Robotics Toolbox for Python
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Updated
Jun 19, 2024 - Python
Robotics Toolbox for Python
TensorFlow implementations of losses for sequence to sequence machine learning models
Official code for the paper "Hybrid Quantum-Classical Scheduling for Accelerating Neural Network Training with Newton's Gradient Descent"
Scalable Computation of Hessian Diagonals
Code to reproduce the paper "Deconstructing the Goldilocks Zone of Neural Network Initialization"
Implementation of Numerical Analysis algorithms/methods in Python
Measuring generalization properties of fine-tuning using Hessian
Implementation of EPTQ - an Enhanced Post-Training Quantization algorithm for DNN compression
Some Hessian based analysis for practical deep models with tensorflow2
Measuring generalization properties of graph neural networks
A python3 script computing bond and valence angle force constants using the Seminario (projected hessian) method.
Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.
Hessian spectral analysis with tensorflow1.x
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
Implementation of automatic differentiation (AD) in forward and backward modes with mathematical explanations
Code and weights for local feature affine shape estimation paper "Repeatability Is Not Enough: Learning Discriminative Affine Regions via Discriminability"
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
Generic derivative objects (gradients, Jacobians, Hessians, and more) by finite differences
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