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Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML

- Numerical approximations of partial differential equations (PDEs) are routinely employed to formulate the solution of physics, engineering, and mathematical problems involving functions of several...

Graph Neural Networks for Graphs with Heterophily: A Survey

- Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriad graph analytic tasks and applications. Most GNNs rely on the homophily assumption that nodes...

Conductance and Influence-Capital: Modeling Online Social Influence

- Human interactions are mediated by social influence. During crises like the COVID-19 pandemic, social influence determines whether life-saving information is adopted or immunization campaigns meet...

Improved Guarantees for Offline Stochastic Matching via New Ordered Contention Resolution Schemes

- Matching is one of the most fundamental and broadly applicable problems across many domains. In these diverse real-world applications, there is often a degree of uncertainty in the input which has...

Revisiting Active Sequential Prediction-Powered Mean Estimation

- In this work, we revisit the problem of active sequential prediction-powered mean estimation, where at each round one must decide the query probability of the ground-truth label upon observing the...

FUSE: Ensembling Verifiers with Zero Labeled Data

- Verification of model outputs is rapidly emerging as a key primitive for both training and real-world deployment of large language models (LLMs). In practice, this often involves using imperfect LLM...

Learning the Riccati solution operator for time-varying LQR via Deep Operator Networks

- We propose a computational framework for replacing the repeated numerical solution of differential Riccati equations in finite-horizon Linear Quadratic Regulator (LQR) problems by a learned operator...

Spectral bandits for smooth graph functions

- Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is...

Momentum Stability and Adaptive Control in Stochastic Reconfiguration

- Variational Monte Carlo (VMC) combined with expressive neural network wavefunctions has become a powerful route to high-accuracy ground-state calculations, yet its practical success hinges on...

Predictive Modeling of Natural Medicinal Compounds for Alzheimer Disease Using Cheminformatics

- The most common cause of dementia is Alzheimer disease, a progressive neurodegenerative disorder affecting older adults that gradually impairs memory, cognition, and behavior. It is characterized by...

Symmetry Guarantees Statistic Recovery in Variational Inference

- Variational inference (VI) is a central tool in modern machine learning, used to approximate an intractable target density by optimising over a tractable family of distributions. As the variational...

On quantum functionals for higher-order tensors

- Upper and lower quantum functionals, introduced by Christandl, Vrana and Zuiddam (STOC 2018, J. Amer. Math. Soc. 2023), are families of monotone functions of tensors indexed by a weighting on the set...

Block-encodings as programming abstractions: The Eclipse Qrisp BlockEncoding Interface

- Block-encoding is a foundational technique in modern quantum algorithms, enabling the implementation of non-unitary operations by embedding them into larger unitary matrices. While theoretically...

DeepRitzSplit Neural Operator for Phase-Field Models via Energy Splitting

- The multi-scale and non-linear nature of phase-field models of solidification requires fine spatial and temporal discretization, leading to long computation times. This could be overcome with...

Incremental learning for audio classification with Hebbian Deep Neural Networks

- The ability of humans for lifelong learning is an inspiration for deep learning methods and in particular for continual learning. In this work, we apply Hebbian learning, a biologically inspired...

mlr3torch: A Deep Learning Framework in R based on mlr3 and torch

- Deep learning (DL) has become a cornerstone of modern machine learning (ML) praxis. We introduce the R package mlr3torch, which is an extensible DL framework for the mlr3 ecosystem. It is built upon...

Self-referentiality and asymmetric knowledge flows between journals. The case of economics

- This paper investigates the evolution of self-referentiality and knowledge flows in economics journals before and after the 2008 financial crisis. Using a multi-level approach, we analyze patterns at...

Distributional Off-Policy Evaluation with Deep Quantile Process Regression

- This paper investigates the off-policy evaluation (OPE) problem from a distributional perspective. Rather than focusing solely on the expectation of the total return, as in most existing OPE methods,...

NIM4-ASR: Towards Efficient, Robust, and Customizable Real-Time LLM-Based ASR

- Integrating large language models (LLMs) into automatic speech recognition (ASR) has become a mainstream paradigm in recent years. Although existing LLM-based ASR models demonstrate impressive...

Joint Detection and Velocity Estimation in OFDM-ISAC Cell-Free Massive MIMO Networks

- This paper develops a Doppler-aware sensing framework for cell-free massive MIMO (CF-mMIMO) networks operating under OFDM-based integrated sensing and communication (ISAC). The framework explicitly...

Boltzmann Machine Learning with a Parallel, Persistent Markov chain Monte Carlo method for Estimating Evolutionary Fields and Couplings from a Protein Multiple Sequence Alignment

- The inverse Potts problem for estimating evolutionary single-site fields and pairwise couplings in homologous protein sequences from their single-site and pairwise amino acid frequencies observed in...