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ahoy

Vlad Niculae vene

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View hyperbolic.py
"""wrapped hyperbolic distributions
following https://arxiv.org/abs/1902.02992
"""
# author: vlad niculae <v.niculae@uva.nl>
# license: bsd 3-clause
import torch
View check_det_without_rotat.py
""" trying to understand the determinant of sphere-to-cyl """
import numpy as np
import jax.numpy as jnp
from jax import jacfwd
# map from cylinder to sphere
def phi(zr):
d = zr.shape[0]
View kl.py
"""
Approximating the cross-entropy between two Power Sphericals.
Uses a second-order Taylor expansion to approximate E[log(1+z)].
"""
# author: vlad n <vlad@vene.ro>
# license: mit
# documentation: https://hackmd.io/@vladn/SJ93wMevK
View plot_dual_norms.py
"""
Dual p-norms illustrated.
For any norm |.|, the dual norm is defined as |y|_* = max{ <x, y> for |x| <= 1 }.
The figure shows the unit balls of the p-norm, for p = 1.5, 2, and 3.
We compute the dual norm at a dual vector y (short black arrow), rotating
uniformly around the origin over time.
@vene
vene / multi_mixins.py
Last active Aug 17, 2021
python multiple inheritance / mixin MRO
View multi_mixins.py
class Base:
def say(self, val):
print("base says", val)
class A(Base):
def say(self, val):
print("say A")
@vene
vene / README.txt
Created Jul 19, 2021
Experimental config with files & CLI using only OmegaConf
View README.txt
Example input and output.
$ python conf.py seed=42 lr=.1
project: ???
seed: 42
lr: 0.1
epochs: ???
p_drop: 0.5
baseconf: null
View decision_boundary.py
# author: vlad niculae <vlad@vene.ro>
# license: mit
import torch
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from entmax import sparsemax, entmax15
from entmax.losses import sparsemax_loss, entmax15_loss
View soft_box.py
# author: vn
import numpy as np
from scipy.optimize import root_scalar
import torch
import matplotlib.pyplot as plt
def entropy(y, a, b):
@vene
vene / energy.py
Created Oct 19, 2020
energy model with langevin dynamics
View energy.py
# Density estimation with energy-based models
# Langevin sampling, contrastive divergence training.
# Author: Vlad Niculae <vlad@vene.ro>
# License: MIT
import numpy as np
import torch
from sklearn import datasets
import matplotlib.pyplot as plt
View check_spherical_jacob_tangent.py
# Geometric intepretation of the gradient of the mapping:
# f : (0, inf) x Sphere(k-1) -> R^k
# f(r, u) -> r*u
# The *catch*: R can vary on (0, inf) but u may only vary on the
# k-1--dimensional tangent plane!
import numpy as np
def main():