Unbiased Monte Carlo cluster updates with autoregressive neural networks

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Contemporary AI faces important limitations that remain unresolved despite significant successes. Chief among these are the inability to acquire new knowledge without destroying old, the incomprehensible and nonengineerable nature of internal representatio ...

Point identification of causal effects requires strong assumptions that are unreasonable in many practical settings. However, bounds on these effects can often be derived under plausible assumptions. Even when these bounds are wide or cover null effects, t ...

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Under resource constraints, LLMs are usually fine- tuned with additional knowledge using Parameter Efficient Fine-Tuning (PEFT), using Low-Rank Adaptation (LoRA) modules. In fact, LoRA injects a new set of small trainable matrices to adapt an LLM to a new ...

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Under resource constraints, LLMs are usually fine-tuned with additional knowledge using Parameter Efficient Fine-Tuning (PEFT), using Low-Rank Adaptation (LoRA) modules. In fact, LoRA injects a new set of small trainable matrices to adapt an LLM to a new t ...

Real-time bioaerosol monitoring is improving the quality of life for people affected by allergies, but it often relies on deep learning models which pose challenges for widespread adoption. These models are typically trained in a supervised fashion and req ...

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While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

Unbiased Monte Carlo cluster updates with autoregressive neural networks

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Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Improved bounds and inference on optimal regimes

Instabilities in Elastic and Magneto-Elastic Beams and Shells: from Shell Buckling to the Design of a Programmable Braille Reader

Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment

Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

Probing the latent hierarchical structure of data via diffusion models

Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

Self-supervised and few-shot learning for robust bioaerosol monitoring

A new flexible multiple discrete–continuous extreme value (MDCEV) choice model: exploring non-monotonic preference in time use decisions

Random matrix methods for high-dimensional machine learning models

De Sitter Bra-Ket wormholes

Band Gap Renormalization at Different Symmetry Points in Perovskites

Machine-learning-empowered FDTD/FEM simulations for predictive solar energy absorption in plasmonic metamaterial nanocavity arrays

An immersive virtual reality tool for assessing left and right unilateral spatial neglect

In-Sensor Passive Speech Classification with Phononic Metamaterials

Simulating Interacting Quantum Systems with Near-Term Quantum Computers

Effects of Hyporheic Exchange and Settlement on the Particle Size Distribution of Colloids

DEMO: Dielectric elastomer actuators (DEAs) for the restoration of facial movements

On the Generalization of Stochastic Gradient Descent with Momentum