miguelfrde

Arch Linux installation (preinstalled Windows 8.1 dual boot)

Before

1. Disable Windows Fast-Startup
2. Disable Secure Boot

Partitioning

lmullen

The file page.st goes in the templates/ directory in the Gitit wiki home directory. You'll put the Ace JavaScript and CSS files in static/.

debasishg

1. Feature Learning

• Learning Feature Representations with K-means by Adam Coates and Andrew Y. Ng
• The devil is in the details: an evaluation of recent feature encoding methods by Chatfield et. al.
• Emergence of Object-Selective Features in Unsupervised Feature Learning by Coates, Ng
• Scaling Learning Algorithms towards AI Benjio & LeCun
• A Theory of Feature Learning by Brendan van Rooyen, Robert C. Williamson

2. Deep Learning

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
• [Understanding

sbarratt

def get_jacobian(net, x, noutputs):
    x = x.squeeze()
    n = x.size()[0]
    x = x.repeat(noutputs, 1)
    x.requires_grad_(True)
    y = net(x)
    y.backward(torch.eye(noutputs))
    return x.grad.data

erincandescent

Foreward

This document was originally written several years ago. At the time I was working as an execution core verification engineer at Arm. The following points are coloured heavily by working in and around the execution cores of various processors. Apply a pinch of salt; points contain varying degrees of opinion.

It is still my opinion that RISC-V could be much better designed; though I will also say that if I was building a 32 or 64-bit CPU today I'd likely implement the architecture to benefit from the existing tooling.

Mostly based upon the RISC-V ISA spec v2.0. Some updates have been made for v2.2

Original Foreword: Some Opinion

The RISC-V ISA has pursued minimalism to a fault. There is a large emphasis on minimizing instruction count, normalizing encoding, etc. This pursuit of minimalism has resulted in false orthogonalities (such as reusing the same instruction for branches, calls and returns) and a requirement for superfluous instructions which impacts code density both in terms of size and

devmotion

using AdvancedMH
using ArraysOfArrays
using CairoMakie
using DiffEqNoiseProcess
using Distributions
using StochasticDiffEq
using Turing
using Random

struct CrankNicolsonProposal{P,T} <: AdvancedMH.Proposal{P}

nullcline

import traceback
import openai
import sys

# list models
models = openai.Model.list()

def baka(error, character="tsundere",):
    exc_type, exc_value, exc_traceback = sys.exc_info()
    traceback_list = traceback.extract_tb(exc_traceback)

ekzhang

dvruette

import copy
import torch
import torch.nn as nn


class DecayToInit(nn.Module):
    def __init__(self, param: torch.Tensor):
        super().__init__()
        self.register_buffer("param", param)