Claims Browser

A two-phase decentralized strategy using Gaussian Markov random field priors can estimate graph Laplacians from noisy stochastic gradient iterates and enable cooperative multitask learning

"We propose a decentralized two-phase strategy that first estimates a generalized graph Laplacian from noisy non-cooperative stochastic gradient iterates, and subsequently exploits the learned graph to enable cooperative multitask diffusion learning. This framework is motivated by a Gaussian Markov random field prior, which gives rise to a decentralized maximum likelihood estimator for the graph Laplacian."

Cooperation enabled by learned task graphs significantly improves performance over non-cooperative learning and approaches true-graph baseline performance with sufficiently small estimation stepsizes

"Simulation results corroborate the theoretical findings and demonstrate that cooperation enabled by the learned task graph significantly improves performance over non-cooperative learning, while approaching the true-graph baseline when the estimation stepsize is sufficiently small."

Little is known about how representation learning methods deal with out-of-domain speech and how they could be adapted in a few shot to new domains

"little is known about how such methods deal with out-of-domain speech and how could they be adapted in a few shot to new domains"

Adaptive domain normalization fine-tuning of pretrained Contrastive Predictive Coding models yields a 23.6% relative improvement on across-speaker ABX scores for accented speech

"the proposed method yields a relative improvement of 23.6% on across-speaker ABX scores on average compared to non adapted models"

ABX-Accent benchmark provides a standardized way to evaluate representation learning methods on accented speech with 10 different English accents

"We introduce ABX- Accent, a benchmark based on the AESRC dataset that features 10 different accents of English. It includes a small (< 10 hours) unlabelled training set in each of the accents and adaptations of the Zero Resources Challenge ABX evaluation metrics to each of the accents"

Explicit visual intermediates can help multimodal large language models externalize spatial evidence and updated visual states, but their utility depends on the image editor's ability to faithfully realize transformations

"Explicit visual intermediates can help multimodal large language models (MLLMs) externalize spatial evidence and updated visual states, but their utility depends on whether an image editor can faithfully realize the required transformation."

Utility of visual intermediates for MLLMs is strongly task-conditioned, with gains concentrated in visual cue injection, grounding, and counterfactual state realization

"we find that utility is strongly task-conditioned. Gains concentrate in visual cue injection, grounding, and counterfactual state realization"

Visual intermediates requiring symbol-sensitive construction or structural extrapolation are substantially less reliable for MLLMs

"intermediates requiring symbol-sensitive construction or structural extrapolation are substantially less reliable"

The consolidated Qwen pipeline with visual intermediates improves mean task score from 0.343 to 0.445, representing a 29.7% relative improvement

"our consolidated Qwen pipeline improves the mean task score from 0.343 to 0.445 ($+10.2$ points; $+29.7\%$ relative)"

Image editing should be positioned as a specialized visual workspace rather than a universal reasoning mechanism for MLLMs

"These results position image editing as a specialized visual workspace rather than a universal reasoning mechanism"

Existing VLM and Medical-VLM efforts have mainly focused on improving accuracy while leaving calibration in the medical domain underexplored

"existing efforts mainly focused on improving accuracy, leaving calibration in the medical domain underexplored"

Reliable evaluation of vision-language models requires calibrated confidence, particularly under realistic clinical conditions

"Reliable evaluation of vision-language models (VLMs) and medical vision-language models (Medical-VLMs) requires calibrated confidence, particularly under realistic clinical conditions"

Multi-Class Margin (MCM) regularization achieves lowest Expected Calibration Error on 10 out of 12 in-domain settings and remains competitive under domain shifts

"Multi-Class Margin (MCM) regularization, which achieves lowest ECE on 10 out of 12 settings in in-domain and remains competitive under domain shifts"

MVC-Bench provides a structured evaluation framework and actionable guidance for improving calibration in safety-critical medical workflows

"MVC-Bench provides a structured evaluation framework and actionable guidance for improving calibration in safety-critical medical workflows"

Hidden coordinates in language models are not uniquely determined by the input-output function, making representation measurements non-invariant to function-preserving basis changes

"Hidden coordinates are not uniquely determined by a language model's input--output function, so representation-derived measurements should be invariant to function-preserving changes of basis."

Column-permutation parallel analysis violates function-preserving reparameterization invariance because its reference distribution and component count can change while the model function remains fixed

"column-permutation parallel analysis violates function-preserving reparameterization invariance because its reference distribution and selected component count can change while the model function and observed covariance spectrum remain fixed"

Data-internal reference procedures face fundamental limitations: they cannot simultaneously preserve coordinate marginals, remain orthogonally equivariant, and remove cross-coordinate covariance

"a data-internal reference procedure cannot simultaneously preserve every coordinate marginal, remain orthogonally equivariant, and remove cross-coordinate covariance"

Parallel analysis shows high instability across models and transformations, with median component-count disagreement of 0.79 and decision disagreement of 0.26

"across five models, three retrieval domains, and 75 transformations, median component-count disagreement is 0.79 and median fixed-threshold decision disagreement is 0.26"