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Llms

[D] Tested model routing on financial AI datasets — good savings and curious what benchmarks others use.

Ran a benchmark evaluating whether prompt complexity-based routing delivers meaningful savings. Used public HuggingFace datasets. Here's ...

Reddit - Machine Learning · 1 min ·
OpenAI alums have been quietly investing from a new, potentially $100M fund  | TechCrunch
Data Science

OpenAI alums have been quietly investing from a new, potentially $100M fund  | TechCrunch

Zero Shot, a new venture capital fund with deep ties to OpenAI, is aiming to raise $100 million for its first fund. It has already writte...

TechCrunch - AI · 6 min ·
Google quietly releases an offline-first AI dictation app on iOS | TechCrunch
Machine Learning

Google quietly releases an offline-first AI dictation app on iOS | TechCrunch

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

TechCrunch - AI · 4 min ·

All Content

[2602.17941] Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders
Machine Learning

[2602.17941] Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders

The paper presents CCAGNN, a novel Confounder-Aware causal GNN framework designed to improve predictions in graph causal classification b...

arXiv - Machine Learning · 4 min ·
[2602.17940] Tighter Regret Lower Bound for Gaussian Process Bandits with Squared Exponential Kernel in Hypersphere
Machine Learning

[2602.17940] Tighter Regret Lower Bound for Gaussian Process Bandits with Squared Exponential Kernel in Hypersphere

This paper presents a tighter lower bound on cumulative regret for Gaussian process bandits using a squared exponential kernel in a hyper...

arXiv - Machine Learning · 4 min ·
[2602.17934] Causal Neighbourhood Learning for Invariant Graph Representations
Machine Learning

[2602.17934] Causal Neighbourhood Learning for Invariant Graph Representations

The paper presents Causal Neighbourhood Learning (CNL-GNN), a novel framework for improving Graph Neural Networks (GNNs) by addressing sp...

arXiv - Machine Learning · 4 min ·
[2602.17898] Breaking the Correlation Plateau: On the Optimization and Capacity Limits of Attention-Based Regressors
Machine Learning

[2602.17898] Breaking the Correlation Plateau: On the Optimization and Capacity Limits of Attention-Based Regressors

This paper explores the limitations of attention-based regression models, particularly the phenomenon of the Pearson correlation coeffici...

arXiv - Machine Learning · 4 min ·
[2602.17893] COMBA: Cross Batch Aggregation for Learning Large Graphs with Context Gating State Space Models
Machine Learning

[2602.17893] COMBA: Cross Batch Aggregation for Learning Large Graphs with Context Gating State Space Models

The paper presents COMBA, a novel approach for learning large graphs using state space models, emphasizing cross batch aggregation and gr...

arXiv - Machine Learning · 4 min ·
[2602.17868] MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies
Llms

[2602.17868] MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies

MantisV2 introduces advanced techniques for zero-shot time series classification, utilizing synthetic data and refined test-time strategi...

arXiv - Machine Learning · 4 min ·
[2602.17888] Machine Learning Based Prediction of Surgical Outcomes in Chronic Rhinosinusitis from Clinical Data
Machine Learning

[2602.17888] Machine Learning Based Prediction of Surgical Outcomes in Chronic Rhinosinusitis from Clinical Data

This article explores the application of machine learning to predict surgical outcomes in patients with chronic rhinosinusitis, demonstra...

arXiv - Machine Learning · 4 min ·
[2602.17867] ADAPT: Hybrid Prompt Optimization for LLM Feature Visualization
Llms

[2602.17867] ADAPT: Hybrid Prompt Optimization for LLM Feature Visualization

The paper presents ADAPT, a hybrid method for optimizing prompts in LLM feature visualization, addressing challenges in local minima and ...

arXiv - Machine Learning · 3 min ·
[2602.17865] Financial time series augmentation using transformer based GAN architecture
Machine Learning

[2602.17865] Financial time series augmentation using transformer based GAN architecture

This article explores the use of transformer-based GANs for augmenting financial time series data, enhancing predictive accuracy in forec...

arXiv - Machine Learning · 4 min ·
[2602.17853] Neural Prior Estimation: Learning Class Priors from Latent Representations
Machine Learning

[2602.17853] Neural Prior Estimation: Learning Class Priors from Latent Representations

The paper introduces Neural Prior Estimator (NPE), a framework for learning class priors from latent representations, addressing class im...

arXiv - Machine Learning · 3 min ·
[2602.17835] Influence-Preserving Proxies for Gradient-Based Data Selection in LLM Fine-tuning
Llms

[2602.17835] Influence-Preserving Proxies for Gradient-Based Data Selection in LLM Fine-tuning

The paper presents Iprox, a two-stage framework for gradient-based data selection in LLM fine-tuning, which constructs influence-preservi...

arXiv - Machine Learning · 4 min ·
[2602.17829] Causality by Abstraction: Symbolic Rule Learning in Multivariate Timeseries with Large Language Models
Llms

[2602.17829] Causality by Abstraction: Symbolic Rule Learning in Multivariate Timeseries with Large Language Models

This paper introduces ruleXplain, a framework utilizing Large Language Models to extract causal rules from multivariate timeseries data, ...

arXiv - Machine Learning · 4 min ·
[2602.17798] Grassmannian Mixture-of-Experts: Concentration-Controlled Routing on Subspace Manifolds
Machine Learning

[2602.17798] Grassmannian Mixture-of-Experts: Concentration-Controlled Routing on Subspace Manifolds

The paper presents Grassmannian Mixture-of-Experts (GrMoE), a novel routing framework that enhances expert assignment in machine learning...

arXiv - Machine Learning · 4 min ·
[2602.17809] Calibrated Adaptation: Bayesian Stiefel Manifold Priors for Reliable Parameter-Efficient Fine-Tuning
Llms

[2602.17809] Calibrated Adaptation: Bayesian Stiefel Manifold Priors for Reliable Parameter-Efficient Fine-Tuning

This paper introduces Stiefel-Bayes Adapters (SBA), a Bayesian framework for parameter-efficient fine-tuning of large language models, en...

arXiv - Machine Learning · 4 min ·
[2602.17783] Multi-material Multi-physics Topology Optimization with Physics-informed Gaussian Process Priors
Machine Learning

[2602.17783] Multi-material Multi-physics Topology Optimization with Physics-informed Gaussian Process Priors

This paper presents a novel framework for multi-material, multi-physics topology optimization using physics-informed Gaussian processes, ...

arXiv - Machine Learning · 4 min ·
[2602.17751] Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring
Machine Learning

[2602.17751] Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring

This paper explores the impact of target class selection on the compressibility of neural networks for avian monitoring using energy-auto...

arXiv - Machine Learning · 4 min ·
[2602.17706] Parallel Complex Diffusion for Scalable Time Series Generation
Machine Learning

[2602.17706] Parallel Complex Diffusion for Scalable Time Series Generation

The paper presents PaCoDi, a novel approach to time series generation using parallel complex diffusion, enhancing efficiency and quality ...

arXiv - Machine Learning · 4 min ·
[2602.17696] Can LLM Safety Be Ensured by Constraining Parameter Regions?
Llms

[2602.17696] Can LLM Safety Be Ensured by Constraining Parameter Regions?

This article explores the effectiveness of identifying 'safety regions' in large language models (LLMs) by evaluating various methods acr...

arXiv - Machine Learning · 3 min ·
[2602.17682] Duality Models: An Embarrassingly Simple One-step Generation Paradigm
Machine Learning

[2602.17682] Duality Models: An Embarrassingly Simple One-step Generation Paradigm

The paper presents Duality Models (DuMo), a novel approach in generative modeling that enhances stability and efficiency by using a share...

arXiv - Machine Learning · 4 min ·
[2602.17683] Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates
Nlp

[2602.17683] Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates

This paper presents a probabilistic framework for forecasting NDVI from sparse satellite data and weather covariates, enhancing precision...

arXiv - Machine Learning · 4 min ·
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