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3,048 papers
It is shown decoder-only MDMs, despite a larger modeling space, can achieve significant inference speedups and comparable perplexity with techniques like temperature annealing, offering a path to reduced inference compute.
A multi-view linear Structural Equation Model (SEM) is proposed that extends the well-known framework of non-Gaussian disturbances by alternatively leveraging correlation over views and proves the identifiability of the model for acyclic SEMs.
Quantized ABBA (QABBA), a quantized version of ABBA, provides an error-controlled, low-precision symbolic representation for time-series compression and LLM-based analysis.
This paper forms the resulting $\ell_0$-constrained empirical newsvendor problem with $\ell_2$-regularization, establishes its computational hardness, and develops a mixed-integer second-order cone programming reformulation that strengthens the standard Big-$M$ formulation.
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This work establishes generative augmentation as a distributional perturbation process whose reliability can be quantified through Wasserstein-based measures and supported by finite-sample generalization guarantees.
This work addresses the inverse problem of matched score queries at noise scales $\sigma$ and $\lambda\sigma$ using matched subtraction to establish a perturbation bound and a conditional kernel-density-estimation rate.
This paper formalizes a mathematical framework for the Artificial Rosetta Stone, proposing the Artificial Rosetta Stone (ARS), and separates three claims often conflated: a symbolic sequence can be reconstructed probabilistically; a sequence can be consistent with an explicit grammar; and a historical performance can b...
Numerical studies and a semi-synthetic analysis based on hard-drive failure data illustrate potential reductions in expected error rate and improvements in decision-boundary estimation from modelling feature-dependent label missingness.
A general convolutional neural network framework for detecting heterogeneous event-driven dynamics in univariate time series windows and it is shown that the induced CNN class exactly represents classifiers based on range, maximum drawup, maximum drawdown and slope change, and uniformly approximates realised volatility...
This work shows that diffusion models learned with standard denoising loss can provide effective global MCMC proposals for complex high-dimensional target densities and offers preliminary evidence that the established scaling behavior of standard diffusion training transfers directly to exact sampling from high-dimensi...
It is shown that one-layer transformers with an argmax classification head behave identically to a one-nearest-neighbor classifier in the multiclass setting by leveraging the simplex encoding.
It is proved that a multi-head scaled dot product attention can be viewed as a parameter identification strategy, and a purely mathematical/statistical explanation, identification, for why specific architectural choices in transformers may have improved performance is discussed.
General Science
Accelerating the search for novel antimicrobial peptides using Google Antigravity for Science.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
General Science
General Science
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