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Found 30 papers, 19 papers with code
Importance Sampling via Score-based Generative Models
Importance sampling, which involves sampling from a probability density function (PDF) proportional to the product of an importance weight function and a base PDF, is a powerful technique with applications in variance reduction, biased or customized sampling, data augmentation, and beyond.
Neural Cover Selection for Image Steganography
In steganography, selecting an optimal cover image, referred to as cover selection, is pivotal for effective message concealment.
Generating High Dimensional User-Specific Wireless Channels using Diffusion Models
Deep neural network (DNN)-based algorithms are emerging as an important tool for many physical and MAC layer functions in future wireless communication systems, including for large multi-antenna channels.
Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models
We extend our experiments to a small natural language dataset to further confirm our findings on our synthetic dataset.
Enhancing K-user Interference Alignment for Discrete Constellations via Learning
We propose a novel deep learning-based approach to design the encoder and decoder functions that aim to maximize the sumrate of the interference channel for discrete constellations.
Local to Global: Learning Dynamics and Effect of Initialization for Transformers
In this paper, we address this by focusing on first-order Markov chains and single-layer transformers, providing a comprehensive characterization of the learning dynamics in this context.
LightCode: Light Analytical and Neural Codes for Channels with Feedback
The design of reliable and efficient codes for channels with feedback remains a longstanding challenge in communication theory.
DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
Progress in designing channel codes has been driven by human ingenuity and, fittingly, has been sporadic.
Attention with Markov: A Framework for Principled Analysis of Transformers via Markov Chains
Inspired by the Markovianity of natural languages, we model the data as a Markovian source and utilize this framework to systematically study the interplay between the data-distributional properties, the transformer architecture, the learnt distribution, and the final model performance.
LASER: Linear Compression in Wireless Distributed Optimization
On the latter, we obtain $50$-$64 \%$ improvement in perplexity over our baselines for noisy channels.
Task-aware Distributed Source Coding under Dynamic Bandwidth
A decoder at the central node decompresses and passes the data to a pre-trained machine learning-based task to generate the final output.
TinyTurbo: Efficient Turbo Decoders on Edge
In this paper, we introduce a neural-augmented decoder for Turbo codes called TINYTURBO .
Interpreting Neural Min-Sum Decoders
In decoding linear block codes, it was shown that noticeable reliability gains can be achieved by introducing learnable parameters to the Belief Propagation (BP) decoder.
DeepIC: Coding for Interference Channels via Deep Learning
Interestingly, it is shown that there exists an asymptotic scheme, called Han-Kobayashi scheme, that performs better than TD and TIN.
A Channel Coding Benchmark for Meta-Learning
We use our MetaCC benchmark to study several aspects of meta-learning, including the impact of task distribution breadth and shift, which can be controlled in the coding problem.
Neural Distributed Source Coding
Distributed source coding (DSC) is the task of encoding an input in the absence of correlated side information that is only available to the decoder.
Deepcode and Modulo-SK are Designed for Different Settings
DeepCode is designed and evaluated for the AWGN channel with (potentially delayed) uncoded output feedback.
HAPI: Hardware-Aware Progressive Inference
Convolutional neural networks (CNNs) have recently become the state-of-the-art in a diversity of AI tasks.
BRP-NAS: Prediction-based NAS using GCNs
What is more, we investigate prediction quality on different metrics and show that sample efficiency of the predictor-based NAS can be improved by considering binary relations of models and an iterative data selection strategy.
Journey Towards Tiny Perceptual Super-Resolution
Recent works in single-image perceptual super-resolution (SR) have demonstrated unprecedented performance in generating realistic textures by means of deep convolutional networks.
ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers
Automatic speech recognition (ASR) via call is essential for various applications, including AI for contact center (AICC) services.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator
We automate HW-CNN codesign using NAS by including parameters from both the CNN model and the HW accelerator, and we jointly search for the best model-accelerator pair that boosts accuracy and efficiency.
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels
Designing codes that combat the noise in a communication medium has remained a significant area of research in information theory as well as wireless communications.
DeepTurbo: Deep Turbo Decoder
We focus on Turbo codes and propose DeepTurbo, a novel deep learning based architecture for Turbo decoding.
LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks
Designing channel codes under low-latency constraints is one of the most demanding requirements in 5G standards.
Efficient Neural Network Compression
The better accuracy and complexity compromise, as well as the extremely fast speed of our method makes it suitable for neural network compression.
Deepcode: Feedback Codes via Deep Learning
The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical applications.
Automatic Rank Selection for High-Speed Convolutional Neural Network
In this paper, we define rank selection as a combinatorial optimization problem and propose a methodology to minimize network complexity while maintaining the desired accuracy.
Combinatorial Optimization
Vocal Bursts Intensity Prediction
Communication Algorithms via Deep Learning
We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms).
Discovering Potential Correlations via Hypercontractivity
Discovering a correlation from one variable to another variable is of fundamental scientific and practical interest.
