60 results for “matthiask”
Four more voices from Django on the Med 2026 in Pescara 🏖️
Tim came for some focused Django time, Alex for the community, Italian food and contributing, @carlton to work on async support, and @matthiask to finally meet in person people he had worked with online for years.
Different reasons, same good excuse to spend three mornings working on Django together 😊
https://www.europesays.com/de/1354057/ Fußball: 3. Liga: Kickers-Talent Xaver Kiefersauer nach dem 2:0 gegen Waldhof Mannheim: „So kann es weitergehen“ #1FCNürnberg #BadenWürttemberg #DE #Deutschland #DritteLiga #EU #Europa #Europe #Germany #Headlines #IvanFranjic #Mannheim #MatthiasKiefersauer #Nachrichten #News #Schlagzeilen #TSV1860München #Unterfranken #Waldhof #XaverKiefersauer
Matthias Jaissle says Newcastle dominated against Aston Villa but must improve after Johan Manzambi's winner seals 1-0 defeat.
#Football #epl #NewcastleUnited #footballnews2026 #AstonVilla #PremierLeague #footballnewsandupdates
https://www.mondiaries.com/2026/10/epl-newcastle-lose-to-aston-villa-as.html
Four managers who could replace Matthias Jaissle if Newcastle sack boss | Football | Sport
Matthias Jaissle only arrived at Newcastle in the summer but is already under significant pressure to save his…
#Football #Soccer #News #ArneSlot #Football(section) #JulianNagelsmann #KieranMcKenna #MatthiasJaissle #NewcastleUnited #NewcastleUnitedFC #nextnewcastlemanager #PremierLeague #PremierLeaguemanagers #ThomasFrank
https://www.europesays.com/football/74721/
Hamburg | Show statt Politik: Günther singt Matthias-Reim-Klassiker
Hamburg (dpa) – Daniel Günther hat abseits der politischen Bühne sein Show-Talent unter Beweis gestellt. Der Ministerpräsident von…
#Hamburg #Deutschland #Deutsch #DE #Schlagzeilen #Headlines #Nachrichten #News #Europe #Europa #EU #Fernsehen #hamburg #Leute #Medien #Politik #Regierung #Schleswig-Holstein
https://www.europesays.com/de/1353344/
https://www.europesays.com/de/1353344/ Hamburg | Show statt Politik: Günther singt Matthias-Reim-Klassiker #Deutschland #Fernsehen #hamburg #Leute #Medien #Politik #Regierung #SchleswigHolstein
Mensur in Düsseldorf: AfD-Politiker tragen Fechtkampf aus
Streit in Landesverband Zwei AfD-Vertreter tragen ihren Konflikt mit Klingen aus 17.10.2026 – 20:13 UhrLesedauer: 2 Min. Drei…
#Duesseldorf #Deutschland #Deutsch #DE #Schlagzeilen #Headlines #Nachrichten #News #Europe #Europa #EU #Düsseldorf #100312362 #AfD #DeutschePresse-Agentur #Fechtkampf #Germany #Konflikt #Landesverband #MatthiasBüschges #Mensurkiste #Nordrhein-Westfalen
https://www.europesays.com/de/1353012/
https://www.europesays.com/at/461367/ Krebs-Angst bei Matthias Reim: „Lieber Gott, bitte nicht!“ #AT #Austria #Entertainment #Österreich #Unterhaltung
Google DeepMind says four Gemini 4 Argon agents beat Minecraft without any human help in 27 hours and 14 minutes, costing $1,620 at Argon's planned 2027 price (Matthias Bastian/The Decoder)
https://the-decoder.com/four-gemini-4-argon-agents-beat-minecraft-without-any-human-help/
http://www.techmeme.com/261019/p14#a261019p14
https://www.europesays.com/de/1354947/ Excelsis: Lichtshow verbindet im Ingolstädter Münster Historie mit digitaler Kunst #ChristopherBritt #DE #Deutschland #Germany #Ingolstadt #Konflikt #Lichtshow #MatthiasBolle #Münster #NordrheinWestfalen #StadtwerkeIngolstadt #Süddeutschland #Weihnachten #WilliHagn
Excelsis: Lichtshow verbindet im Ingolstädter Münster Historie mit digitaler Kunst
Das Ingolstädter Münster ist eine mächtige Kirche: hohe Säulen, ein langes Kirchenschiff und ein Dachstuhl, d…
#Muenster #Deutschland #Deutsch #DE #Schlagzeilen #Headlines #Nachrichten #News #Europe #Europa #EU #Münster #ChristopherBritt #Germany #Ingolstadt #Konflikt #Lichtshow #MatthiasBolle #Nordrhein-Westfalen #StadtwerkeIngolstadt #Süddeutschland #Weihnachten #WilliHagn
https://www.europesays.com/de/1354947/
https://www.europesays.com/de/1353012/ Mensur in Düsseldorf: AfD-Politiker tragen Fechtkampf aus #100312362 #AfD #DE #DeutschePresseAgentur #Deutschland #Düsseldorf #Fechtkampf #Germany #Konflikt #Landesverband #MatthiasBüschges #Mensurkiste #NordrheinWestfalen
Mehr als 700 Seiten, mehr Befugnisse für BND und Verfassungsschutz, und eine Anhörung, die zur Abrechnung wurde. Sachverständige warnen vor Massenüberwachung im Netz, vor dem Aufweichen der Trennung von Polizei und Geheimdiensten und vor schwacher Kontrolle. Der Jurist Matthias Bäcker sagte: „So etwas macht ein Rechtsstaat nicht.“ Wenn Fachleute im Bundestag von offensichtlicher Verfassungswidrigkeit sprechen, gehört der Entwurf zurück in die Schublade.
#Geheimdienstreform #BND #Verfassungsschutz #Grundrechte #Überwachung
#Frage Wer hat Lust auf eine Leserunde im Fediverse
Am 15.11.26 starte ich eine LESERUNDE zu meinem weihnachtlichen Fantasyroman Das Tor ins Anderswann – und ihr könnt dabei sein.
Ich verlose 5 Buchexemplare.
Eigentlich würde die 14-jährige Jana viel lieber mit ihrer Band proben, statt die Ferien bei ihrer Oma zu verbringen. Doch als sie einen alten Holzengel findet, verwandeln sich die öden Ferien in ein Abenteuer und ihr erscheint jede Nacht der Geist eines Mädchens. Das Geistermädchen führt sie zu einem Baumportal, durch das Jana ins Jahr 1838 gelangt. In der Vergangenheit wird Jana selbst zu einem unsichtbaren Geist, was es ihr nicht einfach macht, Kontakt aufzunehmen. Dennoch gelingt es ihr, neue Freunde zu finden: Matthias, den Straßenjungen, ihre Vorfahrin Agathe und den smarten Arzt Konstantin Heydenreich. Gemeinsam wollen sie dem Geheimnis um das Geistermädchen auf die Spur kommen. Geht es dabei um ein schreckliches Unglück, ein Verbrechen oder gar um Dämonen?
Beantwortet bis zum 25.10.26 die folgende Frage und hüpft in den Lostopf!
Würdest Du durch das Tor ins Anderswann gehen und wenn ja, warum?
Teilnahmebedingungen:
Teilnahmeschluss ist der 25.10.2026, 23:59 Uhr. Pro Person ist nur eine Teilnahme möglich. Teilnehmen kann, wer bis zu diesem Zeitpunkt die Frage im Beitrag beantwortet; unter 18 Jahren nur mit Einverständnis der Erziehungsberechtigten. Die 5 Gewinner:innen werden per Losverfahren ermittelt und per Antwort/Direktnachricht benachrichtigt. Die Versandadresse muss innerhalb von 7 Tagen übermittelt werden, sonst verfällt der Gewinn und wird neu verlost. Die Buchexemplare werden nur an Adressen innerhalb Deutschlands versandt. Die Daten werden ausschließlich für den Versand verwendet und danach gelöscht. Es besteht kein Rechtsanspruch. Der Rechtsweg ist ausgeschlossen.
#DasTorinsAnderswann #Leserunde #Weihnachten #Fantasy #Zeitreise #Jugendbuch #lesen
Hailing from Gothenburg, Sweden, Firebreather have been kicking around since 2016. They sketched out the bones of their sound on 2017’s self-titled, but it wasn’t until 2019’s Under a Blood Moon that they struck gold in the form of a small but potent infusion of Americana. That influence lessened on 2022 follow-up Dwell in the Fog, but a refinement of their groove kept the quality high. Four years later, the doomy trio returns with their fourth full-length in a decade, Blood That I Spill Turns to Fire. With this record, the band sought to keep things raw and heavy while adding a bit of melody and emotion to the brew. These elements can be tricky to mix, but how does Firebreather handle this new direction?
If you looked at the album art above and guessed stoner/sludge, you’d be right. With riffs too fat to fit through a barn door and a bass that registers on the Richter scale, Firebreather know how to rattle some teeth. These guys strike a balance between a slow and middle pace, favoring powerful, patient grooves over technical whizbangs and wankery. While Firebreather’s style may be too slow or repetitive for the layman and too fast for the funeral doom fan, true defenders of the faith will revel in Blood That I Spill Turns to Fire. Whether invoking the Sabbath (“The Sorrow Hides”), swinging with the ocean’s current (“El Cuervo”), or marching to Amon Amarth’s beat (“Against the Light,” “Shrines”), Matthias Nӧӧjd is a devout worshipper at the altar of the riff, savoring each fully before moving to the next. If Nӧӧjd brings the groove to Firebreather’s sound, bassist Jacob Madetoja brings the weight. Rumbling in step with Nӧӧjd’s fretwork, Madetoja turns Firebreather’s deep rumblings into a full-fledged earthquake.
The western flair from previous records seems to have disappeared, but that doesn’t hinder Blood at all. Firebreather’s latest effort surpasses their previous work while leaving their hallmark Conan-meets-High on Fire sound intact. Each track possesses a crystal-clear sense of self and flows effortlessly from verse to chorus to bridge and back, showcasing a focus on and vast improvement in raw songcraft. While Nӧӧjd still calls down from the mountaintop with a voice like a landslide, Blood sees him occasionally utilize softer, cleaner pipes. This isn’t a runaway success, but the variation is appreciated and allows Firebreather to orchestrate more dramatic compositions with more nuanced pushes and pulls. In a genre stereotypically focused on distortion and thiccness, Blood is surprisingly open with a DR of 9. Whatever magic Joona Hassinen wove at Studio Underjord made Blood an absolute pleasure to listen to—equal parts raw, dirty, and clear.
For all the praise I’m heaping on Firebreather, it’s the solo work that truly sets Blood apart. Suffused with the heart of our chosen genre’s distant progenitor, each time Nӧӧjd takes center stage, his guitar weeps with the same sweet sorrow that B.B. King’s and Albert Collins’s once did. Both “Only in the Eyes of Others” and “Protector of Realm” boast crooning, blues-soaked axe work—both guitar and bass—that could make a brick wall cry. On album highlight “Against the Light,” Madetoja soulfully slithers and winds around Nӧӧjd’s rhythmic fretwork, clearing the way for Nӧӧjd to crack the fucking skye. When I first heard this song, I could only imagine what tragedy gave birth to this impassioned centerpiece. Did his dog die? Is the thrill gone? Did he get the dyin’ flu? Whatever happened, I’m kinda thankful, because this solo fucking slaps.
There are a few dips—an overlong interlude here, a trimmable outro there, a suitable but unremarkable percussive performance—but in a genre that often drags and attempts to mimic drugs’ warped sense of time, Blood That I Spill Turns to Fire absolutely flies by, always leaving me wanting more. Firebreather’s fanatical worship of the riff, strong songwriting and flow, and soulful DNA make Blood an impressive entry a decade into the band’s career. I was surprised to find out this is Firebreather’s first visit to our Halls, but after spinning Blood That I Spill Turns to Fire, I’m certain they’ll be invited back.
Rating: 3.5/5.0
DR: 9 | Format Reviewed: 320 kbps mp3
Label: Heavy Psych Sounds Records
Websites: Bandcamp | Facebook | Instagram
Releases Worldwide: October 16th, 2026
🚀 How AI Got Smarter (Not Just Bigger): The Quiet Revolution in Efficient Deep Learning
Five papers that changed how we build AI — and why the future isn't about 100-billion-parameter models
By Rayyan · 10 min read · Published 19 oct 2026
For years, the story of AI was a story of scale.
Bigger models. More data. More GPUs. GPT-3 had 175 billion parameters. ViT-Huge had 632 million. The assumption was simple: if you want human-level AI, you need to build something enormous.
But here's what most people missed: while everyone was watching the giants, a quieter revolution was happening. A handful of researchers were asking a different question:
What if we didn't need to make models bigger — just smarter?
The answer, as it turns out, is yes. And it's changing everything.
📊 The Problem with Big
Let's be honest about what "big" actually means.
Training GPT-3 cost an estimated $4.6 million. Running it requires specialized hardware that most companies don't have. And deploying it on a phone? Forget about it.
But the real issue is deeper. Big models are:
· Expensive to train and run
· Slow at inference
· Impossible to deploy on edge devices
· Environmentally costly — training one large model emits as much CO₂ as five cars over their lifetimes
And here's the kicker: bigger doesn't always mean better. A model with 175 billion parameters can still fail at basic reasoning. Scale alone doesn't solve intelligence.
So researchers started asking: what if we could get the same performance with 10× less compute? 100× less?
That's the story of the five papers below.
┌─────────────────────────────────────────────────────────────┐
│ THE EFFICIENCY FRONTIER │
│ │
│ Accuracy │
│ ▲ │
│ │ ★ EfficientNet │
│ │ ★ │
│ │ ★ │
│ │ ★ │
│ │ ★ │
│ │ ★ │
│ │ ★ │
│ └──────────────────────────────────────────► FLOPs │
│ │
│ EfficientNet pushes the frontier: more accuracy, │
│ less compute. The gap widens at every budget level. │
└─────────────────────────────────────────────────────────────┘
🧩 Paper 1: The Transformer Goes to Vision (ViT, 2021)
For a decade, computer vision belonged to CNNs. Convolutional Neural Networks — with their sliding filters and hierarchical features — were the undisputed kings of image recognition.
Then a team at Google asked a heretical question: what if we just… didn't use convolutions at all?
Their idea, called the Vision Transformer (ViT), was almost absurdly simple. Take an image. Cut it into 16×16 patches. Treat each patch like a word in a sentence. Feed the sequence into a standard Transformer — the same architecture that powers GPT and BERT.
That's it. No convolutions. No hand-crafted features. Just patches and attention.
┌─────────────────────────────────────────────────────────────┐
│ VISION TRANSFORMER (ViT) │
│ │
│ Input Image (224×224) │
│ ┌────┬────┬────┬────┐ │
│ │ P1 │ P2 │ P3 │ P4 │ ← 16×16 patches │
│ ├────┼────┼────┼────┤ │
│ │ P5 │ P6 │ P7 │ P8 │ │
│ ├────┼────┼────┼────┤ │
│ │ P9 │P10 │P11 │P12 │ │
│ ├────┼────┼────┼────┤ │
│ │P13 │P14 │P15 │P16 │ │
│ └────┴────┴────┴────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────┐ │
│ │ Linear Projection (each patch → │ │
│ │ a vector of 768 numbers) │ │
│ └─────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────┐ │
│ │ Add [CLS] token + Positional │ │
│ │ Embeddings │ │
│ └─────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────┐ │
│ │ Transformer Encoder (12 layers) │ │
│ │ ┌───────────────────────────────┐ │ │
│ │ │ Multi-Head Self-Attention │ │ │
│ │ │ (every patch attends to │ │ │
│ │ │ every other patch) │ │ │
│ │ └───────────────────────────────┘ │ │
│ │ ┌───────────────────────────────┐ │ │
│ │ │ Multi-Layer Perceptron │ │ │
│ │ └───────────────────────────────┘ │ │
│ └─────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────┐ │
│ │ Classification Head │ │
│ │ (uses [CLS] token output) │ │
│ └─────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ "Dog" (88.5% confidence) │
└─────────────────────────────────────────────────────────────┘
The results were stunning. ViT-Huge achieved 88.5% top-1 accuracy on ImageNet — state of the art at the time — and it required 4× less compute to train than comparable CNNs.
Why it matters: ViT proved that Transformers are general-purpose. They're not just for language. They can see, too.
But there was a catch: ViT is data-hungry. It needs 300 million images to work well. On small datasets, it underperforms CNNs. The architecture had won, but it was expensive.
┌─────────────────────────────────────────────────────────────┐
│ THE DATA SCALE HYPOTHESIS │
│ │
│ Accuracy │
│ ▲ │
│ │ ● ViT │
│ │ / │
│ │ / │
│ │ / │
│ │ / │
│ │ / │
│ │ / ○ ResNet │
│ │ / / │
│ │ / / │
│ │ / / │
│ │ / / │
│ │ / / │
│ │ / / │
│ │ / / │
│ └────────/───/────────────────────────────► Data Size │
│ 1.3M 14M 300M │
│ │
│ On small data (1.3M), ResNet wins. │
│ On large data (300M), ViT overtakes dramatically. │
│ Inductive biases are a shortcut for small data. │
│ Flexible architectures win with big data. │
└─────────────────────────────────────────────────────────────┘
🧩 Paper 2: Making Transformers Efficient (Swin, 2021)
ViT had a problem: its self-attention is quadratic in image size. Double the image size, and you quadruple the compute. That makes it impractical for high-resolution images — the kind you need for object detection and segmentation.
Microsoft's Swin Transformer solved this with a clever trick: shifted window attention.
Instead of computing attention across the entire image, Swin computes it within local windows. Then, in the next layer, it shifts the windows by half a window — so information can flow between them. The result is linear complexity instead of quadratic.
┌─────────────────────────────────────────────────────────────┐
│ SWIN: SHIFTED WINDOWS │
│ │
│ Layer 1: Standard Windows (7×7 patches each) │
│ ┌─────────┬─────────┬─────────┐ │
│ │ │ │ │ │
│ │ Win 1 │ Win 2 │ Win 3 │ │
│ │ │ │ │ │
│ ├─────────┼─────────┼─────────┤ │
│ │ │ │ │ │
│ │ Win 4 │ Win 5 │ Win 6 │ │
│ │ │ │ │ │
│ ├─────────┼─────────┼─────────┤ │
│ │ │ │ │ │
│ │ Win 7 │ Win 8 │ Win 9 │ │
│ │ │ │ │ │
│ └─────────┴─────────┴─────────┘ │
│ │
│ Layer 2: Shifted Windows (by half a window) │
│ ┌────┬─────────┬─────────┬────┐ │
│ │ │ │ │ │ │
│ │ │ Win A │ Win B │ │ │
│ ├────┼─────────┼─────────┼────┤ │
│ │ │ │ │ │ │
│ │Win C│ Win D │ Win E │Win F│ │
│ ├────┼─────────┼─────────┼────┤ │
│ │ │ │ │ │ │
│ │ │ Win G │ Win H │ │ │
│ └────┴─────────┴─────────┴────┘ │
│ │
│ Patches that were in different windows now interact. │
│ Information flows across the entire image over layers. │
└─────────────────────────────────────────────────────────────┘
Swin became a general-purpose backbone. It hit 87.3% top-1 on ImageNet, beat state-of-the-art on object detection (+2.7 mAP), and crushed segmentation (+3.2 mIoU). It won ICCV 2021 Best Paper.
Why it matters: Swin made Transformers practical for real-world vision — not just classification, but detection and segmentation too.
┌─────────────────────────────────────────────────────────────┐
│ SWIN: HIERARCHICAL FEATURES │
│ │
│ Stage 1 Stage 2 Stage 3 Stage 4 │
│ H/4 × W/4 H/8 × W/8 H/16 × W/16 H/32×W/32│
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────┐ │
│ │ █ █ █ █ │ │ ██ ██ █ │ │ ████ ██ │ │█████│ │
│ │ █ █ █ █ │ │ ██ ██ █ │ │ ████ ██ │ │█████│ │
│ │ █ █ █ █ │ │ ██ ██ █ │ │ ████ ██ │ │█████│ │
│ └─────────┘ └─────────┘ └─────────┘ └─────┘ │
│ 96 channels 192 channels 384 channels 768 ch. │
│ │
│ Like CNNs, Swin builds a feature pyramid: │
│ • High resolution at stage 1 → good for small objects │
│ • Low resolution at stage 4 → good for large objects │
│ This is exactly what object detection and segmentation │
│ need. It makes Swin a drop-in replacement for ResNet. │
└─────────────────────────────────────────────────────────────┘
🧩 Paper 3: Scaling Intelligently (EfficientNet, 2020)
Before EfficientNet, scaling CNNs was an art, not a science. Want a better model? Make it deeper (ResNet). Or wider (WideResNet). Or higher-resolution. But nobody knew the optimal way to scale.
Google's EfficientNet introduced compound scaling: a single coefficient that uniformly scales depth, width, and resolution using fixed ratios.
┌─────────────────────────────────────────────────────────────┐
│ COMPOUND SCALING │
│ │
│ Traditional scaling (independent): │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ Deeper │ │ Wider │ │ Higher │ │
│ │ only │ │ only │ │ Res only│ │
│ └─────────┘ └─────────┘ └─────────┘ │
│ Each gives diminishing returns. │
│ │
│ Compound scaling (together): │
│ ┌─────────────────────────────────────┐ │
│ │ Depth × Width × Resolution │ │
│ │ d = α^φ │ │
│ │ w = β^φ │ │
│ │ r = γ^φ │ │
│ │ subject to: α · β² · γ² ≈ 2 │ │
│ └─────────────────────────────────────┘ │
│ Multiplicative benefits. │
│ │
│ Intuition: Like a camera. To take the best photo, │
│ you need a bigger sensor (width), a better zoom │
│ (resolution), AND more lenses (depth) — together. │
└─────────────────────────────────────────────────────────────┘
The result was a family of models that achieved state-of-the-art accuracy with up to 10× better efficiency. EfficientNet-B0 matched ResNet-50 with 5× fewer parameters and 10× fewer FLOPs. EfficientNet-B7 hit 84.3% top-1 while being 8.4× smaller than the previous best.
┌─────────────────────────────────────────────────────────────┐
│ EFFICIENTNET: THE FAMILY │
│ │
│ Model Resolution Params FLOPs Top-1 │
│ ───────────────────────────────────────────────────── │
│ B0 224 5.3M 0.39B 77.1% │
│ B1 240 7.8M 0.70B 79.1% │
│ B2 260 9.2M 1.0B 80.1% │
│ B3 300 12M 1.8B 81.6% │
│ B4 380 19M 4.2B 82.9% │
│ B5 456 30M 9.9B 83.6% │
│ B6 528 43M 19B 84.0% │
│ B7 600 66M 37B 84.3% │
│ │
│ B0 matches ResNet-50 with 5× fewer params, 10× fewer FLOPs│
│ B7 matches GPipe (556M params) with 8.4× fewer params. │
└─────────────────────────────────────────────────────────────┘
Why it matters: EfficientNet showed how to scale optimally, not just more. It's the difference between throwing more GPUs at a problem and actually designing a better model.
🧩 Paper 4: Learning Without Labels (BEiT, 2022)
ViT needed 300 million labeled images. But labels are expensive — someone has to manually annotate every image.
Microsoft's BEiT asked: can we learn from unlabeled images, like BERT learns from unlabeled text?
The answer was yes. BEiT adapts BERT's masked language modeling to vision:
┌─────────────────────────────────────────────────────────────┐
│ BEiT: MASKED IMAGE MODELING │
│ │
│ Step 1: Tokenize image with discrete VAE │
│ ┌────┬────┬────┬────┐ │
│ │ T1 │ T2 │ T3 │ T4 │ ← Each patch → one visual token │
│ ├────┼────┼────┼────┤ │
│ │ T5 │ T6 │ T7 │ T8 │ │
│ ├────┼────┼────┼────┤ │
│ │ T9 │T10 │T11 │T12 │ │
│ ├────┼────┼────┼────┤ │
│ │T13 │T14 │T15 │T16 │ │
│ └────┴────┴────┴────┘ │
│ │
│ Step 2: Mask ~40% of patches │
│ ┌────┬────┬────┬────┐ │
│ │ T1 │????│ T3 │????│ ← ???? = [MASK] token │
│ ├────┼────┼────┼────┤ │
│ │????│ T6 │????│ T8 │ │
│ ├────┼────┼────┼────┤ │
│ │ T9 │????│T11 │????│ │
│ ├────┼────┼────┼────┤ │
│ │T13 │T14 │????│T16 │ │
│ └────┴────┴────┴────┘ │
│ │
│ Step 3: Predict visual tokens of masked patches │
│ ┌────┬────┬────┬────┐ │
│ │ T1 │ T2'│ T3 │ T4'│ ← T2', T4' = predictions │
│ ├────┼────┼────┼────┤ │
│ │ T5'│ T6 │ T7'│ T8 │ │
│ ├────┼────┼────┼────┤ │
│ │ T9 │ T10│T11 │ T12│ │
│ ├────┼────┼────┼────┤ │
│ │T13 │T14 │T15'│T16 │ │
│ └────┴────┴────┴────┘ │
│ │
│ Loss: cross-entropy between predicted and true tokens │
│ No labels needed. The true tokens come from the VAE. │
└─────────────────────────────────────────────────────────────┘
BEiT-L achieved 85.2% top-1 on ImageNet — outperforming supervised ViT (82.3%) — and matched models trained on 10× more data.
Why it matters: BEiT enabled learning from unlabeled data. That's how humans learn — by observing the world, not by being told the answer to everything.
┌─────────────────────────────────────────────────────────────┐
│ PREDICT TOKENS, NOT PIXELS │
│ │
│ Predicting pixels: │
│ • Forces model to learn edges, colors, textures │
│ • Low-level details, not semantics │
│ • Like predicting individual letters of a word │
│ │
│ Predicting visual tokens: │
│ • Forces model to learn what is in the patch │
│ • High-level semantics │
│ • Like predicting whole words │
│ │
│ This is why BEiT outperforms pixel-prediction methods. │
│ It's exactly the same reason BERT predicts words, │
│ not characters. │
└─────────────────────────────────────────────────────────────┘
🧩 Paper 5: Making It Deployable (TinyBERT, 2020)
BERT is powerful. BERT is also huge — 110 million parameters. Too slow for real-time apps. Too big for a phone.
TinyBERT solved this with knowledge distillation: train a small model (the student) to mimic a large model (the teacher). Two stages: general distillation on unlabeled text, then task-specific distillation.
Distillation happens at four levels: embeddings, attention matrices, hidden states, and predictions. The result: 7.5× smaller, 9.4× faster, while retaining 96.8% of BERT's performance.
┌─────────────────────────────────────────────────────────────┐
│ TINYBERT: FOUR-LEVEL DISTILLATION │
│ │
│ Teacher (BERT-Base, 110M params) │
│ ┌─────────────────────────────────────┐ │
│ │ Layer 1 │ Layer 2 │ ... │ Layer 12│ │
│ └─────────────────────────────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌─────────────────────────────────────┐ │
│ │ Student (TinyBERT, 14.5M params) │ │
│ │ Layer 1 │ Layer 2 │ Layer 3│L4 │ │
│ └─────────────────────────────────────┘ │
│ │
│ Distillation happens at 4 levels: │
│ 1. Embedding layer ────► match embeddings │
│ 2. Attention matrices ────► match where teacher looks │
│ 3. Hidden states ────► match internal representations│
│ 4. Prediction logits ────► match final outputs │
│ │
│ Two stages: │
│ Stage 1: General distillation (unlabeled text) │
│ Stage 2: Task-specific distillation (labeled task data) │
│ │
│ Result: 96.8% of BERT's accuracy, 7.5× smaller, 9.4× faster│
└─────────────────────────────────────────────────────────────┘
Why it matters: TinyBERT made powerful models actually deployable. It's the bridge between research and reality.
┌─────────────────────────────────────────────────────────────┐
│ TINYBERT VS DISTILBERT │
│ │
│ Model GLUE Avg Params Speedup │
│ ───────────────────────────────────────────────────── │
│ BERT-Base 79.2 110M 1× │
│ DistilBERT 77.0 66M 1.6× │
│ TinyBERT-4 76.8 14.5M 9.4× │
│ TinyBERT-6 79.0 67M 2.3× │
│ │
│ TinyBERT-4 is 4.5× smaller than DistilBERT │
│ but nearly as accurate (76.8 vs 77.0). │
│ Why? Four-level distillation transfers more knowledge │
│ than logit-only distillation. │
└─────────────────────────────────────────────────────────────┘
🔗 The Through-Line
Together, these five papers tell a story:
┌─────────────────────────────────────────────────────────────┐
│ THE EFFICIENT AI STORY ARC │
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ ViT (2021) │ │
│ │ "Transformers can see" │ │
│ │ Proved the architecture is general-purpose │ │
│ └─────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Swin (2021) │ │
│ │ "Transformers can be efficient" │ │
│ │ Linear complexity + hierarchical features │ │
│ └─────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ EfficientNet (2020) │ │
│ │ "Scaling can be principled" │ │
│ │ Compound scaling: depth + width + resolution │ │
│ └─────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ BEiT (2022) │ │
│ │ "Models can learn without labels" │ │
│ │ Masked image modeling — like BERT for vision │ │
│ └─────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ TinyBERT (2020) │ │
│ │ "Models can be compressed dramatically" │ │
│ │ Four-level distillation — deployable on any device │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ Together: Smarter, not just bigger. │
│ This is the real research direction pushing AI toward │
│ near-human capability. │
└─────────────────────────────────────────────────────────────┘
This is the real research direction pushing AI toward near-human capability. Not by making models bigger — but by making them smarter, more efficient, and more general.
⚠️ The Gaps That Remain
Despite this progress, big problems remain:
┌─────────────────────────────────────────────────────────────┐
│ SEVEN RESEARCH GAPS │
│ │
│ 1. NO UNIFIED EFFICIENCY BENCHMARK │
│ Every paper uses different metrics. │
│ We can't compare fairly. │
│ → Solution: Standardized benchmark (like MLPerf) │
│ │
│ 2. MULTI-MODAL DISTILLATION MISSING │
│ TinyBERT = text; ViT/Swin = vision. │
│ No one distills CLIP or GPT-4V. │
│ → Solution: Multi-modal distillation frameworks │
│ │
│ 3. HARDWARE-AWARE EFFICIENCY │
│ FLOPs ≠ real latency. │
│ Paper-efficient ≠ phone-efficient. │
│ → Solution: Hardware-aware NAS and benchmarking │
│ │
│ 4. SELF-SUPERVISION IS EXPENSIVE │
│ BEiT is powerful but costly to pre-train. │
│ → Solution: Efficient self-supervised methods │
│ │
│ 5. NO EFFICIENT VISION-LANGUAGE MODELS │
│ CLIP and GPT-4V are huge. │
│ We need small, fast equivalents. │
│ → Solution: Efficient multi-modal architectures │
│ │
│ 6. INTERPRETABILITY IS IGNORED │
│ None of these papers address explainability. │
│ → Solution: XAI for efficient models │
│ │
│ 7. CONTINUAL LEARNING │
│ All assume static datasets. │
│ The real world changes. │
│ → Solution: Efficient continual learning │
└─────────────────────────────────────────────────────────────┘
🚀 What's Next
The next frontier is efficient, general-purpose, multi-modal AI — models that:
┌─────────────────────────────────────────────────────────────┐
│ THE NEXT FRONTIER │
│ │
│ ✅ Match human performance on any task │
│ ✅ Run on any device (phone, sensor, laptop) │
│ ✅ Learn from any data (labeled or not) │
│ ✅ Adapt continuously without retraining │
│ ✅ Explain their decisions │
│ │
│ This is the direction AI is heading. │
│ And it's not about making models bigger. │
│ It's about making them smarter. │
│ │
│ The giants will always have their place. │
│ But the future belongs to the efficient. │
└─────────────────────────────────────────────────────────────┘
💡 The Bottom Line
For years, we thought bigger meant better. But the real breakthroughs — the ones pushing AI toward human-level capability — came from a different direction.
· ViT showed Transformers could see.
· Swin made them efficient.
· EfficientNet showed how to scale.
· BEiT learned without labels.
· TinyBERT made it deployable.
Together, they define the efficient AI movement. And they prove something important:
You don't need a 175-billion-parameter model to be smart. You need a better architecture, a smarter training method, and a principled way to scale.
That's the quiet revolution. And it's just getting started.
If you found this useful, share it with someone who thinks AI is just about bigger models. And if you're building AI systems — I'd love to hear what efficiency trade-offs you're making.
📚 References
Tags: #AI #DeepLearning #MachineLearning #VisionTransformer #EfficientAI #Transformers #TechBlog #ArtificialIntelligence #SwinTransformer #EfficientNet #BEiT #TinyBERT
-#ai #deeplearning #machinelearning #software #coding #development #engineering #inclusive #community
An analysis of 5 key papers
War. War never changes. But it takes place in a lot of different places in #StarTrek. And where exactly is the subject of today’s #TrekTriviaTuesday question.
As always no googling and no spoiling the answer for others. Please boost after voting! :BoostOK:
Vote will run for 24h, then I will reply with the correct answer.
On which planet was the Tarsian War fought?
When those last people put me through an entire appointment that didn't involve taking impressions of my teeth, but did end with a pitch for a $4,000 out-of-pocket TMD treatment program, I called them assholes on my way out. I also sent them a letter requesting a refund for the cost of the visit. I've totally lost patience with this kind of shit.
I think someone put Italian dressing in some of the sushi. Just tasted weird. Everything was way too sweet, no spiciness at all, lots of gelatinous meat covered in breading and thick, sticky sauces.
@danyow Bzw. ich auch wegen #PlanetaryHealthDiet geschaut hab, aber die startet halt ab 2 Jahren erst. Das wäre ja eigtl auch so ein Kontaktpunkt gewesen, der da Guidance geben könnte. 2/2
The new "Asian" buffet in Baken Park sucks. Save your money.
Manchmal frag ich mich ja schon, was in den Köpfen von Redakteur*innen vorgeht, wenn sie ihre Berichte planen. Wie gerade im #SWR: Bericht über die Proteste und Krawalle in Frankreich. Was machen wir denn dazu nur? 🤔 Ich habs, wir gehen nach Freiburg in einen Französisch Sprachkurs und befragen die Teilnehmer*innen dazu 🤦 #qualitätsjournalismus
I have a huge sense of relief that I am getting a replacement bite plate that is actually designed for painful TMJ. I was very pissed off about that last clinic.
Everything went great! It looks like I am getting a bite plate made to my specifications and it cost what I expected it to. I told the dentist that I am in pain (not just trying to protect my teeth) and he totally understood. It was a very straightforward and honest dental office.
I'm nervous about my dental appointment today. Not because I'm afraid of drills or needles, but because I'm worried about getting ripped off again.
#OpenCode desktop v2 now also has a context windows info visible. And /btw works (don't know if this worked before). I had to manually download v2 and install it: https://opencode.ai/de/download #AI #KI #Vibecoding
Stayed in a space-y hotel room near the Perpignan station, and finally arrived to a very wet and rainy Barcelona half an hour ago. Next stop Valencia, departing 18:15, arriving at 21:09.
XSLT.js
Vor ein paar Tagen habe ich mich noch darüber aufgeregt, dass Browser künftig wohl kein XSLT mehr unterstützen werden. Es mag sein, dass ich da vielleicht etwas zu hysterisch voreilig war. @krinkle hat mich in den Kommentaren auf einen Blogpost und einen Pull Request hingewiesen, die zeigen wie man mit JavaScript zu einem ähnlichen Resultat kommt. (Danke nochmal dafür!) If you just want to make some existing XSLT work in browsers that don’t support it, check out Mason’s […]
XSLT.js
Vor ein paar Tagen habe ich mich noch darüber aufgeregt, dass Browser künftig wohl kein XSLT mehr unterstützen werden. Es mag sein, dass ich da vielleicht etwas zu hysterisch voreilig...
https://notiz.blog/2026/10/19/xslt-js/
XSLT.js