AI
Fighting AI with AI How do we trust AI inside airplanes and self-driving cars? Deep neural networks are powerful, but opaque—and that makes traditional safety checks hard. This paper proposes using foundation models to make AI-enabled systems safer, from requirements to deployment. * REACT: uses Large Language Models to translate
AIMusic
What if you could sketch a “tension curve” and have an AI compose music that follows it? That’s exactly what this study delivers. Tonal tension—the ebb and flow of stability vs. suspense in harmony—drives how music feels. The authors plug a computational tension model into a Transformer
NLP
Smarter news sorting Classifying news articles is tricky: simple stats like term frequency catch key words, while deep models capture context. AGFF (Attention‑Guided Feature Fusion) blends both—and uses attention to decide, per article, which signal to trust more. * Fuses statistical features (e.g., TF‑IDF) with semantic representations
AI
AI images that "think" as they form Most image generators plan before they draw or fix mistakes after. Thinking-while-Generating (TwiG) lets models do something new: interleave short bursts of textual reasoning during generation—like thinking out loud while painting. As pixels appear, the model explains what to add
AI
Nature builds brains from a single cell. This study shows how an artificial network can do something similar - growing itself from "noise". Inspired by the early visual system (retina to LGN), the authors propose a simple developmental algorithm that starts with one "cell" and self-organizes
AI
What if AI could learn to translate without any bilingual textbooks? This research shows how pictures can act as a bridge between languages. When we look at the same photo, speakers of different languages describe the same objects and actions. The team uses images as “pivots” so a model can
MachineLearning
Training AI usually needs lots of human-labeled examples. This work shows a different path: make models learn from plenty of unlabeled data by asking them to stay consistent under strong "noise." Instead of simple tweaks (like small crops or word drops), they use powerful data augmentation: RandAugment for
AI
Shopping sites host millions of product photos. Some slip through with offensive, non-compliant logos or messages, hurting customers and brands. This paper introduces an AI system that flags those images at massive scale. What’s new: a practical blend of image classification + object detection, tuned for messy, real-world retail catalogs.
AutonomousDriving
Teaching self-driving cars to spot the unknown Real roads are messy. To stay safe, autonomous vehicles must notice unexpected hazards—from a toppled ladder to a runaway stroller. That’s the challenge of out-of-distribution (OOD) segmentation. Seungheon Song and Jaekoo Lee propose a simple idea with big impact: use language
AI
Ever heard of the “lottery ticket” idea in AI? It says big neural nets hide small subnetworks that can perform just as well. This paper proves a strong version of that for the heart of Transformers: multi-head attention (MHA). * The big claim: Inside a randomly initialized MHA, there exists a
AI
Ever heard of the “lottery ticket” idea in AI? It says big neural nets hide small subnetworks that can perform just as well. This paper proves a strong version of that for the heart of Transformers: multi-head attention (MHA). * The big claim: Inside a randomly initialized MHA, there exists a
AI
Ever heard of the “lottery ticket” idea in AI? It says big neural nets hide small subnetworks that can perform just as well. This paper proves a strong version of that for the heart of Transformers: multi-head attention (MHA). * The big claim: Inside a randomly initialized MHA, there exists a