Publications associées à Model-Augmented Conditional Mutual Information Estimation for Feature Selection

Publications associées (12)

Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression

Mathieu Salzmann, Alexandre Massoud Alahi, Megh Hiren Shukla

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 ...

ICLR2025

Why computational complexity may set impenetrable barriers for epistemic reductionism

Michael Herzog, Adrien Christophe Doerig, Christian Sachse

According to physicalism, everything is physical or metaphysically connected to the physical. If physicalism were true, it seems that we should - in principle - be able to reduce the descriptions and explanations of special sciences to physical ones, for e ...

Springer2023

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

Daniel Probst

While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the sci ...

2023

Kernel Density Estimation through Density Constrained Near Neighbor Search

Mikhail Kapralov, Navid Nouri

In this paper we revisit the kernel density estimation problem: given a kernel K(x, y) and a dataset of n points in high dimensional Euclidean space, prepare a data structure that can quickly output, given a query q, a (1 + epsilon)-approximation to mu := ...

IEEE2020

Streaming Principal Component Analysis From Incomplete Data

Armin Eftekhari

Linear subspace models are pervasive in computational sciences and particularly used for large datasets which are often incomplete due to privacy issues or sampling constraints. Therefore, a critical problem is developing an efficient algorithm for detecti ...

Microtome Publishing2019

Fast Robust PCA on Graphs

Pierre Vandergheynst, Gilles Puy, Nathanaël Perraudin, Vasilis Kalofolias, Nauman Shahid

Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the c ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Learning Search Strategies from Human Demonstrations

Guillaume Pierre Luc De Chambrier

Decision making and planning with partial state information is a problem faced by all forms of intelligent entities. The formulation of a problem under partial state information leads to an exorbitant set of choices with associated probabilistic outcomes m ...

EPFL2016

Boosting Localized Features for Speaker and Speech Recognition

Anindya Roy

In this thesis, we propose a novel approach for speaker and speech recognition involving localized, binary, data-driven features. The proposed approach is largely inspired by similar localized approaches in the computer vision domain. The success of these ...

EPFL2011

Particle Swarm Optimization of Memory usage in Embedded Systems

David Atienza Alonso

In this paper, we propose a dynamic, non-dominated sorting, multi-objective particle-swarm-based optimizer, named Hierarchical Non-dominated Sorting Particle Swarm Optimizer (H-NSPSO), for memory usage optimization in embedded systems. It significantly red ...

2009

Evaluating compound critiquing recommenders: A real-user study

Pearl Pu Faltings, Jiyong Zhang

Conversational recommender systems are designed to help users to more efficiently navigate complex product spaces by alternatively making recommendations and inviting users' feedback. Compound critiquing techniques provide an efficient way for users to fee ...

Association for Computing Machinery, New York, NY 10036-5701, United States2007

Variations of coloring problems related to scheduling and discrete tomography

Bernard Ries

The graph coloring problem is one of the most famous problems in graph theory and has a large range of applications. It consists in coloring the vertices of an undirected graph with a given number of colors such that two adjacent vertices get different col ...

EPFL2007

Comparing Algorithms for Reconstructing Digital Off-Axis Fresnel Holograms

Michaël Unser, Michael Stefan Daniel Liebling

Three-dimensional information about an object, such as its depth, may be captured and stored digitally in a single, two-dimensional, real-valued hologram acquired in an off-axis geometry. Digital reconstruction of the hologram permits the quantitative retr ...

SPIE2005