**hamming**

Learning to Learn

september 2018 by drmeme

The Art of Doing Science and Engineering: Learning to Learn" was the capstone course by Dr. Richard W. Hamming (1915-1998) for graduate students at the Naval Postgraduate School (NPS) in Monterey, California.

hamming
youtube
learning
science
engineering
september 2018 by drmeme

[1710.06993] Improved Search in Hamming Space using Deep Multi-Index Hashing

november 2017 by arsyed

"Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. There has been considerable research on generating efficient image representation via the deep-network-based hashing methods. However, the issue of efficient searching in the deep representation space remains largely unsolved. To this end, we propose a simple yet efficient deep-network-based multi-index hashing method for simultaneously learning the powerful image representation and the efficient searching. To achieve these two goals, we introduce the multi-index hashing (MIH) mechanism into the proposed deep architecture, which divides the binary codes into multiple substrings. Due to the non-uniformly distributed codes will result in inefficiency searching, we add the two balanced constraints at feature-level and instance-level, respectively. Extensive evaluations on several benchmark image retrieval datasets show that the learned balanced binary codes bring dramatic speedups and achieve comparable performance over the existing baselines."

papers
hashing
search
hamming
via:vaguery
november 2017 by arsyed

6.896: Essential Coding Theory

february 2017 by nhaliday

- probabilistic method and Chernoff bound for Shannon coding

- probabilistic method for asymptotically good Hamming codes (Gilbert coding)

- sparsity used for LDPC codes

mit
course
yoga
tcs
complexity
coding-theory
math.AG
fields
polynomials
pigeonhole-markov
linear-algebra
probabilistic-method
lecture-notes
bits
sparsity
concentration-of-measure
linear-programming
linearity
expanders
hamming
pseudorandomness
crypto
rigorous-crypto
communication-complexity
no-go
madhu-sudan
shannon
unit
p:**
- probabilistic method for asymptotically good Hamming codes (Gilbert coding)

- sparsity used for LDPC codes

february 2017 by nhaliday

What is the relationship between information theory and Coding theory? - Quora

february 2017 by nhaliday

basically:

- finite vs. asymptotic

- combinatorial vs. probabilistic (lotsa overlap their)

- worst-case (Hamming) vs. distributional (Shannon)

Information and coding theory most often appear together in the subject of error correction over noisy channels. Historically, they were born at almost exactly the same time - both Richard Hamming and Claude Shannon were working at Bell Labs when this happened. Information theory tends to heavily use tools from probability theory (together with an "asymptotic" way of thinking about the world), while traditional "algebraic" coding theory tends to employ mathematics that are much more finite sequence length/combinatorial in nature, including linear algebra over Galois Fields. The emergence in the late 90s and first decade of 2000 of codes over graphs blurred this distinction though, as code classes such as low density parity check codes employ both asymptotic analysis and random code selection techniques which have counterparts in information theory.

They do not subsume each other. Information theory touches on many other aspects that coding theory does not, and vice-versa. Information theory also touches on compression (lossy & lossless), statistics (e.g. large deviations), modeling (e.g. Minimum Description Length). Coding theory pays a lot of attention to sphere packing and coverings for finite length sequences - information theory addresses these problems (channel & lossy source coding) only in an asymptotic/approximate sense.

q-n-a
qra
math
acm
tcs
information-theory
coding-theory
big-picture
comparison
confusion
explanation
linear-algebra
polynomials
limits
finiteness
math.CO
hi-order-bits
synthesis
probability
bits
hamming
shannon
intricacy
nibble
s:null
signal-noise
- finite vs. asymptotic

- combinatorial vs. probabilistic (lotsa overlap their)

- worst-case (Hamming) vs. distributional (Shannon)

Information and coding theory most often appear together in the subject of error correction over noisy channels. Historically, they were born at almost exactly the same time - both Richard Hamming and Claude Shannon were working at Bell Labs when this happened. Information theory tends to heavily use tools from probability theory (together with an "asymptotic" way of thinking about the world), while traditional "algebraic" coding theory tends to employ mathematics that are much more finite sequence length/combinatorial in nature, including linear algebra over Galois Fields. The emergence in the late 90s and first decade of 2000 of codes over graphs blurred this distinction though, as code classes such as low density parity check codes employ both asymptotic analysis and random code selection techniques which have counterparts in information theory.

They do not subsume each other. Information theory touches on many other aspects that coding theory does not, and vice-versa. Information theory also touches on compression (lossy & lossless), statistics (e.g. large deviations), modeling (e.g. Minimum Description Length). Coding theory pays a lot of attention to sphere packing and coverings for finite length sequences - information theory addresses these problems (channel & lossy source coding) only in an asymptotic/approximate sense.

february 2017 by nhaliday

Richard Hamming: "Learning to Learn" - YouTube

december 2016 by cstanhope

The Art of Doing Science and Engineering: Learning to Learn" was the capstone course by Dr. Richard W. Hamming (1915-1998) for graduate students at the Naval Postgraduate School (NPS) in Monterey, California.

video
lecture
science
learning
hamming
richard
learn
december 2016 by cstanhope

You and Your Research

june 2016 by lucastheis

"At a seminar in the Bell Communications Research Colloquia Series, Dr. Richard W. Hamming, a Professor at the Naval Postgraduate School in Monterey, California and a retired Bell Labs scientist, gave a very interesting and stimulating talk, `You and Your Research' to an overflow audience of some 200 Bellcore staff members and visitors at the Morris Research and Engineering Center on March 7, 1986. This talk centered on Hamming's observations and research on the question ``Why do so few scientists make significant contributions and so many are forgotten in the long run?'' From his more than forty years of experience, thirty of which were at Bell Laboratories, he has made a number of direct observations, asked very pointed questions of scientists about what, how, and why they did things, studied the lives of great scientists and great contributions, and has done introspection and studied theories of creativity. The talk is about what he has learned in terms of the properties of the individual scientists, their abilities, traits, working habits, attitudes, and philosophy."

hamming
research
june 2016 by lucastheis

You and Your Research

april 2016 by nhaliday

- Richard Hamming's famous advice

- story about Einstein is interesting

advice
career
productivity
academia
science
reflection
expert
quotes
🎓
scholar
tradeoffs
strategy
classic
hi-order-bits
frontier
lens
curiosity
meta:math
meta:science
success
stories
ground-up
giants
einstein
hamming
shannon
optimate
🦉
unit
nibble
the-trenches
innovation
novelty
metameta
meta:research
wisdom
courage
confluence
len:long
high-variance
p:whenever
s:***
discovery
🔬
info-dynamics
s-factor
org:junk
org:edu
expert-experience
- story about Einstein is interesting

april 2016 by nhaliday