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Marabou 2.0: a versatile formal analyzer of neural networks
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks | SpringerLink Skip to main
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Robust iterative value conversion: Deep reinforcement learning for neurochip-driven edge robots
A neurochip is a device that reproduces the signal processing mechanisms of brain neurons
and calculates Spiking Neural Networks (SNNs) with low power consumption and at high …
and calculates Spiking Neural Networks (SNNs) with low power consumption and at high …
Deep Combination of CDCL (T) and Local Search for Satisfiability Modulo Non-Linear Integer Arithmetic Theory
Satisfiability Modulo Theory (SMT) generalizes the propositional satisfiability problem (SAT)
by extending support for various first-order background theories. In this paper, we focus on …
by extending support for various first-order background theories. In this paper, we focus on …
Certified quantization strategy synthesis for neural networks
Quantization plays an important role in deploying neural networks on embedded, real-time
systems with limited computing and storage resources (eg, edge devices). It significantly …
systems with limited computing and storage resources (eg, edge devices). It significantly …
Quantization-Based Optimization Algorithm for Hardware Implementation of Convolution Neural Networks
Convolutional neural networks (CNNs) have demonstrated remarkable performance in
many areas but require significant computation and storage resources. Quantization is an …
many areas but require significant computation and storage resources. Quantization is an …
Automated Program Refinement: Guide and Verify Code Large Language Model with Refinement Calculus
Recently, the rise of code-centric Large Language Models (LLMs) has reshaped the
software engineering world with low-barrier tools like Copilot that can easily generate code …
software engineering world with low-barrier tools like Copilot that can easily generate code …
Live on the Hump: Self Knowledge Distillation via Virtual Teacher-Students Mutual Learning
S Wang, P Hao, F Wu, C Bai - … of the 32nd ACM International Conference …, 2024 - dl.acm.org
For solving the limitations of the current self knowledge distillation including never fully
utilizing the knowledge of shallow exits and neglecting the impact of auxiliary exits' structure …
utilizing the knowledge of shallow exits and neglecting the impact of auxiliary exits' structure …
Neural Network Verification is a Programming Language Challenge
LC Cordeiro, ML Daggitt, J Girard-Satabin… - ar** field of research. So far, the
main priority has been establishing efficient verification algorithms and tools, while proper …
main priority has been establishing efficient verification algorithms and tools, while proper …
Parallel Verification for -Equivalence of Neural Network Quantization
Quantization replaces floating point arithmetic with integer arithmetic in deep neural
networks, enabling more efficient on-device inference with less power and memory …
networks, enabling more efficient on-device inference with less power and memory …
[PDF][PDF] Quantization-Based Optimization Algorithm for Hardware Implementation of Convolution Neural Networks. Electronics 2024, 13, 1727
Convolutional neural networks (CNNs) have demonstrated remarkable performance in
many areas but require significant computation and storage resources. Quantization is an …
many areas but require significant computation and storage resources. Quantization is an …