Search Results for author:
Found 15 papers, 4 papers with code
Learning-Based Symbol Level Precoding: A Memory-Efficient Unsupervised Learning Approach
Symbol level precoding (SLP) has been proven to be an effective means of managing the interference in a multiuser downlink transmission and also enhancing the received signal power.
An Unsupervised Deep Unfolding Framework for robust Symbol Level Precoding
Symbol Level Precoding (SLP) has attracted significant research interest due to its ability to exploit interference for energy-efficient transmission.
A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights
Our results show that while SLP-DNet provides near-optimal performance, its quantized versions through SQ yield 3. 46x and 2. 64x model compression for binary-based and ternary-based SLP-SQDNets, respectively.
Deep Perceptual Preprocessing for Video Coding
We introduce the concept of rate-aware deep perceptual preprocessing (DPP) for video encoding.
An Unsupervised Learning-Based Approach for Symbol-Level-Precoding
This paper proposes an unsupervised learning-based precoding framework that trains deep neural networks (DNNs) with no target labels by unfolding an interior point method (IPM) proximal `log' barrier function.
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations
We propose a novel mixture-of-experts class to optimize computer vision models in accordance with data transfer limitations at test time.
Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing
The core of our framework comprises a spatial feature learning module, which utilizes residual-graph convolutional neural networks (RG-CNN), for end-to-end learning of appearance-based features directly from graphs.
Complexity-Scalable Neural Network Based MIMO Detection With Learnable Weight Scaling
This paper introduces a framework for systematic complexity scaling of deep neural network(DNN) based MIMO detectors.
Graph-Based Object Classification for Neuromorphic Vision Sensing
Neuromorphic vision sensing (NVS)\ devices represent visual information as sequences of asynchronous discrete events (a. k. a., ``spikes'') in response to changes in scene reflectance.
Deep Video Precoding
We propose to use deep neural networks as precoders for current and future video codecs and adaptive video streaming systems.
Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks
Advanced video classification systems decode video frames to derive the necessary texture and motion representations for ingestion and analysis by spatio-temporal deep convolutional neural networks (CNNs).
Improved Techniques for Adversarial Discriminative Domain Adaptation
Adversarial discriminative domain adaptation (ADDA) is an efficient framework for unsupervised domain adaptation in image classification, where the source and target domains are assumed to have the same classes, but no labels are available for the target domain.
Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor
We demonstrate that selective access to MB motion vector (MV) information within compressed video bitstreams can also provide for selective, motion-adaptive, MB pixel decoding (a. k. a., MB texture decoding).
Voronoi-based compact image descriptors: Efficient Region-of-Interest retrieval with VLAD and deep-learning-based descriptors
Our proposal is a compact image descriptor that combines the state-of-the-art in content-based descriptor extraction with a multi-level, Voronoi-based spatial partitioning of each dataset image.
Vectors of Locally Aggregated Centers for Compact Video Representation
We create vectors of locally aggregated centers (VLAC) by first clustering SIFT features to obtain local feature centers (LFCs) and then encoding the latter with respect to given centers of local feature centers (CLFCs), extracted from a training set.
