MACHINE LEARNING

HomeRobot: Open-Vocabulary Mobile Manipulation for Object Picking

By

–

21 June 2023 6h48

HomeRobot: Open-Vocabulary Mobile Manipulation

paper page: https://t.co/Bg5DY8snQ8

Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object in any unseen environment, and placing it in a commanded location. This is a foundational challenge for robots to… pic.twitter.com/J4atienxw2
— AK (@_akhaliq) 21 juin 2023

HomeRobot: Open-Vocabulary Mobile Manipulation paper page: https://
huggingface.co/papers/2306.11
565
… Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object in any unseen environment, and placing it in a commanded location. This is a foundational challenge for robots to

→ View original post on X — @yuhelenyu

IoT Sensors and AI Drive Innovation in Smart Cities

By

@yuhelenyu

–

21 June 2023 6h31

#IoT sensor applications for a smarter world #Smartcities #IIoT #5G #technology #DataAnalytics #bigdata #VR #DataScientists #AI #Robots #Robotics #AutonomousVehicles #Digital #MachineLearning TK @Nanowerk

Point-Cloud Completion Using Pretrained Text-to-Image Diffusion Models

By

–

21 June 2023 5h38

Point-Cloud Completion with Pretrained Text-to-image Diffusion Models paper page: https://
huggingface.co/papers/2306.10
533
… Point-cloud data collected in real-world applications are often incomplete. Data is typically missing due to objects being observed from partial viewpoints, which only

Phi-1: Efficient 1.3B Parameter Language Model for Code

By

–

21 June 2023 5h33

Textbooks Are All You Need paper page: https://
huggingface.co/papers/2306.11
644
… introduce phi-1, a new large language model for code, with significantly smaller size than competing models: phi-1 is a Transformer-based model with 1.3B parameters, trained for 4 days on 8 A100s, using a selection

RepoFusion: Training Code Models to Understand Repository Context

By

–

21 June 2023 5h05

RepoFusion: Training Code Models to Understand Your Repository paper page: https://
huggingface.co/papers/2306.10
998
… Despite the huge success of Large Language Models (LLMs) in coding assistants like GitHub Copilot, these models struggle to understand the context present in the repository (e.g.,

Guiding Code Language Models with Global Context Using Monitors

By

–

21 June 2023 4h36

Guiding Language Models of Code with Global Context using Monitors paper page: https://
huggingface.co/papers/2306.10
763
… Language models of code (LMs) work well when the surrounding code in the vicinity of generation provides sufficient context. This is not true when it becomes necessary to use

GLIMMER: Generalized Late-Interaction Memory Reranker for Language Models

By

–

21 June 2023 4h10

GLIMMER: generalized late-interaction memory reranker paper page: https://
huggingface.co/papers/2306.10
231
… Memory-augmentation is a powerful approach for efficiently incorporating external information into language models, but leads to reduced performance relative to retrieving text. Recent

Meta-Personalizing Vision-Language Models for Named Instance Video Search

By

–

21 June 2023 4h00

Meta-Personalizing Vision-Language Models to Find Named Instances in paper page: https://
huggingface.co/papers/2306.10
169
… Large-scale vision-language models (VLM) have shown impressive results for language-guided search applications. While these models allow category-level queries, they

BayLing: Cross-lingual Alignment and Instruction Following for LLMs

By

–

21 June 2023 3h40

BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Models paper page: https://
huggingface.co/papers/2306.10
968
… Large language models (LLMs) have demonstrated remarkable prowess in language understanding and generation.