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Check Out This Place: Inferring Ambiance from Airbnb Photos
Related publications (33)
Benign Overfitting in Deep Neural Networks under Lazy Training
Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Zhenyu Zhu
This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve Bayesoptimal test error for classification while obtaining (nearly) zero-trai ...
2023Deep Learning Generalization with Limited and Noisy Labels
Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...
EPFL2023ReLU Neural Network Galerkin BEM
Fernando José Henriquez Barraza
We introduce Neural Network (NN for short) approximation architectures for the numerical solution of Boundary Integral Equations (BIEs for short). We exemplify the proposed NN approach for the boundary reduction of the potential problem in two spatial dime ...
SPRINGER/PLENUM PUBLISHERS2023An exact mapping from ReLU networks to spiking neural networks
Wulfram Gerstner, Stanislaw Andrzej Wozniak, Ana Stanojevic, Giovanni Cherubini, Angeliki Pantazi
Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an ...
2023From Kernel Methods to Neural Networks: A Unifying Variational Formulation
The minimization of a data-fidelity term and an additive regularization functional gives rise to a powerful framework for supervised learning. In this paper, we present a unifying regularization functional that depends on an operator L\documentclass[12pt]{ ...
New York2023Generalization Properties of NAS under Activation and Skip Connection Search
Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Zhenyu Zhu
Neural Architecture Search (NAS) has fostered the automatic discovery of stateof- the-art neural architectures. Despite the progress achieved with NAS, so far there is little attention to theoretical guarantees on NAS. In this work, we study the generaliza ...
2022Global information processing in feedforward deep networks
Michael Herzog, Ben Henrik Lönnqvist, Adrien Christophe Doerig, Alban Bornet
While deep neural networks are state-of-the-art models of many parts of the human visual system, here we show that they fail to process global information in a humanlike manner. First, using visual crowding as a probe into global visual information process ...
2022Improving the Training of Compact Neural Networks for Visual Recognition
During the Artificial Intelligence (AI) revolution of the past decades, deep neural networks have been widely used and have achieved tremendous success in visual recognition. Unfortunately, deploying deep models is challenging because of their huge model s ...
EPFL2022Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models
Deep neural networks have completely revolutionized the field of machinelearning by achieving state-of-the-art results on various tasks ranging fromcomputer vision to protein folding. However, their application is hindered bytheir large computational and m ...
EPFL2022Sharp asymptotics on the compression of two-layer neural networks
In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M < N nodes. More precisely, we consider the setting in which the weights of the target network are i.i.d. sub-Gaussian, and we minimiz ...
IEEE2022