Distribution Testing with a Confused Collector

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Deep heteroscedastic regression models the mean and covariance of the target distribution through neural networks. The challenge arises from heteroscedasticity, which implies that the covariance is sample dependent and is often unknown. Consequently, recen ...

Many empirical studies have provided evidence for the emergence of algorithmic mechanisms (abilities) in the learning of language models, that lead to qualitative improvements in the model capabilities. Yet, a theoretical characterization of how such mecha ...

Differences in acquisition protocols or hardware result in measurable changes in image characteristics. These differences affect distributional properties and can also affect the spatial and temporal characteristics of the images. Likewise, the distributio ...

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 ...

As one of the major threats to women's health worldwide, breast cancer requires early diagnosis and accurate classification, since they are key to optimizing therapeutic interventions and ensuring precise prognosis. Recently, deep learning has demonstrated ...

Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been p ...

Multi-frequency and Doppler spectral radars are valuable tools for retrieving information on the microphysics of precipitation, particularly snowfall. However, estimating microphysical properties from radar measurements is an ill-posed problem, i.e., diffe ...

Significant damages caused by lightning can be avoided by predicting lightning and taking precautionary measures. Here, a machine learning model is developed to nowcast lightning flashes using dew point temperature, precipitation, wind speed, wind directio ...

This paper investigates the fundamental regression task of learning k neurons (a.k.a. teachers) from Gaussian input, using two-layer ReLU neural networks with width m (a.k.a. students) and m, k = O(1), trained via gradient descent under proper initializati ...

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

We present a reinforcement learning framework for constructing assemblies composed of rigid parts, which are commonly seen in many historical masonry buildings and bridges. Traditional construction methods for such structures often depend on dense scaffold ...

Objectives: Endometrial preparation provides significant surgical benefits prior to hysteroscopic procedures. However, there is still no consensus on the optimal presurgical protocol. Although there is evidence on rapid preparation, there are currently no ...

Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables. Recent applications of deep learning to SDMs have enabled new avenues, specifically the inclusion of spatial data (envir ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

In this paper, we perform the joint study of scale -invariant operators and self -similar processes of complex order. More precisely, we introduce general families of scale -invariant complex -order fractional -derivation and integration operators by const ...

Reinforcement learning (RL) is inherently rife with non-stationarity since the states and rewards the agent observes during training depend on its changing policy. Therefore, networks in deep RL must be capable of adapting to new observations and fitting n ...

Distribution Testing with a Confused Collector

Chattez avec Graph Search

Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression

A phase transition between positional and semantic learning in a solvable model of dot-product attention

Domain shift, domain adaptation, and generalization: A focus on MRI

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

Mspo: a Machine Learning Hyperparameter Optimization Method for Enhanced Breast Cancer Image Classification

Eflop: a Sparsity-aware Metric for Evaluating Computational Cost in Spiking and Non-spiking Neural Networks

What can radars tell us about snowfall microphysics? Insights from a Markov chain Monte-Carlo approach

Exploring the portability of ML-based lightning nowcasting models

How Gradient Descent Balances Features: A Dynamical Analysis For Two-Layer Neural Networks

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

Learning to Assemble with Alternative Plans

Oral Drospirenone/estetrol as Rapid and Random Start Endometrial Preparation Before Office Hysteroscopic Polypectomy: a Multicenter, Prospective, Randomized Controlled Trial

Multi-Scale and Multimodal Species Distribution Modeling

Spectral Estimators for High-Dimensional Matrix Inference

Complex-order scale-invariant operators and self-similar processes

No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO