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Loss function and objective function are different, or are they?

less than 1 minute read


This blog post is a result of a discussion (read borderline heated argument) that I had with a friend regarding if machine learning terms loss function and objective function meant the same. We will get to know if they do mean the same or not by the end of this post. I am choosing to write this post as a dialogue between Pinfy and Scooby, to let you decide if it was a discussion or an argument :’). Also, I am using a colab notebook to write this post because the post is going to get mathematical.

Visualizing Learning of PyTorch Code using TensorboardX

less than 1 minute read


Visualizating learning is a great way to gain better understaning of your machine learning model’s inputs, outputs and/or the model parameters. In this article we discuss

  • how to use TensorboardX, a wrapper around Tensorboard, to visualize training of your existing PyTorch models.
  • how to use a conda environment to install tensorboard in case of installation clashes.
  • how to remotely access the web interface for tensorboard.

Academic Research: What does it involve?

6 minute read


What is academic research all about? This is a question that I keep asking myself, frequently so. From whatever research experience I have, I have come to realise that there are three major components that determine the quality of research one gets to do (or atleast the one I get to do): working, networking and not-working. Working: Because if you are a researcher, you have got to do research. Networking: Because you need collaborators, mentors and reveiwers to do good research. Not-working: Because you are a human, and you need rest no matter how much you think you don’t.



Geometric Calculus and the Fibre Bundle description of Quantum Mechanics

Published in arXiv:math-ph, 2002

This work has the purpose of applying the concept of Geometric Calculus (Clifford Algebras) to the Fibre Bundle description of Quantum Mechanics. Thus, it is intended to generalize that formulation to curved spacetimes [the base space of the fibre bundle in question] in a more natural way.

Recommended citation: Daniel D. Ferrante. (2002). "Geometric Calculus and the Fibre Bundle description of Quantum Mechanics." arXiv:math-ph.

Mollified Monte Carlo

Published in Nuclear Physics. B, 2002

Using a common technique for approximating distributions [generalized functions], we are able to use standard Monte Carlo methods to compute QFT quantities in Minkowski spacetime, under phase transitions, or when dealing with coalescing stationary points.

Recommended citation: D.D. Ferrante, J. Doll, G.S. Guralnik, D. Sabo. (2003). "Mollified Monte Carlo." Nuclear Physics. B. 119(965).

A Review Of Two Novel Numerical Methods in QFT

Published in arXiv:hep-lat, 2003

We outline two alternative schemes to perform numerical calculations in quantum field theory. In principle, both of these approaches are better suited to study phase structure than conventional Monte Carlo. The first method, Source Galerkin, is based on a numerical analysis of the Schwinger-Dyson equations using modern computer techniques. The nature of this approach makes dealing with fermions relatively straightforward, particularly since we can work on the continuum. Its ultimate success in non-trivial dimensions will depend on the power of a propagator expansion scheme which also greatly simplifies numerical calculation of traditional perturbation graphs. The second method extends Monte Carlo approaches by introducing a procedure to deal with rapidly oscillating integrals.

Recommended citation: R. Easther, D. D. Ferrante, G. S. Guralnik, D. Petrov. (2003). "A Review Of Two Novel Numerical Methods in QFT." arXiv:hep-lat.

Mollifying Quantum Field Theory or Lattice QFT in Minkowski Spacetime and Symmetry Breaking

Published in arXiv:hep-lat, 2006

This work develops and applies the concept of mollification in order to smooth out highly oscillatory exponentials. This idea, known for quite a while in the mathematical community (mollifiers are a means to smooth distributions), is new to numerical Quantum Field Theory. It is potentially very useful for calculating phase transitions [highly oscillatory integrands in general], for computations with imaginary chemical potentials and Lattice QFT in Minkowski spacetime.

Recommended citation: D. D. Ferrante, G. S. Guralnik. (2006). "Mollifying Quantum Field Theory or Lattice QFT in Minkowski Spacetime and Symmetry Breaking." arXiv:hep-lat.

Published in , 1900

Phase Transitions and Moduli Space Topology

Published in arXiv:hep-th, 2008

By means of an appropriate re-scaling of the metric in a Lagrangian, we are able to reduce it to a kinetic term only. This form enables us to examine the extended complexified solution set (complex moduli space) of field theories by finding all possible geodesics of this metric. This new geometrical standpoint sheds light on some foundational issues of QFT and brings to the forefront non-perturbative core aspects of field theory. In this process, we show that different phases of the theory are topologically inequivalent, i.e., their moduli space has distinct topologies. Moreover, the different phases are related by “duality transformations”, which are established by the modular structure of the theory. In conclusion, after the topological structure is elucidated, it is possible to use the Euler Characteristic in order to topologically quantize the theory, in resonance with the content of the Atiyah-Singer Index theorem.

Recommended citation: D. D. Ferrante, G. S. Guralnik. (2008). "Phase Transitions and Moduli Space Topology." arXiv:hep-th.

Published in , 1900



Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.