Claims Browser

Most ASR advances remain concentrated on high-resource languages while indigenous languages lack speech resources and language technologies

"most advances remain concentrated on high-resource languages, while indigenous languages continue to suffer from a lack of speech resources and language technologies"

Deep neural networks often exploit spurious associations in their training data through shortcut learning

"Deep neural networks often exploit spurious associations in their training data, a failure known as shortcut learning."

Concept-based explainability methods can mistake correlations between concepts as evidence that the model uses them because they evaluate each concept in isolation

"Because each concept is evaluated in isolation, these methods can mistake correlations between concepts as evidence that the model uses them."

ICON decomposition recovers concept importance more accurately than seven alternative baseline methods on synthetic data with known ground truth

"On synthetic data with known ground truth, ICON recovers concept importance more accurately than seven alternative baseline methods."

ICON decomposition can isolate concepts that models genuinely rely on and quantify unexplained representation in real-world imaging models

"On skin-lesion and brain-imaging models, it isolates the concepts on which a model genuinely relies, quantifies the representation unexplained by any of the supplied concepts, and yields sparse explanations that we validate by retraining and out-of-distribution testing."

Muon is a strong optimizer for matrix-valued parameters in large language model pretraining that approximately orthogonalizes momentum using Newton-Schulz iterations

"Muon has emerged as a strong optimizer for the matrix-valued parameters in large language model pretraining, approximately orthogonalizing its momentum with a few Newton-Schulz iterations."

Finite Newton-Schulz iterations can be beneficial for nonsmooth nonconvex optimization, contrary to existing theory which treats finite depth as an approximation error

"We show that finite Newton-Schulz can instead be beneficial for nonsmooth nonconvex optimization."

A Newton-Schulz depth growing only logarithmically in the target accuracy suffices for convergence to stationary points in nonsmooth nonconvex optimization

"we prove that a Newton-Schulz depth growing only logarithmically in the target accuracy suffices for convergence to stationary points in nonsmooth nonconvex optimization"

Muon with exact-polar update may fail to converge, while Muon with finite Newton-Schulz can succeed

"Muon with the exact-polar update may fail to converge"

The sample complexity bounds for Muon match the best-known guarantees for nonsmooth nonconvex optimization and are optimal for smooth nonconvex optimization up to problem-dependent factors

"The resulting sample complexity bounds match the best-known guarantees for nonsmooth nonconvex optimization and are optimal for smooth nonconvex optimization up to problem-dependent factors."

Under finite second moments, minimizers exist for the weak GW framework and martingale gluing can recover an optimal coupling

"Under finite second moments, minimizers exist and martingale gluing recovers an optimal coupling."

Social interaction in collective learning can amplify early mistakes when credibility is generated by senders' decision processes

"Social interaction can improve collective learning but also amplify early mistakes."

Moderate transmission accelerates error correction in multi-agent learning systems, while strong transmission can lock populations into wrong consensus

"Monte Carlo experiments show the corresponding non-monotone performance pattern: moderate transmission accelerates correction, whereas strong transmission can lock populations into wrong consensus"

Confidence-sensitive transmission amplifies social error in reinforcement learning agents, while confidence-dependent private learning stabilizes it

"Ablations reveal a dual role for confidence: credibility-sensitive transmission amplifies social error, while confidence-dependent private learning stabilises it."

Low permeability between communities sustains disagreement in multi-agent systems

"low permeability instead sustains disagreement"

Existing minimax, primal-dual, and fitted fixed-point estimators for marginalized importance weighting can leave residual occupancy-balance violations due to function-class approximation, regularization, or incomplete optimization

"Existing minimax, primal-dual, and fitted fixed-point estimators can leave residual occupancy-balance violations because of function-class approximation, regularization, or incomplete optimization."

Anima Anandkumar is using AI to model physical world phenomena including weather and fusion reactors

"Now she's using it to model the physical world, from weather to fusion reactors."

Occupancy-balance violations in existing estimators are difficult to diagnose and reduce because objectives lack a direct supervised validation loss for hyperparameter tuning, model selection, and early stopping

"These violations are difficult to diagnose and reduce because the objectives generally lack a direct supervised validation loss for hyperparameter tuning, model selection, and early stopping."