Ronnypetson

## jacobian_hessian.py
import torch

def jacobian(y, x, create_graph=False):
    jac = []
    flat_y = y.reshape(-1)
    grad_y = torch.zeros_like(flat_y)
    for i in range(len(flat_y)):
        grad_y[i] = 1.
        grad_x, = torch.autograd.grad(flat_y, x, grad_y, retain_graph=True, create_graph=create_graph)
        jac.append(grad_x.reshape(x.shape))

## Deep Math.md

      
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                shagunsodhani
                / Deep Math.md
            
            
              Created
              July 16, 2016 10:09
            
              
                Notes for Deep Math paper
              
          
    Deep Math: Deep Sequence Models for Premise Selection

Introduction


Automated Theorem Proving (ATP) - Attempting to prove mathematical theorems automatically.
Bottlenecks in ATP:

Autoformalization - Semantic or formal parsing of informal proofs.
Automated Reasoning - Reasoning about already formalised proofs.


Paper evaluates the effectiveness of neural sequence models for premise selection (related to automated reasoning) without using hand engineered features.
Link to the paper
	import torch

	def jacobian(y, x, create_graph=False):
	jac = []
	flat_y = y.reshape(-1)
	grad_y = torch.zeros_like(flat_y)
	for i in range(len(flat_y)):
	grad_y[i] = 1.
	grad_x, = torch.autograd.grad(flat_y, x, grad_y, retain_graph=True, create_graph=create_graph)
	jac.append(grad_x.reshape(x.shape))