The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry

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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 ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

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 ...

We propose a comparative study of three different methods aimed at optimizing existing groundwater monitoring networks. Monitoring piezometric heads in subsurface porous formations is crucial at regional scales to properly characterize the relevant subsurf ...

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 ...

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

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 ...

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 ...

The Internet of Things (IoT) consists of physical objects and devices embedded with network connectivity, software, and sensors to collect and transmit data. The development of the Internet of Things (IoT) has led to various security and privacy issues, in ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their forecast errors are severely limited by the local optimal weight issue in opt ...

This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “3D printing of customizable transient bioelectronics and sensors”. This work aims to study and demonstrate ...

Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, often unknown environmental factors. These challenges call for adaptable learnin ...

This document contains all the data and the details of the analysis used in the manuscript titled " Control of Ge island coalescence for the formation of nanowires on silicon." https://doi.org/10.1039/D3NH00573A ...

Animals have to navigate complex environments and perform intricate swimming maneuvers in the real world. To conquer these challenges, animals evolved a variety of motion control strategies. While it is known that many factors contribute to motion control, ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

Neurons in the human amygdala and hippocampus are classically thought to encode a person’s identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortical areas is translated into such a semanti ...

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

This paper presents a novel hybrid framework for generating and updating a synthetic population. We call it hybrid because it combines model-based and data-driven approaches. Existing generators produce a snapshot of synthetic data that becomes outdated ov ...

This thesis explores the how computational methods can be used to enrich the history of architecture. Using Venice as a case study, it establishes a rich and multifaceted information system, made up of temporal, stylistic, photographic, architectural, and ...

The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry

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

Understanding generalization and robustness in modern deep learning

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

Data-driven statistical optimization of a groundwater monitoring network

Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Simulating Interacting Quantum Systems with Near-Term Quantum Computers

Random matrix methods for high-dimensional machine learning models

GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Improving the accuracy and interpretability of neural networks for wind power forecasting

Dataset for '3D printing of customizable transient bioelectronics and sensors'

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics

Data set for control of Ge island coalescence for the formation of nanowires on silicon

Using deep reinforcement learning to investigate stretch feedback during swimming of the lamprey

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus

Robust NAS under adversarial training: benchmark, theory, and beyond

Hybrid Simulator for Capturing Dynamics of Synthetic Populations

The Skin of Venice: A Computational Approach to the City's Urban Structure