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 ...

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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 ...

High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessin ...

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 ...

Derived from the Karush–Kuhn–Tucker (KKT) optimality conditions of utility maximization, the multiple discrete–continuous extreme value (MDCEV) models are extensively used in the field of time use. This is because they are grounded in a framework that maxi ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

We study a model for the initial state of the universe based on a gravitational path integral that includes connected geometries which simultaneously produce bra and ket of the wave function. We argue that a natural object to describe this state is the Wig ...

Using ultrafast broad-band transient absorption (TA) spectroscopy of photoexcited MAPbBr3 thin films with probe continua in the visible and the mid- to deep-ultraviolet (UV) ranges, we capture the ultrafast renormalization at the fundamental gap at the R s ...

FDTD/FEM simulations, strengthened by machine learning, achieve 95.44% optical absorption efficiency in TiO 2 /TiN plasmonic nanocavities for solar energy harvesting. ...

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Mitigating the energy requirements of artificial intelligence requires novel physical substrates for computation. Phononic metamaterials have vanishingly low power dissipation and hence are a prime candidate for green, always-on computers. However, their u ...

In less than a century, electronic computers have become essential to everyday life and have transformed scientific research. Their use in solving scientific problems has integrated theoretical and experimental approaches, often blurring the lines between ...

Colloid particle size plays an important role in contaminant adsorption and clogging in the hyporheic zone, but it remains unclear how the particle size changes during the transport of colloids. This study investigated the variation of the particle size of ...

Facial paralysis is a highly burdening condition, resulting in a patient's inability to move his musculature on one or both sides of the mouth. The restoration of facial movement is crucial for enhancing the overall patients' quality of life. The use of DE ...

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

Chattez avec Graph Search

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

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On the Generalization of Stochastic Gradient Descent with Momentum