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#machine learning

18 articles tagged #machine learning

arxivMay 29bullish

HARP: Hadamard-Preconditioned Adaptive Rotation Processor for Extreme LLM Quantization

arXiv:2605.29843v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is essential for deploying LLMs under memory and bandwidth constraints. However, extreme low-bit quantization remains highly sensitive to activation outliers and anisotropic weight curvature. Existing incoherence-base

LL1 model#quantization#machine learning#optimizationRead on arxiv →
arxivMay 28

Revisiting Metafeatures to Explain Model Differences on Tabular Data

arXiv:2605.28418v1 Announce Type: new Abstract: With the rise of tabular foundation models alongside traditional models still performing well on many tasks, choosing the right model for a tabular dataset remains difficult. We investigate whether dataset meta-features can explain performance gaps bet

TATA2 models#machine learning#benchmark#tabular dataRead on arxiv →
arxivMay 19

Fidelity Probes for Specification--Code Alignment

arXiv:2605.17246v1 Announce Type: cross Abstract: We introduce fidelity probes: natural-language questions generated from a reference artifact with code-derived ground-truth answers, answered from a candidate specification. The fraction of agreeing probes, which we call the fidelity, decomposes into

LLANDE7 models · +4#machine learning#artificial intelligence#benchmarkRead on arxiv →
arxivMay 15bearish

NodeSynth: Socially Aligned Synthetic Data for AI Evaluation

arXiv:2605.14381v1 Announce Type: cross Abstract: Recent advancements in generative AI facilitate large-scale synthetic data generation for model evaluation. However, without targeted approaches, these datasets often lack the sociotechnical nuance required for sensitive domains. We introduce NodeSyn

CLLLTA3 models#synthetic data#model evaluation#safetyRead on arxiv →
arxivMay 14bullish

Attention Once Is All You Need: Efficient Streaming Inference with Stateful Transformers

arXiv:2605.13784v1 Announce Type: new Abstract: Conventional transformer inference engines are request-driven, paying an O(n) prefill cost on every query. In streaming workloads, where data arrives continuously and queries probe an ever-growing context, this cost is prohibitive. We introduce a data-

VLSGTE3 models#streaming#inference#optimizationRead on arxiv →
arxivMay 13

Constructive conditional normalizing flows

arXiv:2602.08606v3 Announce Type: replace-cross Abstract: Motivated by applications in conditional sampling, given a probability measure $\mu$ and a diffeomorphism $\phi$, we consider the problem of simultaneously approximating $\phi$ and the pushforward $\phi_{\#}\mu$ by means of the flow of a cont

PE1 model#optimization#machine learning#probabilityRead on arxiv →
arxivMay 11

A Rod Flow Model for Adam at the Edge of Stability

arXiv:2605.06821v1 Announce Type: cross Abstract: Cohen et al. (arXiv:2207.14484) observed that adaptive gradient methods such as Adam operate at the edge of stability. While there has been significant work on continuous-time modeling of gradient descent at the edge of stability, extending these mod

ADRMNA5 models · +2#optimization#machine learning#momentum methodsRead on arxiv →
arxivMay 5bullish

FG$^2$-GDN: Enhancing Long-Context Gated Delta Networks with Doubly Fine-Grained Control

arXiv:2604.19021v2 Announce Type: replace Abstract: Linear attention mechanisms have emerged as promising alternatives to softmax attention, offering linear-time complexity during inference. Recent advances such as Gated DeltaNet (GDN) and Kimi Delta Attention (KDA) have demonstrated that the delta

GAKIFG4 models · +1#machine learning#attention mechanisms#optimizationRead on arxiv →
arxivMay 1bullish

Making Logic a First-Class Citizen in Generative ML for Networking

arXiv:2506.23964v3 Announce Type: replace-cross Abstract: Generative ML models are increasingly popular in networking for tasks such as telemetry imputation, prediction, and synthetic trace generation. Despite their capabilities, they suffer from two shortcomings: \emph{(i)} their output is often vi

GPDUZO4 models · +1#networking#machine learning#rule learningRead on arxiv →
arxivApr 30bullish

MARVIS: Modality Adaptive Reasoning over VISualizations

arXiv:2507.01544v2 Announce Type: replace Abstract: Predictive applications of machine learning often rely on small (sub 1 Bn parameter) specialized models tuned to particular domains or modalities. Such models often achieve excellent performance, but lack flexibility. LLMs and VLMs offer versatilit

MAGE2 models#machine learning#predictive modeling#multimodal learningRead on arxiv →
arxivApr 28

Surface Sensitivity in Lean 4 Autoformalization

arXiv:2604.23135v1 Announce Type: new Abstract: Natural-language variation poses a key challenge in Lean autoformalization: semantically equivalent paraphrases of the same theorem statements can induce divergent formal outputs, yet it remains unclear whether this variation reflects semantic disagree

GPPRMI3 models#machine learning#autoformalization#natural language processingRead on arxiv →
arxivApr 27

Neural Recovery of Historical Lexical Structure in Bantu Languages from Modern Data

arXiv:2604.22730v1 Announce Type: cross Abstract: We investigate whether neural models trained exclusively on modern morphological data can recover cross-lingual lexical structure consistent with historical reconstruction. Using BantuMorph v7, a transformer over Bantu morphological paradigms, we ana

BAME2 models#machine learning#natural language processing#language reconstructionRead on arxiv →
arxivApr 24

Promoting Simple Agents: Ensemble Methods for Event-Log Prediction

arXiv:2604.21629v1 Announce Type: cross Abstract: We compare lightweight automata-based models (n-grams) with neural architectures (LSTM, Transformer) for next-activity prediction in streaming event logs. Experiments on synthetic patterns and five real-world process mining datasets show that n-grams

N-LSTR3 models#machine learning#ensemble methods#process miningRead on arxiv →
arxivApr 16

Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study

arXiv:2604.13928v1 Announce Type: new Abstract: Industrial time-series data from real production environments exhibits substantially higher complexity than commonly used benchmark datasets, primarily due to heterogeneous, multi-stage operational processes. As a result, anomaly detection methods vali

ISTERE4 models · +1#anomaly detection#industrial time-series#autoencodersRead on arxiv →
arxivApr 13

Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods: A Retrospective Cohort Study

arXiv:2510.22293v4 Announce Type: replace Abstract: Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects 30-40% of US adults and is the most common chronic liver disease. Although often asymptomatic, progression can lead to cirrhosis. The objective of the study was to

LARAXG5 models · +2#machine learning#healthcare#prediction modelRead on arxiv →
arxivApr 3

Semantic Interaction Information mediates compositional generalization in latent space

arXiv:2603.27134v2 Announce Type: replace Abstract: Are there still barriers to generalization once all relevant variables are known? We address this question via a framework that casts compositional generalization as a variational inference problem over latent variables with parametric interactions

REECFU4 models · +1#machine learning#generalization#reinforcement learningRead on arxiv →
arxivApr 3bullish

Adaptive Regime-Aware Stock Price Prediction Using Autoencoder-Gated Dual Node Transformers with Reinforcement Learning Control

arXiv:2603.19136v2 Announce Type: replace Abstract: Stock markets exhibit regime-dependent behavior where prediction models optimized for stable conditions often fail during volatile periods. Existing approaches typically treat all market states uniformly or require manual regime labeling, which is

AUDUSO3 models#machine learning#stock market#predictionRead on arxiv →
arxivApr 2

Event Embedding of Protein Networks : Compositional Learning of Biological Function

arXiv:2604.00911v1 Announce Type: new Abstract: In this work, we study whether enforcing strict compositional structure in sequence embeddings yields meaningful geometric organization when applied to protein-protein interaction networks. Using Event2Vec, an additive sequence embedding model, we trai

EVWO2 models#protein-protein interaction networks#sequence embeddings#machine learningRead on arxiv →
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