Kundan Kumar @pheonix1729
Ph.D. Student, Indian Institute of Science. Neuromorphic Engineering & Analog IC/FPGA Design | IEEE HKN ( #IamHKN) | BITS Pilani Alumni Bangalore, India Joined July 2010-
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Kundan Kumar retweeted
Many recent posts on free energy. Here is a summary from my class “Statistical mechanics of learning and computation” on the many relations between free energy, KL divergence, large deviation theory, entropy, Boltzmann distribution, cumulants, Legendre duality, saddle points,…
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Kundan Kumar retweeted
In continuous generative diffusion, the conditional entropy rate is the constant term that separates the score matching and the denoising score matching loss This can be directly interpreted as the information transfer (bit rate) from the state x_t and the final generation x_0.
Kundan Kumar retweeted
Mathematicians are a bit weird sometimes
Kundan Kumar retweeted
Jensen's inequality gives the difference between the average value of a convex function φ, and its value at the center, where both “average” and “center” are defined in terms of some distribution p_X. When the function φ is flat, or the distribution is narrow, they agree.
Kundan Kumar retweeted
1/18 Today I will try to describe a mathematical trick that can logically explain what happens in Quantum Mechanics and Special Relativity.
Kundan Kumar retweeted
On Tuesday, in my class, we have learnt that all a neural net does is stretching / contracting the space fabric. For example this 3-layer net (1 hidden layer of 100 positive neurons) gets its 5D logits (2D projections) linearly separable by the classifier hyperplanes (lines).
Kundan Kumar retweeted
Happy birthday to Sir Andrew J. Wiles! He received the Abel Prize in 2016 for "his stunning proof of Fermat’s Last Theorem by way of the modularity conjecture for semistable elliptic curves, opening a new era in number theory." #Abelprize #Abelprize2016 #science #mathematics
Kundan Kumar retweeted
The mathematician John Conway didn’t fit into a box. 🧵
Kundan Kumar retweeted
Freeman Dyson on how he struggled to learn quantum mechanics from Paul Dirac
Kundan Kumar retweeted
Reinforcement Learning
Kundan Kumar retweeted
Oldies but goldies: Mark Kac, Can you hear the shape of a drum? 1966. Non-isometric shapes can have the same Laplacian eigenvalues (spectrum). Displaying here some Laplacian eigenvectors. en.wikipedia.org/wiki/Hearing_t…
Kundan Kumar retweeted
Book #OTD "Functions of Matrices: Theory and Computation" by Nicholas J. Higham epubs.siam.org/doi/10.1137/1.…
Kundan Kumar retweeted
The l^p functional is convex and hence a norm for p>=1. It is sparsity-inducing for p<=1. The l^1 norm is the heart of the lasso, aka basis pursuit. en.wikipedia.org/wiki/Basis_pur… en.wikipedia.org/wiki/Lasso_(st…
Kundan Kumar retweeted
Exact Fisher-Rao geodesics and guaranteed approximations of Fisher-Rao distances for multivariate normal distributions. doi.org/10.1016/bs.hos…
Kundan Kumar retweeted
The dynamics of a system of rods (multiple pendulums) is an ODE evolution on a 2nd-order algebraic variety. Defines highly complex patterns as soon as n>2. en.wikipedia.org/wiki/Double_pe…
Kundan Kumar retweeted
A throwback to some algorithmic botany #rstats #creativecoding #plottertwitter
Kundan Kumar retweeted
Oldies but goldies: Nina Amenta, Marshall Bern, Manolis Kamvysselis, A New Voronoi-Based Surface Reconstruction Algorithm, 1998. Introduces the power-crust algorithm, one of the first reconstruction methods with theoretical guarantees. web.cs.ucdavis.edu/~amenta/pubs/c…
Kundan Kumar retweeted
Every technical person knows about ordinary least-squares (OLS) but most don’t know *total* least-squares (TLS). These measure fitting error differently: OLS minimizes sum of sq. vertical distances whereas TLS minimizes the sum of orthogonal distances from data to fit line 1/2
Kundan Kumar retweeted
The Kabsch-Nadas formula solves in closed form the orthogonal least square problem (aka orthogonal Procrustes). At the heart of the iterative closest point method for registration. en.wikipedia.org/wiki/Orthogona… en.wikipedia.org/wiki/Kabsch_al…
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