Publications associées à TensorFlow

A Non-Exponential Transmittance Model for Volumetric Scene Representations

Wenzel Alban Jakob, Delio Aleardo Vicini

We introduce a novel transmittance model to improve the volumetric representation of 3D scenes. The model can represent opaque surfaces in the volumetric light transport framework. Volumetric representations are useful for complex scenes, and become increa ...

ASSOC COMPUTING MACHINERY2021

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Nicolas Henri Bernard Flammarion, Scott William Pesme, Loucas Pillaud-Vivien

Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the dynamics of stochastic gradient descent over diagonal linear networks through i ...

2021

Equinox: Training (for Free) on a Custom Inference Accelerator

Babak Falsafi, Martin Jaggi, Louis Coulon, Ahmet Caner Yüzügüler, Mario Paulo Drumond Lages De Oliveira, Arash Pourhabibi Zarandi

DNN inference accelerators executing online services exhibit low average loads because of service demand variability, leading to poor resource utilization. Unfortunately, reclaiming idle inference cycles is difficult as other workloads can not execute on a ...

ACM2021

Solving frustrated Ising models using tensor networks

Frédéric Mila, Jeanne Colbois

Motivated by the recent success of tensor networks to calculate the residual entropy of spin ice and kagome Ising models, we develop a general framework to study frustrated Ising models in terms of infinite tensor networks that can be contracted using stan ...

AMER PHYSICAL SOC2021

System and method for privacy-preserving distributed training of machine learning models on distributed datasets

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean-Philippe Léonard Bossuat, Apostolos Pyrgelis, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Sinem Sav

A computer-implemented method and a distributed computer system (100) for privacy- preserving distributed training of a global model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being communicatively coupl ...

2021

Artificial out-of-plane Ising antiferromagnet on the kagome lattice with very small farther-neighbor couplings

Frédéric Mila, Jeanne Colbois, Xueqiao Wang

Despite their simple formulation, short-range classical antiferromagnetic Ising models on frustrated lattices give rise to exotic phases of matter, in particular, due to their macroscopic ground-state degeneracy. Recent experiments on artificial spin syste ...

AMER PHYSICAL SOC2021

Max-infinitely divisible models and inference for spatial extremes

Emeric Rolland Georges Thibaud, Raphaël Huser

For many environmental processes, recent studies have shown that the dependence strength is decreasing when quantile levels increase. This implies that the popular max-stable models are inadequate to capture the rate of joint tail decay, and to estimate jo ...

WILEY2020

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Michaël Unser, Shayan Aziznejad, Harshit Gupta, Joaquim Gonçalves Garcia Barreto Campos

We introduce a variational framework to learn the activation functions of deep neural networks. Our aim is to increase the capacity of the network while controlling an upper-bound of the actual Lipschitz constant of the input-output relation. To that end, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

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TensorFlow

A Non-Exponential Transmittance Model for Volumetric Scene Representations

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Measuring the Impact of Model and Input Heterogeneity in Personalized Federated Learning

Equinox: Training (for Free) on a Custom Inference Accelerator

Solving frustrated Ising models using tensor networks

System and method for privacy-preserving distributed training of machine learning models on distributed datasets

Artificial out-of-plane Ising antiferromagnet on the kagome lattice with very small farther-neighbor couplings

pyannote.audio: neural building blocks for speaker diarization

Max-infinitely divisible models and inference for spatial extremes

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Graph Chatbot

Chattez avec Graph Search

Solving frustrated Ising models using tensor networks

Max-infinitely divisible models and inference for spatial extremes

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Measuring the Impact of Model and Input Heterogeneity in Personalized Federated Learning

System and method for privacy-preserving distributed training of machine learning models on distributed datasets

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

A Non-Exponential Transmittance Model for Volumetric Scene Representations

Artificial out-of-plane Ising antiferromagnet on the kagome lattice with very small farther-neighbor couplings

pyannote.audio: neural building blocks for speaker diarization

Equinox: Training (for Free) on a Custom Inference Accelerator