Related papers: NANCY: Neural Adaptive Network Coding methodologY …

The quality of experience (QoE) delivered by video conferencing systems is significantly influenced by accurately estimating the time-varying available bandwidth between the sender and receiver. Bandwidth estimation for real-time…

Random network coding (RNC) is an efficient coding scheme to improve the performance of the broadband networks, especially for multimedia applications which are popular in 5G network. However, it is a challenging work to transmit the real…

We present Adaptive Memory Networks (AMN) that processes input-question pairs to dynamically construct a network architecture optimized for lower inference times for Question Answering (QA) tasks. AMN processes the input story to extract…

Artificial Neural Networks (ANNs) are a promising approach to the decoding problem of Quantum Error Correction (QEC), but have observed consistent difficulty when generalising performance to larger QEC codes. Recent scalability-focused…

This paper introduces an adaptive convolutional neural network (CNN) architecture capable of automating various topology optimization (TO) problems with diverse underlying physics. The proposed architecture has an encoder-decoder-type…

Efficient image compression relies on modeling both local and global redundancy. Most state-of-the-art (SOTA) learned image compression (LIC) methods are based on CNNs or Transformers, which are inherently rigid. Standard CNN kernels and…

Nowadays Dynamic Adaptive Streaming over HTTP (DASH) is the most prevalent solution on the Internet for multimedia streaming and responsible for the majority of global traffic. DASH uses adaptive bit rate (ABR) algorithms, which select the…

The problem of characterizing the optimal rate achievable with analog network coding (ANC) for a unicast communication over general wireless relay networks is computationally hard. A relay node performing ANC scales and forwards its input…

Attention improves representation learning over RNNs, but its discrete nature limits continuous-time (CT) modeling. We introduce Neuronal Attention Circuit (NAC), a novel, biologically inspired CT-Attention mechanism that reformulates…

Convolutional Neural Networks (CNN) outperform traditional classification methods in many domains. Recently these methods have gained attention in neuroscience and particularly in brain-computer interface (BCI) community. Here, we introduce…

This paper introduces a novel dynamic optimization framework for video streaming that leverages Network Digital Twin (NDT) technology to address the challenges posed by fluctuating wireless network conditions. Traditional adaptive streaming…

With the emergence of multiple modern video codecs, streaming service providers are forced to encode, store, and transmit bitrate ladders of multiple codecs separately, consequently suffering from additional energy costs for encoding,…

The rapid growth in video consumption has introduced significant challenges to modern streaming architectures. Over-the-Top (OTT) video delivery now predominantly relies on Adaptive Bitrate (ABR) streaming, which dynamically adjusts bitrate…

Adaptive bitrate streaming enables video users to adapt their playing bitrates to the real-time network conditions, hence achieving the desirable quality-of-experience (QoE). In a multi-user wireless scenario, however, existing single-user…

Adaptive bitrate (ABR) streaming enables video users to adapt the playing bitrate to the real-time network conditions to achieve the desirable quality of experience (QoE). In this work, we propose a novel crowdsourced streaming framework…

Convolutional neural network (CNN) delivers impressive achievements in computer vision and machine learning field. However, CNN incurs high computational complexity, especially for vision quality applications because of large image…

To optimize the perceived quality under a specific bitrate constraint, multi-pass encoding is usually performed with the rate control mode of the average bitrate (ABR) or the constant rate factor (CRF) to distribute bits as reasonably as…

Sentence compression is a Natural Language Processing (NLP) task aimed at shortening original sentences and preserving their key information. Its applications can benefit many fields e.g. one can build tools for language education. However,…

Multi-hop networks become popular network topologies in various emerging Internet of things applications. Batched network coding (BNC) is a solution to reliable communications in such networks with packet loss. By grouping packets into…

Binary neural network (BNN) is an extreme quantization version of convolutional neural networks (CNNs) with all features and weights mapped to just 1-bit. Although BNN saves a lot of memory and computation demand to make CNN applicable on…