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Found 16 papers, 2 papers with code
NaturalFinger: Generating Natural Fingerprint with Generative Adversarial Networks
Deep neural network (DNN) models have become a critical asset of the model owner as training them requires a large amount of resource (i. e. labeled data).
Resilience in Platoons of Cooperative Heterogeneous Vehicles: Self-organization Strategies and Provably-correct Design
We show that self-organization promotes resilience to acceleration limits and communication failures, i. e., homogenizing to a common group behavior makes the platoon recover from these causes of impairments.
Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrieval
Text-to-SQL is a task that converts a natural language question into a structured query language (SQL) to retrieve information from a database.
A Universal PINNs Method for Solving Partial Differential Equations with a Point Source
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs)method emerges to be a promising method for solving both forward and inverse PDE problems.
Deep Reinforcement Learning for Orienteering Problems Based on Decomposition
A dynamic pointer network (DYPN) is introduced as the TSP solver, which takes city locations as inputs and immediately outputs a permutation of nodes.
Binary Neural Networks as a general-propose compute paradigm for on-device computer vision
For binary neural networks (BNNs) to become the mainstream on-device computer vision algorithm, they must achieve a superior speed-vs-accuracy tradeoff than 8-bit quantization and establish a similar degree of general applicability in vision tasks.
Meta-Auto-Decoder for Solving Parametric Partial Differential Equations
Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i. e., PDEs with different physical parameters, boundary conditions, shapes of computation domains, etc.
Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a promising method for solving both forward and inverse PDE problems.
Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration
The training of deep neural networks (DNNs) always requires intensive resources for both computation and data storage.
Channel Pruning via Multi-Criteria based on Weight Dependency
However, these methods ignore a small part of weights in the next layer which disappears as the feature map is removed.
Exceptional Spin Liquids from Couplings to the Environment
We establish the appearance of a qualitatively new type of spin liquid with emergent exceptional points when coupling to the environment.
Strongly Correlated Electrons Mesoscale and Nanoscale Physics Quantum Physics
PFGDF: Pruning Filter via Gaussian Distribution Feature for Deep Neural Networks Acceleration
Deep learning has achieved impressive results in many areas, but the deployment of edge intelligent devices is still very slow.
Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1
The training of deep neural networks (DNNs) requires intensive resources both for computation and for storage performance.
Convolutional Sparse Coding for Compressed Sensing CT Reconstruction
Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems.
Sparse-View CT Reconstruction via Convolutional Sparse Coding
Traditional dictionary learning based CT reconstruction methods are patch-based and the features learned with these methods often contain shifted versions of the same features.
Efficient Computation of Quantized Neural Networks by {−1, +1} Encoding Decomposition
Deep neural networks require extensive computing resources, and can not be efficiently applied to embedded devices such as mobile phones, which seriously limits their applicability.
