Papers Inference Optimization
“Inference Optimization” 태그가 달린 논문 56편 · 필터 해제
Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model
Recently, large-scale transformer-based models have been proven to be effective over various tasks across many domains. Nevertheless, applying them in industrial production requires tedious and heavy works to reduce infe…
DecoderGPUInference OptimizationSBbadger: Biochemical Reaction Networks with Definable Degree Distributions
Motivation: An essential step in developing computational tools for the inference, optimization, and simulation of biochemical reaction networks is gauging tool performance against earlier efforts using an appropriate se…
Inference OptimizationADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography
Advances in multi-spectral detectors are causing a paradigm shift in X-ray Computed Tomography (CT). Spectral information acquired from these detectors can be used to extract volumetric material composition maps of the o…
3D ReconstructionComputed Tomography (CT)Image ReconstructionInference Optimization+3A Novel 1D State Space for Efficient Music Rhythmic Analysis
Inferring music time structures has a broad range of applications in music production, processing and analysis. Scholars have proposed various methods to analyze different aspects of time structures, such as beat, downbe…
Inference OptimizationOnline Beat TrackingOnline Downbeat TrackingBifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization
We present Bifocal RNN-T, a new variant of the Recurrent Neural Network Transducer (RNN-T) architecture designed for improved inference time latency on speech recognition tasks. The architecture enables a dynamic pivot f…
Inference OptimizationKeyword SpottingQuantizationspeech-recognition+1Developing efficient transfer learning strategies for robust scene recognition in mobile robotics using pre-trained convolutional neural networks
We present four different robust transfer learning and data augmentation strategies for robust mobile scene recognition. By training three mobile-ready (EfficientNetB0, MobileNetV2, MobileNetV3) and two large-scale basel…
Data AugmentationInference OptimizationScene RecognitionTransfer LearningEasy and Efficient Transformer : Scalable Inference Solution For large NLP model
Recently, large-scale transformer-based models have been proven to be effective over various tasks across many domains. Nevertheless, applying them in industrial production requires tedious and heavy works to reduce infe…
DecoderGPUInference OptimizationText GenerationInvestigations on the inference optimization techniques and their impact on multiple hardware platforms for Semantic Segmentation
In this work, the task of pixel-wise semantic segmentation in the context of self-driving with a goal to reduce the inference time is explored. Fully Convolutional Network (FCN-8s, FCN-16s, and FCN-32s) with a VGG16 enco…
Inference OptimizationSemantic SegmentationSNDCNN: Self-normalizing deep CNNs with scaled exponential linear units for speech recognition
Very deep CNNs achieve state-of-the-art results in both computer vision and speech recognition, but are difficult to train. The most popular way to train very deep CNNs is to use shortcut connections (SC) together with b…
Inference Optimizationspeech-recognitionSpeech RecognitionA bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data
The emergence of data-driven demand analysis has led to the increased use of generative modelling to learn the probabilistic dependencies between random variables. Although their apparent use has mostly been limited to i…
Bayesian InferenceBIG-bench Machine LearningClusteringDimensionality Reduction+1Deep Signal Recovery with One-Bit Quantization
Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference. In this paper, we consider the application of the deep unfolding technique in the proble…
BIG-bench Machine LearningComputational EfficiencyInference OptimizationQuantizationA General Method for Amortizing Variational Filtering
We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i.e. …
Inference OptimizationVariational InferenceIterative Amortized Inference
Inference models are a key component in scaling variational inference to deep latent variable models, most notably as encoder networks in variational auto-encoders (VAEs). By replacing conventional optimization-based inf…
Inference OptimizationVariational InferenceCRVI: Convex Relaxation for Variational Inference
We present a new technique for solving non-convex variational inference optimization problems. Variational inference is a widely used method for posterior approximation in which the inference problem is transformed …
Inference OptimizationregressionVariational InferenceLearning to Infer
Inference models, which replace an optimization-based inference procedure with a learned model, have been fundamental in advancing Bayesian deep learning, the most notable example being variational auto-encoders (VAEs). …
Inference OptimizationInput Convex Neural Networks
This paper presents the input convex neural network architecture. These are scalar-valued (potentially deep) neural networks with constraints on the network parameters such that the output of the network is a convex func…
ImputationInference Optimizationreinforcement-learningReinforcement Learning+2