, and Outlier-aware Supervised Contrastive Learning to simultaneously improve detection performance and classification accuracy while outperforming existing methods across diverse benchmarks.

Hyunjun Choi, JaeHo Chung, Hawook Jeong2026-09-11
🤖 machine learning

Explainable few-shot learning workflow for detecting invasive and exotic tree species

This paper proposes an explainable few-shot learning workflow that integrates a Siamese network with XAI to effectively detect invasive and exotic tree species in Brazil's Atlantic Forest using UAV images, achieving high accuracy with minimal labeled data while providing transparent visual explanations for its predictions.

Caroline M. Gevaert, Alexandra Aguiar Pedro, Ou Ku, Hao Cheng, Pranav Chandramouli, Farzaneh Dadrass Javan, Francesco Na (…)2026-09-11
🤖 machine learning

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations

The paper introduces SG-Blend, a per-layer adaptive activation function that learns an optimal interpolation between a novel bias-corrected Swish variant (SSwish) and GELU, demonstrating reduced training variance and superior performance across natural language processing and computer vision tasks compared to standard fixed activations.

Gaurav Sarkar, Syed Affan Daimi, Jay Gala, Subarna Tripathi2026-09-11
🤖 machine learning

Configuration-Dependent Lower Bounds for Approximation by Shallow ReLUk^k Networks on the Sphere

This paper establishes configuration-dependent lower bounds for shallow ReLUk^k networks on the sphere, demonstrating that while these networks can outperform finite elements, their approximation accuracy for smooth functions is intrinsically limited by a saturation order determined by the network's parameter configuration and the target function's regularity.

Tong Mao, Jinchao Xu2026-09-11
📊 statistics

Statistical analysis of Inverse Entropy-regularized Reinforcement Learning

This paper presents a statistical framework for Inverse Entropy-regularized Reinforcement Learning that resolves the non-uniqueness of reward recovery in classical IRL by combining entropy regularization with least-squares reconstruction, thereby establishing non-asymptotic minimax optimal convergence rates for the estimated reward function and bridging behavior cloning with modern statistical learning theory.

Denis Belomestny, Alexey Naumov, Artemy Rubtsov, Sergey Samsonov2026-09-11