Struggle for Land Regularization at the Federal University of Ceará, Brazil
Struggle for Land Regularization at the Federal University of Ceará, Brazil
www.anarchistfederation.net
November 8, 2026 at 5:59 PM
Struggle for Land Regularization at the Federal University of Ceará, Brazil
Broad regularization
November 8, 2026 at 9:00 PM
Broad regularization
Migrant Regularization as Climate Adaptation: Marc Riudavets-Barcons
NEP/RePEc link
to paper
d.repec.org
November 10, 2026 at 2:59 AM
Migrant Regularization as Climate Adaptation: Marc Riudavets-Barcons
How do we understand neural nets with so many interacting params that we can’t describe what they do in words?
Our take: objective functions and regularizers offer a concise description of "what models learn"
Can we *empirically* study implicit regularization in DL?🧵
Our take: objective functions and regularizers offer a concise description of "what models learn"
Can we *empirically* study implicit regularization in DL?🧵
November 5, 2026 at 9:08 PM
How do we understand neural nets with so many interacting params that we can’t describe what they do in words?
Our take: objective functions and regularizers offer a concise description of "what models learn"
Can we *empirically* study implicit regularization in DL?🧵
Our take: objective functions and regularizers offer a concise description of "what models learn"
Can we *empirically* study implicit regularization in DL?🧵
Massimo Gobbino, Filippo Quattrocchi: On the elliptic regularization of maximal slope curves https://hal.science/hal-05854668v1 [math.math-ap]
November 9, 2026 at 3:00 AM
Massimo Gobbino, Filippo Quattrocchi: On the elliptic regularization of maximal slope curves https://hal.science/hal-05854668v1 [math.math-ap]
Alexandre Perrin, J\'er\'emie Messud, S\'ebastien Burdin, Matthieu Sauban\`ere
Exploring Variational Regularization of Krylov Subspaces
https://arxiv.org/abs/2611.09229
Exploring Variational Regularization of Krylov Subspaces
https://arxiv.org/abs/2611.09229
November 10, 2026 at 10:01 PM
Alexandre Perrin, J\'er\'emie Messud, S\'ebastien Burdin, Matthieu Sauban\`ere
Exploring Variational Regularization of Krylov Subspaces
https://arxiv.org/abs/2611.09229
Exploring Variational Regularization of Krylov Subspaces
https://arxiv.org/abs/2611.09229
Ernie has a few interesting tokenization tidbits.
- they use BPE-Dropout (the only frontier model known to use subword regularization)
- they use UTF-16 (all others use UTF8, UTF16 has some better encoding properties for a subset of CJK)
- they use R2L groups of 3 in digit pretokenization
- they use BPE-Dropout (the only frontier model known to use subword regularization)
- they use UTF-16 (all others use UTF8, UTF16 has some better encoding properties for a subset of CJK)
- they use R2L groups of 3 in digit pretokenization
November 6, 2026 at 6:36 AM
Ernie has a few interesting tokenization tidbits.
- they use BPE-Dropout (the only frontier model known to use subword regularization)
- they use UTF-16 (all others use UTF8, UTF16 has some better encoding properties for a subset of CJK)
- they use R2L groups of 3 in digit pretokenization
- they use BPE-Dropout (the only frontier model known to use subword regularization)
- they use UTF-16 (all others use UTF8, UTF16 has some better encoding properties for a subset of CJK)
- they use R2L groups of 3 in digit pretokenization
Exploring Variational Regularization of Krylov Subspaces
https://arxiv.org/pdf/2611.09229
Alexandre Perrin, Jérémie Messud, Sébastien Burdin, Matthieu Saubanère.
https://arxiv.org/pdf/2611.09229
Alexandre Perrin, Jérémie Messud, Sébastien Burdin, Matthieu Saubanère.
https://arxiv.org/abs/2611.09229
arXiv abstract link
arxiv.org
November 9, 2026 at 9:30 PM
Exploring Variational Regularization of Krylov Subspaces
https://arxiv.org/pdf/2611.09229
Alexandre Perrin, Jérémie Messud, Sébastien Burdin, Matthieu Saubanère.
https://arxiv.org/pdf/2611.09229
Alexandre Perrin, Jérémie Messud, Sébastien Burdin, Matthieu Saubanère.
GENIE framework treats hardware noise as a tunable regularizer for quantum generative models. Adaptive selection during training achieves 0.3% median regret vs 8.4-22.7% for fixed noise levels, with rigorous generalization bounds proving regularization effects.
#QuantumML #ErrorMitigation #Research
#QuantumML #ErrorMitigation #Research
Residual Noise as Adaptive Regularization in Quantum Circuit Born Machines
arxiv.org
November 5, 2026 at 1:24 PM
GENIE framework treats hardware noise as a tunable regularizer for quantum generative models. Adaptive selection during training achieves 0.3% median regret vs 8.4-22.7% for fixed noise levels, with rigorous generalization bounds proving regularization effects.
#QuantumML #ErrorMitigation #Research
#QuantumML #ErrorMitigation #Research
Malena Espa\~nol, Markus Juvonen, Samuli Siltanen, Ilaria Trombini, Chuyang Wu
A Structured-Surrogate Double-Model Approach to Regularization Parameter Selection
https://arxiv.org/abs/2611.09158
A Structured-Surrogate Double-Model Approach to Regularization Parameter Selection
https://arxiv.org/abs/2611.09158
November 9, 2026 at 4:36 PM
Malena Espa\~nol, Markus Juvonen, Samuli Siltanen, Ilaria Trombini, Chuyang Wu
A Structured-Surrogate Double-Model Approach to Regularization Parameter Selection
https://arxiv.org/abs/2611.09158
A Structured-Surrogate Double-Model Approach to Regularization Parameter Selection
https://arxiv.org/abs/2611.09158
Implicit Regularization via Spectral Neural Networks and Non-linear Matrix Sensing
https://arxiv.org/abs/2402.17595
https://arxiv.org/abs/2402.17595
November 7, 2026 at 2:07 AM
Implicit Regularization via Spectral Neural Networks and Non-linear Matrix Sensing
https://arxiv.org/abs/2402.17595
https://arxiv.org/abs/2402.17595
We first sanity check this by adding explicit elastic net regularization to small neural networks, and verify that we can accurately recover the ground truth regularization parameters
November 5, 2026 at 9:08 PM
We first sanity check this by adding explicit elastic net regularization to small neural networks, and verify that we can accurately recover the ground truth regularization parameters
Interview prep questions from recent sessions: cross-validation, L1 vs L2 regularization, choosing prompting/RAG/fine-tuning, why FlashAttention speeds up inference, and the lost-in-the-middle problem in LLMs.
November 9, 2026 at 1:42 PM
Interview prep questions from recent sessions: cross-validation, L1 vs L2 regularization, choosing prompting/RAG/fine-tuning, why FlashAttention speeds up inference, and the lost-in-the-middle problem in LLMs.
MANILA, Philippines — The Land Transportation Franchising and Regulatory Board (LTFRB) has extended the application period for the regularization of ride-hailing partners operating under temporary or “tempo” status.
LTFRB extends application period for TNVS regularization
www.manilatimes.net
November 6, 2026 at 5:23 AM
MANILA, Philippines — The Land Transportation Franchising and Regulatory Board (LTFRB) has extended the application period for the regularization of ride-hailing partners operating under temporary or “tempo” status.
Malena Espa\~nol, Markus Juvonen, Samuli Siltanen, Ilaria Trombini, Chuyang Wu: A Structured-Surrogate Double-Model Approach to Regularization Parameter Selection https://arxiv.org/abs/2611.09158 https://arxiv.org/pdf/2611.09158 https://arxiv.org/html/2611.09158
November 9, 2026 at 6:49 AM
Malena Espa\~nol, Markus Juvonen, Samuli Siltanen, Ilaria Trombini, Chuyang Wu: A Structured-Surrogate Double-Model Approach to Regularization Parameter Selection https://arxiv.org/abs/2611.09158 https://arxiv.org/pdf/2611.09158 https://arxiv.org/html/2611.09158
Alexandre Perrin, J\'er\'emie Messud, S\'ebastien Burdin, Matthieu Sauban\`ere: Exploring Variational Regularization of Krylov Subspaces https://arxiv.org/abs/2611.09229 https://arxiv.org/pdf/2611.09229 https://arxiv.org/html/2611.09229
November 9, 2026 at 6:59 AM
Alexandre Perrin, J\'er\'emie Messud, S\'ebastien Burdin, Matthieu Sauban\`ere: Exploring Variational Regularization of Krylov Subspaces https://arxiv.org/abs/2611.09229 https://arxiv.org/pdf/2611.09229 https://arxiv.org/html/2611.09229
STEK: Structural-Terrain-Enhanced Koopman-Inspired Cross-Channel Regularization for Retrospective Battery Multi-Target Estimation
Jing Wang, Shiyuan Piao, Fugee Tsung
#arXiv #cs.AI
Jing Wang, Shiyuan Piao, Fugee Tsung
#arXiv #cs.AI
STEK: Structural-Terrain-Enhanced Koopman-Inspired Cross-Channel Regularization for Retrospective Battery Multi-Target Estimation
Retrospective battery characterization across heterogeneous cells is challenging because cycling records vary substantially with chemistry, protocol, cell format, capacity, and operating conditions. An endpoint charge--discharge curve captures the local condition of an evaluated cycle, while a cell…
arxiv.org
November 6, 2026 at 1:36 AM
With a little guidance, Claude can turn a stats paper into a webapp. I tried this one bc it showed up as prior art for another project.
paper: "Nonparametric Regression, Confidence Regions and Regularization" Davies, Kovac & Meise arxiv.org/abs/0711.0690
webapp: xangregg.github.io/tautfit/
paper: "Nonparametric Regression, Confidence Regions and Regularization" Davies, Kovac & Meise arxiv.org/abs/0711.0690
webapp: xangregg.github.io/tautfit/
November 8, 2026 at 4:21 PM
With a little guidance, Claude can turn a stats paper into a webapp. I tried this one bc it showed up as prior art for another project.
paper: "Nonparametric Regression, Confidence Regions and Regularization" Davies, Kovac & Meise arxiv.org/abs/0711.0690
webapp: xangregg.github.io/tautfit/
paper: "Nonparametric Regression, Confidence Regions and Regularization" Davies, Kovac & Meise arxiv.org/abs/0711.0690
webapp: xangregg.github.io/tautfit/
Won-Ki Seo, Jiazhen Xu, Han Lin Shang: Local Whittle estimation for distributional time series: A regularization framework in Bayes Hilbert space https://arxiv.org/abs/2611.05765 https://arxiv.org/pdf/2611.05765 https://arxiv.org/html/2611.05765
November 5, 2026 at 7:02 AM
Won-Ki Seo, Jiazhen Xu, Han Lin Shang: Local Whittle estimation for distributional time series: A regularization framework in Bayes Hilbert space https://arxiv.org/abs/2611.05765 https://arxiv.org/pdf/2611.05765 https://arxiv.org/html/2611.05765
LTFRB grants 30-day extension for TNVS regularization applications
🤖 IA: It's not clickbait ✅
👥 Users: It's not clickbait ✅
#ltfrb #tnvs #permits
👇👇👇
🤖 IA: It's not clickbait ✅
👥 Users: It's not clickbait ✅
#ltfrb #tnvs #permits
👇👇👇
LTFRB grants 30-day extension for TNVS regularization applications
The Land Transportation Franchising and Regulatory Board (LTFRB) has extended the application deadline for the regularization of ride-hailing service providers operating under temporary or 'tempo' status by an additional 30 days. The original 30-working-day window for online applications began on October 8, 2026, but has now been extended. This extension, implemented at the directive of Transportation Secretary Vince Dizon, aims to ensure that no TNVS (Transport Network Vehicle Services) operators are left behind in the public transportation sector. LTFRB Acting Chairman Greg Pua Jr. emphasized that the regularization process is designed to assist thousands of TNVS drivers who, in good faith, onboarded with transport network companies (TNCs) and made substantial financial commitments. The LTFRB clarified that 'tempo' status refers to the practice where TNCs onboard TNVS units on their digital platforms without obtaining the necessary authorization from the LTFRB. Operating under tempo status violates LTFRB regulations as it allows unlicensed services to operate. According to the agency, 13,216 applications have been received, with 5,091 already granted provisional authority. Pua urged applicants to submit all required documents to avoid processing delays and advised those facing difficulties to contact the agency for assistance. The extension aims to streamline the regularization process and ensure compliance with transportation regulations while supporting the TNVS community.
en.killbait.com
November 6, 2026 at 11:43 PM
Massimo Gobbino, Filippo Quattrocchi: On the elliptic regularization of maximal slope curves https://arxiv.org/abs/2611.08377 https://arxiv.org/pdf/2611.08377 https://arxiv.org/html/2611.08377
November 6, 2026 at 6:47 AM
Massimo Gobbino, Filippo Quattrocchi: On the elliptic regularization of maximal slope curves https://arxiv.org/abs/2611.08377 https://arxiv.org/pdf/2611.08377 https://arxiv.org/html/2611.08377
We introduce a framework for estimating effective regularizers from trained models and their loss gradients—to help explain how training choices shape what models learn.
Blog post: jrudoler.com/estimating-i...
Paper: arxiv.org/abs/2605.05436
Blog post: jrudoler.com/estimating-i...
Paper: arxiv.org/abs/2605.05436
Estimating implicit regularization in deep learning
Gradient matching estimates regularization parameters and tests whether an explicit penalty reproduces learned weights.
jrudoler.com
November 5, 2026 at 9:08 PM
We introduce a framework for estimating effective regularizers from trained models and their loss gradients—to help explain how training choices shape what models learn.
Blog post: jrudoler.com/estimating-i...
Paper: arxiv.org/abs/2605.05436
Blog post: jrudoler.com/estimating-i...
Paper: arxiv.org/abs/2605.05436
📰 "Spain | Regularization helped increase affiliations and unemployment in October." - BBVA Publications
https://www.bbvaresearch.com/en/publicaciones/spain-regularization-helped-increase-affiliations-and-unemployment-in-october/
https://www.bbvaresearch.com/en/publicaciones/spain-regularization-helped-increase-affiliations-and-unemployment-in-october/
November 4, 2026 at 12:02 PM
📰 "Spain | Regularization helped increase affiliations and unemployment in October." - BBVA Publications
https://www.bbvaresearch.com/en/publicaciones/spain-regularization-helped-increase-affiliations-and-unemployment-in-october/
https://www.bbvaresearch.com/en/publicaciones/spain-regularization-helped-increase-affiliations-and-unemployment-in-october/
New on CRAN: pulsar (0.3.14). View at https://CRAN.R-project.org/package=pulsar
pulsar: Parallel Utilities for Lambda Selection along a Regularization Path
Model selection for penalized graphical models using the Stability Approach to Regularization Selection ('StARS'), with options for speed-ups including Bounded StARS (B-StARS), batch computing, and other stability metrics (e.g., graphlet stability G-StARS). Christian L. Müller, Richard Bonneau, Zachary Kurtz (2016) <<a href="https://doi.org/10.48550%2FarXiv.1605.07072" target="_top">doi:10.48550/arXiv.1605.07072</a>>.
CRAN.R-project.org
November 6, 2026 at 10:45 PM
New on CRAN: pulsar (0.3.14). View at https://CRAN.R-project.org/package=pulsar
On nonlinear ODEs, VM reaches lower trajectory error than teacher forcing (TF) and rollouts at the training cost of TF (w/ explicit Euler), and velocity and Jacobian errors drop accordingly (this comes from an implicit regularization effect, explained below).
November 7, 2026 at 5:37 PM
On nonlinear ODEs, VM reaches lower trajectory error than teacher forcing (TF) and rollouts at the training cost of TF (w/ explicit Euler), and velocity and Jacobian errors drop accordingly (this comes from an implicit regularization effect, explained below).