Exciting news! The highly anticipated AI Workshops begin in ~24 hours, and we don’t want you to miss out on this incredible opportunity! 📅 Event Start: June 13–15 ⏰ Event Schedule: 9:00 am Pacific Time Zone 📍 Event Location: Zoom 💡 What to Expect: Engaging workshops,… Read More »AI Workshops Start in 24 Hours! Don’t Miss Out! Stefan Kojouharov Chatbots Life – Medium
Home Table of Contents DETR Breakdown Part 2: Methodologies and Algorithms The DETR Model 👁️ Object Detection Set Prediction Loss 📉 Optimal Bipartite Matching 🔄 Optimal Bipartite Matching for Objects 🌐 Optimize Object Specific Losses 🔧 Quiz Time! 🤓 Summary Citation Information DETR Breakdown… Read More »DETR Breakdown Part 2: Methodologies and Algorithms Aritra Roy Gosthipaty and Ritwik Raha PyImageSearch
Current visual generative models, particularly diffusion-based models, have made tremendous leaps in automating content generation. Thanks to computation, data scalability, and architectural design advancements, designers can generate realistic visuals or videos using a textual prompt as input. To achieve unparalleled fidelity and diversity, these… Read More »Alibaba Group and Ant Group Researchers Introduce VideoComposer: An AI Model That Enables To Combine Multiple Modalities Like Text, Sketch, Style, And Even Motion To Drive Video Generation Tanushree Shenwai Artificial Intelligence Category – MarkTechPost
From Artificial Intelligence and Data Analysis to Cryptography and Optimization, algorithms play an important role in every domain. Algorithms are basically a set of procedures that help in completing a particular task in a step-by-step manner. These sets of rules deliver instructions to computers… Read More »DeepMind Introduces AlphaDev: A Deep Reinforcement Learning Agent Which Discovers Faster Sorting Algorithms From Scratch Tanya Malhotra Artificial Intelligence Category – MarkTechPost
The out-of-distribution (OOD) detection in deep learning models, particularly in image classification, addresses the challenge of identifying inputs unrelated to the model’s training task. It aims to prevent the model from making confident but incorrect predictions on (OOD) inputs while accurately classifying in-distribution (ID)… Read More »In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation (Paper Summary) Mahmoud Ghorbel Artificial Intelligence Category – MarkTechPost
Using audio and text embeddings jointly for Keyword Spotting (KWS) has shown high-quality results, but the key challenge of how to semantically align two embeddings for multi-word keywords of different sequence lengths remains largely unsolved. In this paper, we propose an audio-text-based end-to-end model architecture… Read More »Matching Latent Encoding for Audio-Text based Keyword Spotting Apple Machine Learning Research
*=Equal Contributors We consider online learning problems in the realizable setting, where there is a zero-loss solution, and propose new Differentially Private (DP) algorithms that obtain near-optimal regret bounds. For the problem of online prediction from experts, we design new algorithms that obtain near-optimal regret… Read More »Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime Apple Machine Learning Research
This paper focuses on long-tailed object detection in the semi-supervised learning setting, which poses realistic challenges, but has rarely been studied in the literature. We propose a novel pseudo-labeling-based detector called CascadeMatch. Our detector features a cascade network architecture, which has multi-stage detection heads with… Read More »Semi-Supervised and Long-Tailed Object Detection with CascadeMatch Apple Machine Learning Research
This paper presents an extension to train end-to-end Context-Aware Transformer Transducer ( CATT ) models by using a simple, yet efficient method of mining hard negative phrases from the latent space of the context encoder. During training, given a reference query, we mine a number… Read More »Approximate Nearest Neighbor Phrase Mining for Contextual Speech Recognition Apple Machine Learning Research
The techniques for 3D indoor scene capturing are widely used, but the meshes produced leave much to be desired. In this paper, we propose “RoomDreamer”, which leverages powerful natural language to synthesize a new room with a different style. Unlike existing image synthesis methods, our… Read More »RoomDreamer: Text-Driven 3D Indoor Scene Synthesis with Coherent Geometry and Texture Apple Machine Learning Research