Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
Data Compression and Inference in Cosmology with Self-Supervised Machine Learning
The influx of massive amounts of data from current and upcoming cosmological surveys necessitates compression schemes that can efficiently summarize the data with minimal loss of information. We introduce a method that l…
Data CompressionBayesian Flow Networks
This paper introduces Bayesian Flow Networks (BFNs), a new class of generative model in which the parameters of a set of independent distributions are modified with Bayesian inference in the light of noisy data samples, …
Bayesian InferenceData CompressionImage GenerationLanguage ModellingApproximating Human-Like Few-shot Learning with GPT-based Compression
In this work, we conceptualize the learning process as information compression. We seek to equip generative pre-trained models with human-like learning capabilities that enable data compression during inference. We prese…
Data CompressionFew-Shot LearningSemantic SimilaritySemantic Textual Similarity+4Event-based Vision for Early Prediction of Manipulation Actions
Neuromorphic visual sensors are artificial retinas that output sequences of asynchronous events when brightness changes occur in the scene. These sensors offer many advantages including very high temporal resolution, no …
Action RecognitionData CompressionEvent-based visionPredictionNonlinear optical encoding enabled by recurrent linear scattering
Optical information processing and computing can potentially offer enhanced performance, scalability and energy efficiency. However, achieving nonlinearity-a critical component of computation-remains challenging in the o…
Data CompressionDecoderDeep LearningEdge-computing+5Invertible Low-Dimensional Modelling of X-ray Absorption Spectra for Potential Applications in Spectral X-ray Imaging
X-ray interaction with matter is an energy-dependent process that is contingent on the atomic structure of the constituent material elements. The most advanced models to capture this relationship currently rely on Monte …
Data CompressionEdge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection
Recent studies show edge computing-based road anomaly detection systems which may also conduct data collection simultaneously. However, the edge computers will have small data storage but we need to store the collected a…
Anomaly DetectionAudio CompressionAudio Super-ResolutionData Compression+3BayesFlow: Amortized Bayesian Workflows With Neural Networks
Modern Bayesian inference involves a mixture of computational techniques for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows for data analysis. Typical problems i…
Bayesian InferenceData CompressionLeCo: Lightweight Compression via Learning Serial Correlations
Lightweight data compression is a key technique that allows column stores to exhibit superior performance for analytical queries. Despite a comprehensive study on dictionary-based encodings to approach Shannon's entropy,…
Data CompressionSparse wavelet-based solutions for the M/EEG inverse problem
This paper is concerned with variational and Bayesian approaches to neuro-electromagnetic inverse problems (EEG and MEG). The strong indeterminacy of these problems is tackled by introducing sparsity inducing regularizat…
Data CompressionEEGregressionDNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome
Decoding the linguistic intricacies of the genome is a crucial problem in biology, and pre-trained foundational models such as DNABERT and Nucleotide Transformer have made significant strides in this area. Existing works…
Computational EfficiencyCore Promoter DetectionCovid Variant PredictionData Compression+9SpikeCodec: An End-to-end Learned Compression Framework for Spiking Camera
Recently, the bio-inspired spike camera with continuous motion recording capability has attracted tremendous attention due to its ultra high temporal resolution imaging characteristic. Such imaging feature results in hug…
Data CompressionSamplet basis pursuit: Multiresolution scattered data approximation with sparsity constraints
We consider scattered data approximation in samplet coordinates with $\ell_1$-regularization. The application of an $\ell_1$-regularization term enforces sparsity of the coefficients with respect to the samplet basis. Sa…
Data CompressionSurface ReconstructionDifferentiable Earth Mover's Distance for Data Compression at the High-Luminosity LHC
The Earth mover's distance (EMD) is a useful metric for image recognition and classification, but its usual implementations are not differentiable or too slow to be used as a loss function for training other algorithms v…
Data CompressionCompressed Sensing: A Discrete Optimization Approach
We study the Compressed Sensing (CS) problem, which is the problem of finding the most sparse vector that satisfies a set of linear measurements up to some numerical tolerance. We introduce an $\ell_2$ regularized formul…
compressed sensingData CompressionDenoisingImage Reconstruction+2Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning
Methods for carefully selecting or generating a small set of training data to learn from, i.e., data pruning, coreset selection, and data distillation, have been shown to be effective in reducing the ever-increasing cost…
Data CompressionReinforcement Learning with Simple Sequence Priors
Everything else being equal, simpler models should be preferred over more complex ones. In reinforcement learning (RL), simplicity is typically quantified on an action-by-action basis -- but this timescale ignores tempor…
continuous-controlContinuous ControlData Compressionreinforcement-learning+2Universal Weak Coreset
Coresets for $k$-means and $k$-median problems yield a small summary of the data, which preserve the clustering cost with respect to any set of $k$ centers. Recently coresets have also been constructed for constrained $k…
ClusteringConstrained ClusteringData CompressionLyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces
In this paper, we propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge, in the context of 6G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a…
CPUData CompressionDeep Reinforcement LearningEdge Classification+1Bayesian Renormalization
In this note we present a fully information theoretic approach to renormalization inspired by Bayesian statistical inference, which we refer to as Bayesian Renormalization. The main insight of Bayesian Renormalization is…
Data Compression