Publications associées à Algorithme de Viterbi

Gibbs sampling the posterior of neural networks

Lenka Zdeborová, Emanuele Troiani, Giovanni Piccioli

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

Iop Publishing Ltd2024

High-Throughput and Flexible Belief Propagation List Decoder for Polar Codes

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Andreas Toftegaard Kristensen, Yifei Shen, Yuqing Ren, Chuan Zhang

Due to its high parallelism, belief propagation (BP)decoding is amenable to high-throughput applications and thusrepresents a promising solution for the ultra-high peak datarate required by future communication systems. To bridge theperformance gap compare ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Dynamic SCL Decoder With Path-Flipping for 5G Polar Codes

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Yifei Shen, Chuan Zhang

Since polar were ratified as part of the 5G standard, low-complexity polar decoders with close-to-optimum error-rate performance have received significant attention. Compared to successive cancellation (SC) decoding, both SC list and SC flip decoding can i ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Symmetry in design and decoding of polar-like codes

Kirill Ivanov

The beginning of 21st century provided us with many answers about how to reach the channel capacity. Polarization and spatial coupling are two techniques for achieving the capacity of binary memoryless symmetric channels under low-complexity decoding algor ...

EPFL2022

Fast Block Sequential Decoding of Polar Codes

Kirill Ivanov

A reduced complexity sequential decoding algorithm for polar (sub) codes is described. The proposed approach relies on a decomposition of the polar (sub) code being decoded into a number of outer codes, and on-demand construction of codewords of these code ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Modeling time-varying brain networks with a self-tuning optimized Kalman filter

David Pascucci, Gijs Plomp

Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise ...

2020

PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization

Martin Jaggi, Thijs Vogels

We study lossy gradient compression methods to alleviate the communication bottleneck in data-parallel distributed optimization. Despite the significant attention received, current compression schemes either do not scale well, or fail to achieve the target ...

2019

From LDPC Block to LDPC Convolutional Codes: Capacity, Stability, and Universality

Wei Liu

In his landmark paper "A Mathematical Theory of Communication," the founding father of information theory and coding theory, Claude E. Shannon, established the largest rate at which reliable communication is possible and he revealed that the key to this re ...

EPFL2019

High-Speed Wireline Link Design

Hazar Yüksel

High-speed serial links are a crucial application of semiconductor technology and have been the enabler of the scaling of computing systems. The increasing data-rate requirements of these links have only been partially satisfied by advancements in process ...

EPFL2017

Lower Bounds on Active Learning for Graphical Model Selection

Volkan Cevher, Jonathan Mark Scarlett

We consider the problem of estimating the underlying graph associated with a Markov random field, with the added twist that the decoding algorithm can iteratively choose which subsets of nodes to sample based on the previous samples, resulting in an active ...

2017