On Locating Byzantine Attackers

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Information theory has allowed us to determine the fundamental limit of various communication and algorithmic problems, e.g., the channel coding problem, the compression problem, and the hypothesis testing problem. In this work, we revisit the assumptions ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

In the last decade, DNA has been increasingly investigated as an alternative medium for cold data storage, presenting several advantages over standard hard drives such as a higher density, longer lifespan and lower energy consumption. However, such coding ...

The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in ...

The way biological brains carry out advanced yet extremely energy efficient signal processing remains both fascinating and unintelligible. It is known however that at least some areas of the brain perform fast and low-cost processing relying only on a smal ...

Artificial Neural Networks (ANN) are habitually trained via the back-propagation (BP) algorithm. This approach has been extremely successful: Current models like GPT-3 have O(10 11 ) parameters, are trained on O(10 11 ) words and produce awe-inspiring resu ...

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 ...

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 ...

This paper presents an ultra-high-throughput decoder architecture for NB-LDPC codes based on the Hybrid Extended Min-Sum algorithm. We introduce a new processing block that updates a check node and its associated variable nodes in a fully pipelined way, th ...

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal rep ...

Locally correctable codes (LCCs) are error correcting codes C : \Sigmak \rightarrow \Sigman which admit local algorithms that correct any individual symbol of a corrupted codeword via a minuscule number of queries. For systematic codes, this notion is stro ...

The cerebellum is thought to help detect and correct errors between intended and executed commands(1,2) and is critical for social behaviours, cognition and emotion 3 Computations for motor control must be performed quicklyto correct errors in real time an ...

Developing energy-saving neural network models is a topic of rapidly increasing interest in the artificial intelligence community. Spiking neural networks (SNNs) are biologically inspired models that strive to leverage the energy efficiency stemming from a ...

The de-facto standard decoding algorithm for polar codes, successive cancellation list (SCL) decoding, is a breadth-first search algorithm. By keeping a list of candidate codewords, SCL decoding improves the performance as the list size L increases. Howeve ...

The Dolev-Reischuk bound says that any deterministic Byzantine consensus protocol has (at least) quadratic communication complexity in the worst case. While it has been shown that the bound is tight in synchronous environments, it is still unknown whether ...

LoRaWAN is nowadays one of the most popular protocols for low-power Internet of Things communications. Although its physical layer, namely LoRa, has been thoroughly studied in the literature, aspects related to the synchronization of LoRa receivers have re ...

How does reliable computation emerge from networks of noisy neurons? While individual neurons are intrinsically noisy, the collective dynamics of populations of neurons taken as a whole can be almost deterministic, supporting the hypothesis that, in the br ...

To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-w ...

Goods can exhibit positive externalities impacting decisions of customers in social networks. Suppliers can integrate these externalities in their pricing strategies to increase their revenue. Besides optimizing the prize, suppliers also have to consider t ...

Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstruction algorithms under generative priors is, however, challenging. For generati ...

On Locating Byzantine Attackers

Chattez avec Graph Search

On Speed and Advantage : Results in Information Velocity and Monitoring Problems

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Analysis of the influence of errors in DNA-based image coding

Bayesian reconstruction of memories stored in neural networks from their connectivity

Supervised learning and inference of spiking neural networks with temporal coding

Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

An exact mapping from ReLU networks to spiking neural networks

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

Ultra-High-Throughput EMS NB-LDPC Decoder with Full-Parallel Node Processing

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity

Relaxed Locally Correctable Codes With Nearly-Linear Block Length And Constant Query Complexity

Structured cerebellar connectivity supports resilient pattern separation

Approximating Relu Networks By Single-Spike Computation

List Ordered Statistics Decoders for Polar Codes

Byzantine Consensus is Θ(n^2): The Dolev-Reischuk Bound is Tight even in Partial Synchrony!

A Low-Complexity LoRa Synchronization Algorithm Robust to Sampling Time Offsets

Taming neuronal noise with large networks

Learning of physical systems: from inference to control

Maintenance scheduling of manufacturing systems based on optimal price of the network

Multi-layer State Evolution Under Random Convolutional Design