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Machine Learning

[P] bitnet-edge: Ternary-weight CNNs ({-1,0,+1}) on MNIST and CIFAR-10, deployed to ESP32-S3 with zero multiplications

I built a pipeline that takes ternary-quantized CNNs from PyTorch training all the way to bare-metal inference on an ESP32-S3 microcontro...

Reddit - Machine Learning · 1 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 ·
Llms

[R] Looking for arXiv cs.LG endorser, inference monitoring using information geometry

Hi r/MachineLearning, I’m looking for an arXiv endorser in cs.LG for a paper on inference-time distribution shift detection for deployed ...

Reddit - Machine Learning · 1 min ·

All Content

[2602.21824] DocDjinn: Controllable Synthetic Document Generation with VLMs and Handwriting Diffusion
Llms

[2602.21824] DocDjinn: Controllable Synthetic Document Generation with VLMs and Handwriting Diffusion

DocDjinn introduces a framework for generating synthetic documents using Vision-Language Models (VLMs), addressing challenges in data acq...

arXiv - Machine Learning · 4 min ·
[2602.21674] Error-awareness Accelerates Active Automata Learning
Machine Learning

[2602.21674] Error-awareness Accelerates Active Automata Learning

The paper discusses how error-awareness can enhance Active Automata Learning (AAL) algorithms, enabling them to learn more efficiently fr...

arXiv - Machine Learning · 3 min ·
[2602.21597] NGDB-Zoo: Towards Efficient and Scalable Neural Graph Databases Training
Machine Learning

[2602.21597] NGDB-Zoo: Towards Efficient and Scalable Neural Graph Databases Training

The paper presents NGDB-Zoo, a framework designed to enhance the training efficiency of Neural Graph Databases (NGDBs) by decoupling logi...

arXiv - Machine Learning · 3 min ·
[2602.21601] Deep Clustering based Boundary-Decoder Net for Inter and Intra Layer Stress Prediction of Heterogeneous Integrated IC Chip
Machine Learning

[2602.21601] Deep Clustering based Boundary-Decoder Net for Inter and Intra Layer Stress Prediction of Heterogeneous Integrated IC Chip

This article presents a novel approach using a Deep Clustering based Boundary-Decoder Net for predicting inter and intra-layer stress in ...

arXiv - Machine Learning · 4 min ·
[2602.21593] Breaking Semantic-Aware Watermarks via LLM-Guided Coherence-Preserving Semantic Injection
Llms

[2602.21593] Breaking Semantic-Aware Watermarks via LLM-Guided Coherence-Preserving Semantic Injection

The paper introduces a novel attack method, Coherence-Preserving Semantic Injection (CSI), that exploits vulnerabilities in semantic-awar...

arXiv - Machine Learning · 4 min ·
[2602.21585] Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences
Llms

[2602.21585] Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences

The paper presents Duel-Evolve, an innovative algorithm that optimizes large language model outputs at test time using pairwise self-pref...

arXiv - AI · 4 min ·
[2602.21545] Muon+: Towards Better Muon via One Additional Normalization Step
Llms

[2602.21545] Muon+: Towards Better Muon via One Additional Normalization Step

The paper introduces Muon+, an enhancement to the Muon optimizer, which adds an additional normalization step to improve performance in t...

arXiv - Machine Learning · 3 min ·
[2602.21466] Asymptotically Fast Clebsch-Gordan Tensor Products with Vector Spherical Harmonics
Machine Learning

[2602.21466] Asymptotically Fast Clebsch-Gordan Tensor Products with Vector Spherical Harmonics

This article presents a novel algorithm for computing Clebsch-Gordan tensor products using vector spherical harmonics, achieving signific...

arXiv - Machine Learning · 4 min ·
[2602.21426] Proximal-IMH: Proximal Posterior Proposals for Independent Metropolis-Hastings with Approximate Operators
Machine Learning

[2602.21426] Proximal-IMH: Proximal Posterior Proposals for Independent Metropolis-Hastings with Approximate Operators

The paper introduces Proximal-IMH, a novel sampling method for Bayesian inverse problems that enhances the efficiency of the Independent ...

arXiv - Machine Learning · 3 min ·
[2602.21454] When Learning Hurts: Fixed-Pole RNN for Real-Time Online Training
Machine Learning

[2602.21454] When Learning Hurts: Fixed-Pole RNN for Real-Time Online Training

This paper explores the limitations of learning recurrent poles in RNNs for real-time online training, advocating for fixed-pole architec...

arXiv - Machine Learning · 4 min ·
[2602.21408] Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates
Ai Infrastructure

[2602.21408] Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates

This article presents Generative Bayesian Computation (GBC) as a scalable alternative to Gaussian Process (GP) surrogates, addressing lim...

arXiv - Machine Learning · 4 min ·
[2602.21415] Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting
Machine Learning

[2602.21415] Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting

This paper benchmarks various deep learning models for forecasting electricity demand across US power grids, revealing no single best mod...

arXiv - Machine Learning · 4 min ·
[2602.21321] Dynamic Symmetric Point Tracking: Tackling Non-ideal Reference in Analog In-memory Training
Llms

[2602.21321] Dynamic Symmetric Point Tracking: Tackling Non-ideal Reference in Analog In-memory Training

This article presents a novel approach to dynamic symmetric point tracking in analog in-memory computing, addressing the challenges posed...

arXiv - Machine Learning · 4 min ·
[2602.21317] Shared Nature, Unique Nurture: PRISM for Pluralistic Reasoning via In-context Structure Modeling
Llms

[2602.21317] Shared Nature, Unique Nurture: PRISM for Pluralistic Reasoning via In-context Structure Modeling

The paper presents PRISM, a model-agnostic system designed to enhance large language models (LLMs) by fostering pluralistic reasoning thr...

arXiv - Machine Learning · 3 min ·
[2602.21307] SymTorch: A Framework for Symbolic Distillation of Deep Neural Networks
Machine Learning

[2602.21307] SymTorch: A Framework for Symbolic Distillation of Deep Neural Networks

SymTorch is a new library that automates the symbolic distillation of deep neural networks, converting them into interpretable mathematic...

arXiv - Machine Learning · 3 min ·
[2602.21276] Neural network optimization strategies and the topography of the loss landscape
Machine Learning

[2602.21276] Neural network optimization strategies and the topography of the loss landscape

This paper explores neural network optimization strategies, focusing on the differences between stochastic gradient descent (SGD) and qua...

arXiv - Machine Learning · 4 min ·
[2602.02137] DCoPilot: Generative AI-Empowered Policy Adaptation for Dynamic Data Center Operations
Generative Ai

[2602.02137] DCoPilot: Generative AI-Empowered Policy Adaptation for Dynamic Data Center Operations

DCoPilot is a hybrid framework utilizing generative AI to enhance policy adaptation in dynamic data center operations, ensuring efficient...

arXiv - Machine Learning · 4 min ·
[2602.00012] OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models
Llms

[2602.00012] OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models

The OGD4All framework enhances citizen interaction with geospatial Open Government Data using Large Language Models, achieving high accur...

arXiv - Machine Learning · 3 min ·
[2601.17064] Between Search and Platform: ChatGPT Under the DSA
Llms

[2601.17064] Between Search and Platform: ChatGPT Under the DSA

This article analyzes the classification of ChatGPT under the Digital Services Act (DSA), proposing it as a hybrid of search engine and p...

arXiv - AI · 3 min ·
[2512.09069] KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification
Machine Learning

[2512.09069] KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification

The paper presents KD-OCT, a novel knowledge distillation framework that enhances the efficiency of deep learning models for classifying ...

arXiv - Machine Learning · 4 min ·
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