#Contrastive Learning

[govindhtech]

Machine Learning Periodic Table: Unifying AI Algorithms

Researchers from MIT, Microsoft, and Google created Information Contrastive Learning (I-Con), the “machine learning periodic table,” to unite machine learning methodologies. The periodic table organises elements, whereas this table organises machine learning algorithms by learning data point correlations.

I-Con presents a single paradigm to show how classification, regression, big language modelling, clustering, dimensionality reduction, and spectral graph theory are all mathematically similar.

Machine learning periodic table importance

New machine learning algorithms and methods are being developed. To comprehend the essential ideas and links across methods. Benefits of the machine learning periodic table:

Unification: It shows how a single mathematical framework links numerous prominent machine learning approaches. Researchers and practitioners can benefit from understanding algorithm similarities.

The I-Con structure emphasises machine learning technique relationships, like the chemical periodic table defines element interactions. It organises the enormous array of algorithms into a clear style.

Discovery: Its ability to spark fresh discoveries is most exciting. Gaps in the chemical periodic table anticipated unknown elements. The Periodic Table of Machine Learning features “empty spaces” that imply undeveloped algorithms.

Innovation: I-Con helps researchers experiment, redefine “neighbors,” change connection confidence, and mix tactics from various algorithms to build new ways. It encourages creativity and the blending of previously unrelated methods.

Efficiency: This framework lets academics create new machine learning algorithms without “reinventing past ideas”. Understanding the ideas and algorithms in the table helps them strategically explore new methods.

How was the machine learning periodic table created?

The atomic table was an unplanned study outcome. Shaden Alshammari, an MIT Freeman Lab researcher, studied clustering, a technique to group related data elements. She connected contrastive learning with grouping. Contrastive machine learning compares positive and negative data.

Alshammari discovered that both techniques could be expressed by the same fundamental equation by studying their mathematics. Following this turning point, the Information Contrastive Learning (I-Con) paradigm was developed to illustrate that machine learning algorithms imitate real-world data linkages while minimising errors.

The researchers created a periodic table using these findings. The table distinguishes algorithms by two main factors:

Point relationships in actual datasets: Data linkages including visual likeness, shared class labels, and cluster membership are involved. These “connections” may not be 100% trustworthy.

The main ways algorithms approximate those connections: These relationships are acquired and reflected internally via algorithms.

By categorising various existing machine learning techniques inside this framework using these two criteria, the researchers found that many popular algorithms line neatly within defined “squares”. They also observed “gaps” where framework-logical algorithms have not yet been built.

How to Fill Gaps

This method helped researchers construct a current system for detecting photographs without human labelling. Combining debiased contrastive representation learning connection concepts with clustering approximation connections helped them “fill a gap” in their periodic table. This new method improved ImageNet-1K picture categorisation accuracy by 8%. They also found that contrastive learning data debiasing might increase clustering accuracy.

I-Con Learning

I-Con redefines machine learning as a tool for understanding complex data interactions. Consider a bustling party where data points, or visitors, meet at tables representing clusters and discuss shared hobbies or hometowns. Consider machine learning techniques as methods guests find friends and settle in.

I-Con simplifies real-world data point connections to make them easier to work with in algorithms. The concept of “connection” might entail appearing alike, sharing labels, or being in the same group. All algorithms try to close the gap between the connections they learn to imitate and the true connections in the training data.

Researchers Use the Periodic Table

The I-Con-based machine learning periodic table has various functions beyond organisation. This gives academics a toolkit for developing unique algorithms. When various machine learning approaches are defined in I-Con's conceptual language, experimenting with variants is easier:

Redefining neighbourhoods entails testing different ways to organise data points into “neighbours”.

Adjusting uncertainty requires varying trust in learnt connections. integrating strategies entails integrating approaches from different algorithms in unique ways.

Every modification might lead to a new periodic table entry. The table may easily be modified to include rows and columns to show more data points' relationships.

Looking Ahead

As artificial intelligence advances and its uses develop, frameworks like I-Con help us understand the area. They help researchers find hidden patterns and enable purposeful innovation. For non-AI professionals, it's a reminder that even in complex fields, basic patterns and structures are waiting to be identified.

Sorting algorithms by how they understand and estimate data point relationships is the basic notion. A full chart that lists all algorithms and their connection and approximation techniques would require more information than this. A basic table to illustrate the notion may look like this:

[salamispots]

it got turned into a 17" x14" pillow instead haha

[jamalir]

AI discovers that not every fingerprint is unique

[hattersarts]

drew some book!husbands. they feel like they've taken more traits from each other than the show.

[chloesimaginationthings]

i think a lot about your mimic sometimes.... .... .

The way I draw the mimic as just the guy ever

[hinamie]

confessions of affection

chapter 4 of Like the Moon Haunts the Sun is out now!! we hope u enjoy the chapter <3 some... stuff happens lmao twirls hair cutely read from the beginning here ...or don't ...haha...unless? lmhs blog: @lmhsfic sam my love: @philosophiums

[cozylittleartblog]

50+ deaths at 5 am got me yelling absolute nonsense to the bosses kicking my whole entire ass

[the-lazy-cat-bakes-souffles]

This season has been absolutely full of rabbits. Akilah cradling rabbits; rabbits surrounding a doomed Ben; rabbits slaughtered repeatedly onscreen. We know there’s a strong symbolic link between Jackie and rabbits in Yellowjackets, but an important distinction is that they don’t represent Jackie as a person (she didn’t even like rabbits!) so much as the idea of Jackie, something ascribed to her against her will: a warped, disingenuous version of her.

It also doesn’t escape my notice that in the very same episode that Kodiak shoots a rabbit through the heart with his crossbow he also shoots Melissa in the chest. Melissa, who Shauna uses to fill a Jackie-shaped hole inside her. Who is pinned down with a knife, hyperventilating like a cornered prey animal when she first gets with Shauna. Who approaches Shauna tentatively, timidly. Who lays flowers on Shauna’s son’s grave and gifts her a handmade sheath wrapped in bark and berries. Who is fearful yet exhilarated when encouraged to embrace her own darkness.

There’s a strange innocence to her interactions with Shauna, but it’s also the death of innocence; the relinquishment of innocence; innocence taken advantage of. When she’s struck with the arrow she cries out to Shauna, but Shauna is like a wolf hot on the hunt, and she can’t offer Melissa the unconditional love she craves and gives freely.

All of which makes me wonder: who is the rabbit?

[whatudottu]

Mirror Twins

[cocosnowlo-drawings]

2024/25 winter outfit practice ❄️🧣

[notbao]

I've been researching and I cannot for the life of me figure out how to replicate the animation for the show. I know the fps and stuff but for some reason it isn't registering in my mind when I draw. I'm probably being dumb but how do you do it? (Also I apologize for consistently asking u stuff you are just a big inspiration😅)

Pretty much a lot of patience and practice. 2-3 years later I’m still learning how to do the art style because without a style guide all I can do is observe and pray.

the same draft re-animated 2 years later :1 Just a whole lot of patience and practice.

[izloveshorses]

some people may see this cassian/bix content as an obstacle for rebelcaptain... but as a rebelcaptain truther i only see more jyncass material to work with 😌

[edsblanketfort]

Thinking about the parallels between Ed’s and Mary’s violence. Stede hardly even questions Mary’s attempt to kill him, he’s mostly outraged at the method. The story punishes violence stemming from racism or enforcement of harmful social norms, but Mary’s violence to ensure she can continue to live a life where she can be herself is completely unpunished. Instead it’s an open action of self protection and expression that leads to understanding and love and a better outcome for everyone. She’s rewarded! She gets to end the marriage while also reconciling with Stede and knowing that they will both be free and happy. Also she gets Doug and the money (free real estate!) What more do we need to reinforce the message that it’s not violence that’s the problem, it’s why you use it

Ed’s violence against Izzy was for freedom too, but even more justified because it wasn’t just freedom from an unsuitable and stifling marriage, it was freedom from targeted abuse and control

Mary’s skewer more than validates Ed’s leg shot and I will die on this hill

[monobmp]

Shadow

[robotsafari]

Hollow No More.

[kagooleo]

sharing a love for all things mundane and sweet 🍫✨💕

+ bonus! 🎆