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White-collar workers are quietly rebelling against AI as 80% outright refuse adoption mandates

submitted by /u/Effective-Trick-5795 [link] [comments]

Reddit - Artificial Intelligence · 1 min ·
Llms

[R] Forced Depth Consideration Reduces Type II Errors in LLM Self-Classification: Evidence from an Exploration Prompting Ablation Study - (200 trap prompts, 4 models, 8 Step-0 variants) [R]

LLM-Based task classifier tend to misroute prompts that look simple at first glance, but require deeper understanding - I call it "Type I...

Reddit - Machine Learning · 1 min ·
Machine Learning

Anyone have an S3-compatible store that actually saturates H100s without the AWS egress tax? [R]

We’re training on a cluster in Lambda Labs, but our main dataset ( over 40TB) is sitting in AWS S3. The egress fees are high, so we tried...

Reddit - Machine Learning · 1 min ·

All Content

[2602.15595] Multi-Objective Coverage via Constraint Active Search
Nlp

[2602.15595] Multi-Objective Coverage via Constraint Active Search

This paper introduces a novel algorithm, MOC-CAS, for solving the multi-objective coverage problem, enhancing efficiency in applications ...

arXiv - Machine Learning · 4 min ·
[2602.15586] Uniform error bounds for quantized dynamical models
Machine Learning

[2602.15586] Uniform error bounds for quantized dynamical models

This paper presents uniform error bounds for quantized dynamical models, providing statistical guarantees on their accuracy when learned ...

arXiv - Machine Learning · 3 min ·
[2602.15546] CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals
Machine Learning

[2602.15546] CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals

The paper introduces CEPAE, a novel approach utilizing Conditional Entropy-Penalized Autoencoders for effective counterfactual inference ...

arXiv - Machine Learning · 3 min ·
[2602.15510] On the Geometric Coherence of Global Aggregation in Federated GNN
Machine Learning

[2602.15510] On the Geometric Coherence of Global Aggregation in Federated GNN

This paper discusses the geometric coherence issues in global aggregation for Federated Graph Neural Networks (GNNs) and proposes a new f...

arXiv - Machine Learning · 4 min ·
[2602.15478] Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data
Machine Learning

[2602.15478] Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data

This article evaluates a federated learning framework for mood inference using smartphone sensing data across different countries, highli...

arXiv - Machine Learning · 3 min ·
[2602.15473] POP: Prior-fitted Optimizer Policies
Machine Learning

[2602.15473] POP: Prior-fitted Optimizer Policies

The paper introduces POP (Prior-fitted Optimizer Policies), a meta-learned optimization method that predicts step sizes based on contextu...

arXiv - Machine Learning · 3 min ·
[2602.15457] Benchmarking IoT Time-Series AD with Event-Level Augmentations
Machine Learning

[2602.15457] Benchmarking IoT Time-Series AD with Event-Level Augmentations

This paper presents a novel evaluation protocol for anomaly detection in IoT time-series data, emphasizing event-level assessments over t...

arXiv - Machine Learning · 4 min ·
[2602.15405] Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits
Machine Learning

[2602.15405] Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits

This paper presents a novel framework for joint signal enhancement and classification using coupled diffusion models, improving accuracy ...

arXiv - Machine Learning · 4 min ·
[2602.15393] Doubly Stochastic Mean-Shift Clustering
Nlp

[2602.15393] Doubly Stochastic Mean-Shift Clustering

The paper presents Doubly Stochastic Mean-Shift (DSMS), an innovative clustering algorithm that enhances standard Mean-Shift methods by i...

arXiv - Machine Learning · 3 min ·
[2602.15330] A Scalable Curiosity-Driven Game-Theoretic Framework for Long-Tail Multi-Label Learning in Data Mining
Machine Learning

[2602.15330] A Scalable Curiosity-Driven Game-Theoretic Framework for Long-Tail Multi-Label Learning in Data Mining

This paper presents a novel Curiosity-Driven Game-Theoretic framework for addressing long-tail multi-label learning challenges in data mi...

arXiv - AI · 4 min ·
[2602.15322] On Surprising Effectiveness of Masking Updates in Adaptive Optimizers
Llms

[2602.15322] On Surprising Effectiveness of Masking Updates in Adaptive Optimizers

This paper explores the effectiveness of randomly masking updates in adaptive optimizers for training large language models, introducing ...

arXiv - AI · 3 min ·
[2602.15304] Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization
Machine Learning

[2602.15304] Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization

This article presents a hybrid framework combining Federated Learning and Split Learning to enhance privacy in clinical decision-making w...

arXiv - AI · 4 min ·
[2602.15293] The Information Geometry of Softmax: Probing and Steering
Machine Learning

[2602.15293] The Information Geometry of Softmax: Probing and Steering

This paper explores the information geometry of softmax distributions, focusing on how AI systems encode semantic structures and the deve...

arXiv - AI · 3 min ·
[2602.15283] Complex-Valued Unitary Representations as Classification Heads for Improved Uncertainty Quantification in Deep Neural Networks
Machine Learning

[2602.15283] Complex-Valued Unitary Representations as Classification Heads for Improved Uncertainty Quantification in Deep Neural Networks

This paper introduces a novel classification head architecture using complex-valued unitary representations to enhance uncertainty quanti...

arXiv - AI · 4 min ·
[2602.15253] Scaling Laws for Masked-Reconstruction Transformers on Single-Cell Transcriptomics
Machine Learning

[2602.15253] Scaling Laws for Masked-Reconstruction Transformers on Single-Cell Transcriptomics

This article presents a study on the scaling laws of masked-reconstruction transformers applied to single-cell transcriptomics, revealing...

arXiv - Machine Learning · 4 min ·
[2602.15239] Size Transferability of Graph Transformers with Convolutional Positional Encodings
Machine Learning

[2602.15239] Size Transferability of Graph Transformers with Convolutional Positional Encodings

This paper explores the size transferability of Graph Transformers (GTs) with convolutional positional encodings, demonstrating their abi...

arXiv - Machine Learning · 3 min ·
[2602.15236] BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening
Machine Learning

[2602.15236] BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening

BindCLIP introduces a novel framework for virtual screening, enhancing ligand identification through a unified contrastive-generative lea...

arXiv - Machine Learning · 4 min ·
[2602.15229] tensorFM: Low-Rank Approximations of Cross-Order Feature Interactions
Machine Learning

[2602.15229] tensorFM: Low-Rank Approximations of Cross-Order Feature Interactions

The paper introduces tensorFM, a model designed for efficient low-rank approximations of cross-order feature interactions in tabular cate...

arXiv - Machine Learning · 4 min ·
[2602.15210] ÜberWeb: Insights from Multilingual Curation for a 20-Trillion-Token Dataset
Llms

[2602.15210] ÜberWeb: Insights from Multilingual Curation for a 20-Trillion-Token Dataset

The paper discusses multilingual data curation strategies for training foundation models, revealing that targeted improvements in data qu...

arXiv - Machine Learning · 4 min ·
[2602.15184] Learning Data-Efficient and Generalizable Neural Operators via Fundamental Physics Knowledge
Machine Learning

[2602.15184] Learning Data-Efficient and Generalizable Neural Operators via Fundamental Physics Knowledge

This paper presents a framework for enhancing data efficiency and generalization in neural operators by integrating fundamental physics k...

arXiv - Machine Learning · 3 min ·
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