Publications associées à P (complexité)

OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression

Pascal Frossard, Thomas Maugey, Roberto Gerson De Albuquerque Azevedo

State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. First, omn ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Tree in Tree: from Decision trees to Decision Graphs

Mahsa Shoaran, Bingzhao Zhu

Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree in Tree decision graph (TnT), a framework that extends the conventional dec ...

Curran Associates, Inc, (NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems)2021

Knapsack and Subset Sum with Small Items

Adam Teodor Polak, Lars Rohwedder

Knapsack and Subset Sum are fundamental NP-hard problems in combinatorial optimization. Recently there has been a growing interest in understanding the best possible pseudopolynomial running times for these problems with respect to various parameters. In t ...

Schloss Dagstuhl -- Leibniz-Zentrum für Informatik2021

Towards Tight Bounds for Spectral Sparsification of Hypergraphs

Mikhail Kapralov, Jakab Tardos

Cut and spectral sparsification of graphs have numerous applications, including e.g. speeding up algorithms for cuts and Laplacian solvers. These powerful notions have recently been extended to hypergraphs, which are much richer and may offer new applicati ...

ASSOC COMPUTING MACHINERY2021

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Volkan Cevher, Shaul Nadav Hallak

This paper develops a methodology for regret minimization with stochastic first-order oracle feedback in online, constrained, non-smooth, non-convex problems. In this setting, the minimization of external regret is beyond reach for first-order methods, and ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Scalable and Efficient Comparison-based Search without Features

Matthias Grossglauser, Lucas Maystre, Daniyar Chumbalov

We consider the problem of finding a target object t using pairwise comparisons, by asking an oracle questions of the form “Which object from the pair (i, j) is more similar to t?”. Objects live in a space of latent features, from which the oracle generate ...

2020

Fast and Space Efficient Spectral Sparsification in Dynamic Streams

Mikhail Kapralov, Jakab Tardos, Navid Nouri, Aidasadat Mousavifar

In this paper, we resolve the complexity problem of spectral graph sparcification in dynamic streams up to polylogarithmic factors. Using a linear sketch we design a streaming algorithm that uses (O) over tilde (n) space, and with high probability, recover ...

ASSOC COMPUTING MACHINERY2020

Fast Transformers with Clustered Attention

François Fleuret, Angelos Katharopoulos, Apoorv Vyas

Transformers have been proven a successful model for a variety of tasks in sequence modeling. However, computing the attention matrix, which is their key component, has quadratic complexity with respect to the sequence length, thus making them prohibitivel ...

2020

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P (complexité)

OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression

Hardware and Software Interfaces Design for Multi-Panel Electrochemical Sensors

Privacy-Preserving Federated Analytics using Multiparty Homomorphic Encryption

Tree in Tree: from Decision trees to Decision Graphs

Knapsack and Subset Sum with Small Items

Towards Tight Bounds for Spectral Sparsification of Hypergraphs

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Scalable and Efficient Comparison-based Search without Features

Fast and Space Efficient Spectral Sparsification in Dynamic Streams

Fast Transformers with Clustered Attention

Graph Chatbot

Chattez avec Graph Search

Knapsack and Subset Sum with Small Items

Hardware and Software Interfaces Design for Multi-Panel Electrochemical Sensors

Towards Tight Bounds for Spectral Sparsification of Hypergraphs

Privacy-Preserving Federated Analytics using Multiparty Homomorphic Encryption

Tree in Tree: from Decision trees to Decision Graphs

Fast Transformers with Clustered Attention

Scalable and Efficient Comparison-based Search without Features

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression

Fast and Space Efficient Spectral Sparsification in Dynamic Streams