Deep Learning architectures usually aren't trained to perform search... at test time, leading to sample inefficiency + poor generalization. Latent Program Network (LPN) builds in test-time adaption by learning a latent space that can be searched. Clem Bonnet mattshow more

DeCAF won the #ICML Test of Time Award 2024!... Big congrats to trevordarrell (my PhD advisor at MIT), and Jeff Donahue. 🎉 You may not heard of DeCAF, but it is everywhere! DeCAF stands for Deep Convolutional Activation Features. Published ten years ago, the DeCAF paper is a groundbreaking work that shows the activation features of the last few layers of a deep network contain useful features that can be "repurposed" for or "transferred to" many other tasks, not just the original task the network was trained for. I created this exercise to show where we can see DeCAF's influence in some of the most well-known architectures: AlexNet, ViT, U-Net, CLIP, and Latent Diffusion, to prove that DeCAF's "Test of Time Award" is well-deserved! Let's give a round of applause to DeCAF, the unsung hero of computer vision.show more

Test-time learning has a catch that scaling alone does... not solve: the state a system writes to in order to learn is also the record a user must inspect to audit it. Optimize it only for learning, and auditability usually degrades. Blog: Part I treats the learning state and the audit record as the same object. This starts the ADAPT series, exploring the architecture section by section.show more

ICML 2026: Latent Reasoning in TRMs is Secretly a... Policy Improvement Operator Why does recursive reasoning, especially latent reasoning, actually work? The theory is still young, and even mechanistic explanations are limited. We close part of this gap by showing that latent reasoning is secretly doing policy improvement. Each recursion pushes the model steadily toward the target. Based on this view, we propose an algorithm that boosts learning and inference efficiency by up to 18x.show more

The latent space of earlier generative models like GANS... can linearly encode concepts of the data. What if the data was model weights? We present weights2weights, a subspace in diffusion weights that behaves as an interpretable latent space over customized diffusion models.show more

Cleaning up underbrush. Installing mesh. Learning which plants are... highly flammable and which aren't. Easy, right? Not even close. But over time, Hugh Council shows us that defensible space and curb appeal aren't mutually exclusive. Progress can be made, and your yard can still look good doing it. Work in progress doesn't mean unfinished. It means moving forward, one task at a time. 📹 Full video: #CAWildfires #DefensibleSpace #WildfirePreparedness #ReadyForWildfireshow more

Learn how to combine a deep learning model for... pose estimation to perform a 3D reconstruction using two cameras 📸show more

Apple Learning Coach is a free professional learning program... that trains instructional coaches, digital learning specialists, and other coaching educators to help teachers effectively use Apple technology in the classroom. Apply today.show more

Is it possible to adapt a neural network on... the fly at the test time to cope with distribution shifts? RNA does precisely that by creating a closed-loop feedback system. We will present it on Wed afternoon at #ICCV2025. 1/nshow more

1. Machine Learning Specialization Break into AI with this... 3-course program by Andrew Ng. What you’ll learn: → Build machine learning models → Train neural networks → Deep reinforcement learning → Unsupervised learning techniques 🔗show more

POV: you agree to test a non slip bonnet

CS projects in 2015: Snake game in Python that... took you 2 weeks to make. CS projects in 2030: Deep Learning Snake Game Neural Network.show more

u js trained on test bro, it's not that deep

How can we use test-time compute for spatial understanding?... 🤔 In InterPose, we propose to repeatedly sample generative video models to help two-view pose estimation and reconstruction, by leveraging the video models' keyframe interpolation abilities. A 🧵... (1/8)show more

One week left to apply! ⏰ Apple Learning Coach... is a free professional learning program that trains instructional coaches, digital learning specialists, and other educators to help teachers get more out of Apple technology.show more

We’re all looking at the latent space, but ⁦gray... ❄️🫧⁩ is inside of it.show more

At Nodepay, we’ve built a living ecosystem fueled by... unused bandwidth, real-time data retrieval, user-owned AI innovation, & rewards that flow back to the contributors. Picture a global AI network constantly learning and evolving—all powered by our community. How it works: 🧵show more

1. Machine Learning Specialization. Break Into AI with this... 3-course program by Andrew Ng. What You’ll learn: → Build ML models → Train neural networks → Deep reinforcement learning → Unsupervised learning techniques 🔗show more

Day 30 of learning AI/ML Total study time: 3... hours I’m going to focus on Machine Learning for the next 8–9 days. After that, I’ll move on to Deep Learning and follow this roadmap, covering Neural Networks, Attention, Hugging Face, RAG, Agents, Memory, Fine-tuning, and Evals. Following the roadmap shared by Ashutoshx7 🙌show more