Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
FPGA-based AI Smart NICs for Scalable Distributed AI Training Systems
Rapid advances in artificial intelligence (AI) technology have led to significant accuracy improvements in a myriad of application domains at the cost of larger and more compute-intensive models. Training such models on …
Data CompressionEnergy-Efficient Classification at the Wireless Edge with Reliability Guarantees
Learning at the edge is a challenging task from several perspectives, since data must be collected by end devices (e.g. sensors), possibly pre-processed (e.g. data compression), and finally processed remotely to output t…
Data Compressionimage-classificationImage ClassificationSelf-Similarity Priors: Neural Collages as Differentiable Fractal Representations
Many patterns in nature exhibit self-similarity: they can be compactly described via self-referential transformations. Said patterns commonly appear in natural and artificial objects, such as molecules, shorelines, galax…
Data CompressionNeural Estimation of the Rate-Distortion Function With Applications to Operational Source Coding
A fundamental question in designing lossy data compression schemes is how well one can do in comparison with the rate-distortion function, which describes the known theoretical limits of lossy compression. Motivated by t…
Data CompressionA unified theory of learning
Recently machine learning using neural networks (NN) has been developed, and many new methods have been suggested. These methods are optimized for the type of input data and work very effectively, but they cannot be used…
Data CompressionA Learning Framework for Bandwidth-Efficient Distributed Inference in Wireless IoT
In wireless Internet of things (IoT), the sensors usually have limited bandwidth and power resources. Therefore, in a distributed setup, each sensor should compress and quantize the sensed observations before transmittin…
Data CompressionSC2 Benchmark: Supervised Compression for Split Computing
With the increasing demand for deep learning models on mobile devices, splitting neural network computation between the device and a more powerful edge server has become an attractive solution. However, existing split co…
Data CompressionEdge-computingFeature CompressionImage Compression+1Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge Monitoring
Millimeter-scale embedded sensing systems have unique advantages over larger devices as they are able to capture, analyze, store, and transmit data at the source while being unobtrusive and covert. However, area-constrai…
Data CompressionEvent DetectionManagementDCT-Former: Efficient Self-Attention with Discrete Cosine Transform
Since their introduction the Trasformer architectures emerged as the dominating architectures for both natural language processing and, more recently, computer vision applications. An intrinsic limitation of this family …
Data CompressionReliable validation of Reinforcement Learning Benchmarks
Reinforcement Learning (RL) is one of the most dynamic research areas in Game AI and AI as a whole, and a wide variety of games are used as its prominent test problems. However, it is subject to the replicability crisis …
BenchmarkingData CompressionOffline RLreinforcement-learning+2LCP-dropout: Compression-based Multiple Subword Segmentation for Neural Machine Translation
In this study, we propose a simple and effective preprocessing method for subword segmentation based on a data compression algorithm. Compression-based subword segmentation has recently attracted significant attention as…
Data CompressionMachine TranslationSegmentationTranslationTowards Effective and Robust Neural Trojan Defenses via Input Filtering
Trojan attacks on deep neural networks are both dangerous and surreptitious. Over the past few years, Trojan attacks have advanced from using only a single input-agnostic trigger and targeting only one class to using mul…
Data Compressioninput filteringVariational InferenceCognitive Semantic Communication Systems Driven by Knowledge Graph
Semantic communication is envisioned as a promising technique to break through the Shannon limit. However, the existing semantic communication frameworks do not involve inference and error correction, which limits the ac…
Data CompressionSemantic CommunicationMemory Replay with Data Compression for Continual Learning
Continual learning needs to overcome catastrophic forgetting of the past. Memory replay of representative old training samples has been shown as an effective solution, and achieves the state-of-the-art (SOTA) performance…
Autonomous Drivingclass-incremental learningClass Incremental LearningContinual Learning+5An Introduction to Neural Data Compression
Neural compression is the application of neural networks and other machine learning methods to data compression. Recent advances in statistical machine learning have opened up new possibilities for data compression, allo…
BIG-bench Machine LearningData CompressionImage Quality AssessmentUniversality of parametric Coupling Flows over parametric diffeomorphisms
Invertible neural networks based on Coupling Flows CFlows) have various applications such as image synthesis and data compression. The approximation universality for CFlows is of paramount importance to ensure the model …
Bayesian OptimizationData CompressionImage GenerationAnalysis and Optimization of the Latency Budget in Wireless Systems with Mobile Edge Computing
We present a framework to analyse the latency budget in wireless systems with Mobile Edge Computing (MEC). Our focus is on teleoperation and telerobotics, as use cases that are representative of mission-critical uplink-i…
Data CompressionEdge-computingProbability Distribution on Rooted Trees
The hierarchical and recursive expressive capability of rooted trees is applicable to represent statistical models in various areas, such as data compression, image processing, and machine learning. On the other hand, su…
Data CompressionAPack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inference
Data accesses between on- and off-chip memories account for a large fraction of overall energy consumption during inference with deep learning networks. We present APack, a simple and effective, lossless, off-chip memory…
Data CompressionQuantizationA Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow
Analyzing large-scale data from simulations of turbulent flows is memory intensive, requiring significant resources. This major challenge highlights the need for data compression techniques. In this study, we apply a phy…
Data CompressionQuantization