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Llms

[D] The Bitter Lesson of Optimization: Why training Neural Networks to update themselves is mathematically brutal (but probably inevitable)

Are we still stuck in the "feature engineering" era of optimization? We trust neural networks to learn unimaginably complex patterns from...

Reddit - Machine Learning · 1 min ·
Google quietly launched an AI dictation app that works offline
Machine Learning

Google quietly launched an AI dictation app that works offline

Google's new offline-first dictation app uses Gemma AI models to take on the apps like Wispr Flow.

TechCrunch - AI · 4 min ·
UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

AI News - General · 4 min ·

All Content

[2602.16634] Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models
Machine Learning

[2602.16634] Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models

The paper presents Enhanced Diffusion Sampling, a novel method for efficient rare event sampling and free energy calculation in molecular...

arXiv - Machine Learning · 4 min ·
[2602.16568] Separating Oblivious and Adaptive Models of Variable Selection
Machine Learning

[2602.16568] Separating Oblivious and Adaptive Models of Variable Selection

This paper explores the differences between oblivious and adaptive models in variable selection, revealing significant implications for s...

arXiv - Machine Learning · 3 min ·
[2602.16537] Optimal training-conditional regret for online conformal prediction
Machine Learning

[2602.16537] Optimal training-conditional regret for online conformal prediction

The paper explores optimal training-conditional regret in online conformal prediction for non-stationary data streams, addressing distrib...

arXiv - Machine Learning · 3 min ·
[2602.16505] Functional Decomposition and Shapley Interactions for Interpreting Survival Models
Machine Learning

[2602.16505] Functional Decomposition and Shapley Interactions for Interpreting Survival Models

This article introduces Survival Functional Decomposition (SurvFD) and SurvSHAP-IQ, innovative methods for interpreting survival models b...

arXiv - Machine Learning · 3 min ·
[2602.16590] A Contrastive Learning Framework Empowered by Attention-based Feature Adaptation for Street-View Image Classification
Llms

[2602.16590] A Contrastive Learning Framework Empowered by Attention-based Feature Adaptation for Street-View Image Classification

This paper presents CLIP-MHAdapter, a novel contrastive learning framework that enhances street-view image classification by using attent...

arXiv - Machine Learning · 3 min ·
[2602.16585] DataJoint 2.0: A Computational Substrate for Agentic Scientific Workflows
Machine Learning

[2602.16585] DataJoint 2.0: A Computational Substrate for Agentic Scientific Workflows

DataJoint 2.0 introduces a relational workflow model designed to enhance collaboration in scientific data pipelines, ensuring data integr...

arXiv - AI · 3 min ·
[2602.16476] Learning Preference from Observed Rankings
Machine Learning

[2602.16476] Learning Preference from Observed Rankings

This paper presents a framework for learning individual preferences from partial ranking data, enhancing recommendation systems by addres...

arXiv - Machine Learning · 4 min ·
[2602.16352] Machine Learning in Epidemiology
Machine Learning

[2602.16352] Machine Learning in Epidemiology

This article explores the application of machine learning in epidemiology, detailing methodologies for data analysis, model evaluation, a...

arXiv - Machine Learning · 3 min ·
[2602.16265] On sparsity, extremal structure, and monotonicity properties of Wasserstein and Gromov-Wasserstein optimal transport plans
Machine Learning

[2602.16265] On sparsity, extremal structure, and monotonicity properties of Wasserstein and Gromov-Wasserstein optimal transport plans

This article explores the properties of Gromov-Wasserstein (GW) distance in optimal transport, focusing on sparsity, extremal structures,...

arXiv - Machine Learning · 3 min ·
[2602.16183] Multi-Agent Combinatorial-Multi-Armed-Bandit framework for the Submodular Welfare Problem under Bandit Feedback
Ai Agents

[2602.16183] Multi-Agent Combinatorial-Multi-Armed-Bandit framework for the Submodular Welfare Problem under Bandit Feedback

This paper presents a multi-agent combinatorial multi-armed bandit framework for the Submodular Welfare Problem, achieving improved regre...

arXiv - Machine Learning · 3 min ·
[2602.16372] AI-Driven Structure Refinement of X-ray Diffraction
Nlp

[2602.16372] AI-Driven Structure Refinement of X-ray Diffraction

This paper presents WPEM, an AI-driven workflow for refining X-ray diffraction data, enhancing the stability and accuracy of peak intensi...

arXiv - AI · 4 min ·
[2602.16142] Ratio Covers of Convex Sets and Optimal Mixture Density Estimation
Machine Learning

[2602.16142] Ratio Covers of Convex Sets and Optimal Mixture Density Estimation

This paper explores optimal mixture density estimation using Kullback-Leibler divergence, providing new insights into density estimation ...

arXiv - Machine Learning · 4 min ·
[2602.16315] The Diversity Paradox revisited: Systemic Effects of Feedback Loops in Recommender Systems
Machine Learning

[2602.16315] The Diversity Paradox revisited: Systemic Effects of Feedback Loops in Recommender Systems

This paper revisits the diversity paradox in recommender systems, exploring how feedback loops influence user behavior and consumption pa...

arXiv - AI · 3 min ·
[2602.16309] The Weight of a Bit: EMFI Sensitivity Analysis of Embedded Deep Learning Models
Machine Learning

[2602.16309] The Weight of a Bit: EMFI Sensitivity Analysis of Embedded Deep Learning Models

This article investigates the impact of different number representations on the vulnerability of embedded deep learning models to electro...

arXiv - AI · 3 min ·
[2602.16256] Color-based Emotion Representation for Speech Emotion Recognition
Machine Learning

[2602.16256] Color-based Emotion Representation for Speech Emotion Recognition

This article presents a novel approach to Speech Emotion Recognition (SER) by utilizing color attributes to represent emotions, enhancing...

arXiv - AI · 3 min ·
[2602.16241] Are LLMs Ready to Replace Bangla Annotators?
Llms

[2602.16241] Are LLMs Ready to Replace Bangla Annotators?

This article evaluates the effectiveness of Large Language Models (LLMs) as annotators for Bangla hate speech, revealing significant bias...

arXiv - AI · 3 min ·
[2602.16098] Collaborative Zone-Adaptive Zero-Day Intrusion Detection for IoBT
Machine Learning

[2602.16098] Collaborative Zone-Adaptive Zero-Day Intrusion Detection for IoBT

The paper presents a novel Zone-Adaptive Intrusion Detection framework for the Internet of Battlefield Things (IoBT), addressing the chal...

arXiv - Machine Learning · 4 min ·
[2602.16090] Examining Fast Radiative Feedbacks Using Machine-Learning Weather Emulators
Machine Learning

[2602.16090] Examining Fast Radiative Feedbacks Using Machine-Learning Weather Emulators

This article explores the use of machine-learning weather emulators to analyze fast radiative feedbacks in the climate system, focusing o...

arXiv - Machine Learning · 4 min ·
[2602.16080] Surgical Activation Steering via Generative Causal Mediation
Llms

[2602.16080] Surgical Activation Steering via Generative Causal Mediation

This article presents Generative Causal Mediation (GCM), a novel approach for steering language model behaviors by identifying and manipu...

arXiv - Machine Learning · 3 min ·
[2602.16063] MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Markets
Machine Learning

[2602.16063] MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Markets

The paper presents MARLEM, a novel multi-agent reinforcement learning framework designed for studying implicit cooperation in decentraliz...

arXiv - Machine Learning · 4 min ·
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