Articles tagged
Machine Learning
38 articles
OPUS: How to Train LLMs 6x Faster by Choosing the Right Data
OPUS scores every sample by alignment minus redundancy in optimizer update space; on Qwen3-8B it beat full 3B-token training using 0.5B, at 4.7% extra compute.
Green-VLA: One AI Brain for All Robots
Green-VLA trains one policy for humanoids, mobile manipulators and fixed arms across five stages and 3,000 hours of demos, hitting state of the art on CALVIN ABC-D.
To Grok Grokking: Why Neural Networks Sometimes Understand Late
Grokking happens even in ridge regression: fast memorization, a long plateau of orthogonal noise, then late generalization with a delay that scales as 1/λ.
Tensor Networks: A Mathematical Bridge Between Neural and Symbolic AI
Tensor networks give neural and symbolic AI one language: logic becomes sparse tensors, neural nets low-rank ones, and inference in both is a contraction.
M²FMoE: When Experts Learn to Predict Floods
M2FMoE routes Fourier and wavelet experts by frequency band to forecast floods without extreme-event labels, cutting error 45.7% on Almaden and beating 13 baselines.
BALLAST: When a Bandit Teaches Your Database How Long to Wait
BALLAST replaces Raft's random election timeouts with a LinUCB contextual bandit; a Rust simulator cuts cluster unavailability by 84%, and 90.8% under bursty WAN.
AI Co-Scientist: Teaching Models to Write Research Plans Better Than Humans
Rubrics mined from papers turn a frozen grader into an RL reward; experts preferred the generated research plans 70% over baseline and 52% over published ones.
HyDRA: Teaching Your Phone to Understand Images Without Breaking the Bank
HyDRA allocates LoRA rank per layer and inside layers automatically, buying a 4.7% gain on mobile vision-language models with no extra trainable parameters.
Comp-LLM: When an Army of Experts Beats a Giant – An Analysis of a Revolution in AI Architecture
Comp-LLM routes a query as a DAG of sub-queries to parallel experts: about 35B scores 0.83 F1 to Llama-2 70B's 0.85, and 1.1-1.7x faster than sequential agents.
Cost-Constrained LLM Cascades — Meet C3PO
C3PO runs LLM cascades under a probabilistic cost constraint: stay in budget with 95% confidence, escalating to a bigger model only when the cheap one falls short.
Accurate Satellite Rain Forecasting with Physics-Conditioned Neural Networks
TUPANN nowcasts rainfall from satellite data alone, adding a differentiable advection operator and lead-time-conditioned MaxViT for forecasts 10-180 minutes ahead.
How to Detect Credit Card Fraud?
Credit card fraud is 492 cases in 284,807 transactions; pairing undersampling with SMOTE beats either alone, and KNN and MLP gain most on the imbalanced test set.
Reinforcement Learning in Pinterest Ads – DRL-PUT in action!
DRL-PUT replaces Pinterest's hand-tuned ad ranking weights with a policy network picking utility weights per request; A/B tests show CTR up 9.7%, LC-CTR up 7.7%.
Uczenie ze Wzmocnieniem w Reklamach na Pinterest – jak DRL-PUT zmienia grę / Reinforcement Learning in Pinterest Ads – How DRL-PUT Changes the Game
Pinterest's DRL-PUT learns the weights of its ad ranking utility with policy gradients instead of tuning them by hand, lifting CTR by 9.7% and LC-CTR by 7.7%.
Edge AI: How to Accelerate Neural Networks on Specialized Hardware
SLAC's SNL and Auto-SNL push neural network inference onto MPSoC FPGAs for LCLS-II, swapping weights without reconfiguration and matching or beating hls4ml latency.
Global Guarantees of Robustness: A Probabilistic Approach to AI Safety
Rather than certify every point, Mu and Lim estimate the probability that a random input is non-robust, wrapping a Clopper-Pearson interval around a small sample.
Intern-S1: The New AI Scientist That's Redefining Research
Intern-S1 is an open-source MoE science foundation model with 28B activated parameters, trained on 5T tokens and a Mixture-of-Rewards spanning over 1000 tasks.
Systematization of Knowledge: Data Minimization in Machine Learning
SoK: Data Minimization in Machine Learning maps the GDPR principle onto ML pipelines, separating horizontal from vertical techniques and pre-hoc from post-hoc steps.
Learning Machines That Don't Forget: A New Method for Evolving Data
DyMoE counters catastrophic forgetting in incremental graph learning by adding a new expert per increment, with gating and a loss that makes older experts teach it.
Dynamic Fine-Tuning (DFT): How a Single Line of Code is Revolutionizing AI Training
Dynamic Fine-Tuning rescales each token loss by the inverse of its probability, a one-line change that beats SFT on generalization and rivals offline RL methods.
A Deep Dive into the Text-to-SQL Revolution: Analyzing the Adaptive Method
Adaptive Text-to-SQL splits the work in two: a Selector retrieves conceptually similar question-schema-SQL triples, a Generator writes the query in-context.
ASkDAgger: How Artificial Intelligence Learns More Effectively by Asking Questions
ASkDAgger lets a robot flag an uncertain plan before acting; adaptive gating and foresight replay cut teacher queries and raise success on CLIPort and a real arm.
CaPulse: Teaching Machines to Hear the Rhythm of Data
CaPulse spots time-series anomalies by learning the causal rhythm of data: it fires when the cause-effect order breaks, not when a threshold is crossed.
How to Teach AI to Handle Mistakes? Meet ε-Softmax
ε-Softmax swaps rigid one-hot targets for slightly softened ones, so a mislabeled example costs the model far less. A NeurIPS 2024 answer to noisy training labels.
Simple and Effective Method for Uncertainty Quantification
Uncertainty from one deterministic model: kernel density estimation in feature space builds an information potential field flagging OOD samples on CIFAR-10 vs SVHN.
Deep Learning-based Prediction of Clinical Trial Enrollment with Uncertainty Estimates
A multimodal Clinical Longformer model predicts trial enrollment at R² 0.76 and forecasts recruitment duration with 7.52-month MAE versus 10.55 for fit-and-filter.
Consensus-Driven Active Model Selection
CODA picks the best model from many candidates via their agreement: Bayesian Dawid-Skene plus information gain wins 18 of 26 benchmarks with up to 70% fewer labels.
How AI Can Reveal Where Your Honey Comes From — A Look at Mineral Fingerprints
Random Forests read the 12-mineral fingerprint of 429 honey samples to name the source flower with 99.50% accuracy and the region of origin with 98.01%.
Efficient & Geometrically-Smart: Linear Memory SE(2)-Invariant Attention Explained
Fourier-encoded relative pose lets SE(2)-invariant attention use linear rather than quadratic memory, with error below 0.001. Best paper at the RSS 2025 workshop.
Unstable Power: How Sharpness Drives Deep Network Learning
A larger learning rate leaves the NTK better aligned with the labels at the Edge of Stability: Kernel Target Alignment jumps exactly when sharpness drops.
RiemannLoRA: A Unified Riemannian Framework for Ambiguity-Free LoRA Optimization
RiemannLoRA places LoRA updates on the manifold of rank-r matrices, erasing factorization ambiguity: half the fine-tuning steps and up to 1.2 BLEU more.
Target Polish: How to Polish Data and Reveal Its True Structure
Target Polish cleans data before factorizing it: clip X into a delta band around the current WH estimate, refactorize, repeat. Robust NMF for matrices and tensors.
Not Just Bigger Models: Why AI Should See Better Instead of Just Scaling
Adaptive sensing beats brute-force scale: EfficientNet-B0 paired with sensors that tune themselves to the scene outperforms OpenCLIP-H on perception tasks.
HeLo – A New Path for Multimodal Emotion Recognition
HeLo predicts a distribution over emotions instead of one label, fusing ECG and video via cross-attention, entropic optimal transport and a label-correlation loss.
QuEst: Blending Data and Predictions for Robust Quantile Estimation
QuEst blends a small gold sample with large model-generated imputations to estimate quantiles and CVaR, canceling simulator bias and minimizing sampling variance.
RetrySQL: Self-Correcting Query Generation
RetrySQL injects deliberate wrong reasoning steps and a BACK token into text-to-SQL pretraining, lifting OpenCoder 1.5B execution accuracy by 4 points toward GPT-4o.
How to Predict Scooter Demand? XGBoost and Urban Micromobility
XGBoost on 341 temporal, spatial and network features predicts e-scooter demand 27-49% better than rivals and up to 90% better than Prophet, node centrality on top.
Ghost Nodes: A Trick That Makes Neural Networks Learn Smarter
Ghost nodes add fake output classes: softmax runs over 13 outputs while loss counts only the 10 real ones, giving gradients room to escape flat regions early.