This media is not supported in your browser
VIEW IN TELEGRAM
✨Helios: Real Real-Time Long Video Generation Model
📝 Summary:
Helios is a 14B autoregressive diffusion model that achieves real-time minute-scale video generation at 19.5 FPS on a single GPU. It innovatively overcomes long-video drifting and real-time performance challenges without conventional acceleration or anti-drifting techniques. Helios supports T2V, ...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04379
• PDF: https://arxiv.org/pdf/2603.04379
• Github: https://pku-yuangroup.github.io/Helios-Page/
🔹 Models citing this paper:
• https://huggingface.co/BestWishYsh/Helios-Base
• https://huggingface.co/BestWishYsh/Helios-Distilled
• https://huggingface.co/BestWishYsh/Helios-Mid
✨ Spaces citing this paper:
• https://huggingface.co/spaces/multimodalart/Helios-Distilled
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Helios is a 14B autoregressive diffusion model that achieves real-time minute-scale video generation at 19.5 FPS on a single GPU. It innovatively overcomes long-video drifting and real-time performance challenges without conventional acceleration or anti-drifting techniques. Helios supports T2V, ...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04379
• PDF: https://arxiv.org/pdf/2603.04379
• Github: https://pku-yuangroup.github.io/Helios-Page/
🔹 Models citing this paper:
• https://huggingface.co/BestWishYsh/Helios-Base
• https://huggingface.co/BestWishYsh/Helios-Distilled
• https://huggingface.co/BestWishYsh/Helios-Mid
✨ Spaces citing this paper:
• https://huggingface.co/spaces/multimodalart/Helios-Distilled
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨ArtHOI: Articulated Human-Object Interaction Synthesis by 4D Reconstruction from Video Priors
📝 Summary:
ArtHOI synthesizes articulated human-object interactions by formulating 4D reconstruction from monocular video priors, using optical flow for part segmentation and a decoupled reconstruction pipeline ...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04338
• PDF: https://arxiv.org/pdf/2603.04338
• Project Page: https://arthoi.github.io/
• Github: https://github.com/Inso-13/ArtHOI
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
ArtHOI synthesizes articulated human-object interactions by formulating 4D reconstruction from monocular video priors, using optical flow for part segmentation and a decoupled reconstruction pipeline ...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04338
• PDF: https://arxiv.org/pdf/2603.04338
• Project Page: https://arthoi.github.io/
• Github: https://github.com/Inso-13/ArtHOI
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Memex(RL): Scaling Long-Horizon LLM Agents via Indexed Experience Memory
📝 Summary:
A memory mechanism called Memex enables large language model agents to handle long-horizon tasks more effectively by maintaining compact context through structured summaries while storing full interac...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04257
• PDF: https://arxiv.org/pdf/2603.04257
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
A memory mechanism called Memex enables large language model agents to handle long-horizon tasks more effectively by maintaining compact context through structured summaries while storing full interac...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04257
• PDF: https://arxiv.org/pdf/2603.04257
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Phi-4-reasoning-vision-15B Technical Report
📝 Summary:
A compact open-weight multimodal reasoning model is presented that achieves competitive performance through careful architecture design, high-quality data curation, and a hybrid approach combining dir...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03975
• PDF: https://arxiv.org/pdf/2603.03975
• Project Page: https://www.microsoft.com/en-us/research/blog/phi-4-reasoning-vision-and-the-lessons-of-training-a-multimodal-reasoning-model/
• Github: https://github.com/microsoft/Phi-4-reasoning-vision-15B
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
A compact open-weight multimodal reasoning model is presented that achieves competitive performance through careful architecture design, high-quality data curation, and a hybrid approach combining dir...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03975
• PDF: https://arxiv.org/pdf/2603.03975
• Project Page: https://www.microsoft.com/en-us/research/blog/phi-4-reasoning-vision-and-the-lessons-of-training-a-multimodal-reasoning-model/
• Github: https://github.com/microsoft/Phi-4-reasoning-vision-15B
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Heterogeneous Agent Collaborative Reinforcement Learning
📝 Summary:
HACRL enables collaborative reinforcement learning where heterogeneous agents share verified rollouts during training to improve collectively while maintaining independent operation at inference time,...
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02604
• PDF: https://arxiv.org/pdf/2603.02604
• Project Page: https://zzx-peter.github.io/hacrl/
• Github: https://zzx-peter.github.io/hacrl/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
HACRL enables collaborative reinforcement learning where heterogeneous agents share verified rollouts during training to improve collectively while maintaining independent operation at inference time,...
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02604
• PDF: https://arxiv.org/pdf/2603.02604
• Project Page: https://zzx-peter.github.io/hacrl/
• Github: https://zzx-peter.github.io/hacrl/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨T2S-Bench & Structure-of-Thought: Benchmarking and Prompting Comprehensive Text-to-Structure Reasoning
📝 Summary:
Structure of Thought prompting technique enhances language model performance by guiding explicit intermediate text structuring across diverse tasks, while T2S-Bench benchmark evaluates and improves te...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03790
• PDF: https://arxiv.org/pdf/2603.03790
• Project Page: https://t2s-bench.github.io/T2S-Bench-Page/
• Github: https://t2s-bench.github.io/T2S-Bench-Page/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Structure of Thought prompting technique enhances language model performance by guiding explicit intermediate text structuring across diverse tasks, while T2S-Bench benchmark evaluates and improves te...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03790
• PDF: https://arxiv.org/pdf/2603.03790
• Project Page: https://t2s-bench.github.io/T2S-Bench-Page/
• Github: https://t2s-bench.github.io/T2S-Bench-Page/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨CubeComposer: Spatio-Temporal Autoregressive 4K 360° Video Generation from Perspective Video
📝 Summary:
CubeComposer is a spatio-temporal autoregressive diffusion model that generates high-resolution 360° panoramic videos by decomposing them into cubemap representations and using efficient autoregressiv...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04291
• PDF: https://arxiv.org/pdf/2603.04291
• Project Page: https://lg-li.github.io/project/cubecomposer
• Github: https://github.com/TencentARC/CubeComposer
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
CubeComposer is a spatio-temporal autoregressive diffusion model that generates high-resolution 360° panoramic videos by decomposing them into cubemap representations and using efficient autoregressiv...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04291
• PDF: https://arxiv.org/pdf/2603.04291
• Project Page: https://lg-li.github.io/project/cubecomposer
• Github: https://github.com/TencentARC/CubeComposer
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Proact-VL: A Proactive VideoLLM for Real-Time AI Companions
📝 Summary:
Proact-VL is a multimodal framework that enables real-time interactive AI companions for gaming scenarios with low-latency responses and strong video understanding capabilities. AI-generated summary P...
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03447
• PDF: https://arxiv.org/pdf/2603.03447
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Proact-VL is a multimodal framework that enables real-time interactive AI companions for gaming scenarios with low-latency responses and strong video understanding capabilities. AI-generated summary P...
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03447
• PDF: https://arxiv.org/pdf/2603.03447
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning
📝 Summary:
MemSifter is a framework that uses a small proxy model to offload memory retrieval from large language models, employing reinforcement learning with task-performance rewards and training techniques li...
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03379
• PDF: https://arxiv.org/pdf/2603.03379
• Github: https://github.com/plageon/MemSifter
🔹 Models citing this paper:
• https://huggingface.co/zstanjj/MemSifter-4B-Thinking
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
MemSifter is a framework that uses a small proxy model to offload memory retrieval from large language models, employing reinforcement learning with task-performance rewards and training techniques li...
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03379
• PDF: https://arxiv.org/pdf/2603.03379
• Github: https://github.com/plageon/MemSifter
🔹 Models citing this paper:
• https://huggingface.co/zstanjj/MemSifter-4B-Thinking
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models
📝 Summary:
MUSE is an open-source platform for evaluating multimodal safety in large language models, incorporating automated cross-modal attack generation and a dual-metric framework to assess alignment across ...
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02482
• PDF: https://arxiv.org/pdf/2603.02482
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
MUSE is an open-source platform for evaluating multimodal safety in large language models, incorporating automated cross-modal attack generation and a dual-metric framework to assess alignment across ...
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02482
• PDF: https://arxiv.org/pdf/2603.02482
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration
📝 Summary:
SWE-CI presents a repository-level benchmark for evaluating code generation agents' ability to maintain code quality through long-term software evolution cycles. AI-generated summary Large language mo...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03823
• PDF: https://arxiv.org/pdf/2603.03823
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
SWE-CI presents a repository-level benchmark for evaluating code generation agents' ability to maintain code quality through long-term software evolution cycles. AI-generated summary Large language mo...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03823
• PDF: https://arxiv.org/pdf/2603.03823
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨RIVER: A Real-Time Interaction Benchmark for Video LLMs
📝 Summary:
RIVER Bench is introduced to evaluate real-time video comprehension through retrospective memory, live-perception, and proactive anticipation tasks. This benchmark reveals current offline models struggle with real-time processing, long-term memory, and future perception, highlighting the need for...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03985
• PDF: https://arxiv.org/pdf/2603.03985
• Github: https://github.com/OpenGVLab/RIVER
✨ Datasets citing this paper:
• https://huggingface.co/datasets/nanamma/RIVER
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
RIVER Bench is introduced to evaluate real-time video comprehension through retrospective memory, live-perception, and proactive anticipation tasks. This benchmark reveals current offline models struggle with real-time processing, long-term memory, and future perception, highlighting the need for...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03985
• PDF: https://arxiv.org/pdf/2603.03985
• Github: https://github.com/OpenGVLab/RIVER
✨ Datasets citing this paper:
• https://huggingface.co/datasets/nanamma/RIVER
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨EmbodiedSplat: Online Feed-Forward Semantic 3DGS for Open-Vocabulary 3D Scene Understanding
📝 Summary:
EmbodiedSplat provides real-time 3D scene understanding, combining online 3D Gaussian Splatting with CLIP embeddings from streaming images. It simultaneously reconstructs and semantically comprehends 3D scenes using a novel sparse coefficients field and CLIP global codebook for efficiency and gen...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04254
• PDF: https://arxiv.org/pdf/2603.04254
• Project Page: https://0nandon.github.io/EmbodiedSplat/
• Github: https://github.com/0nandon/EmbodiedSplat
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#3DSceneUnderstanding #3DGaussianSplatting #ComputerVision #AI #NeuralRendering
📝 Summary:
EmbodiedSplat provides real-time 3D scene understanding, combining online 3D Gaussian Splatting with CLIP embeddings from streaming images. It simultaneously reconstructs and semantically comprehends 3D scenes using a novel sparse coefficients field and CLIP global codebook for efficiency and gen...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04254
• PDF: https://arxiv.org/pdf/2603.04254
• Project Page: https://0nandon.github.io/EmbodiedSplat/
• Github: https://github.com/0nandon/EmbodiedSplat
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#3DSceneUnderstanding #3DGaussianSplatting #ComputerVision #AI #NeuralRendering
❤1
Forwarded from Machine Learning with Python
Over 20 free courses are now available on our channel for a very limited time.
https://t.iss.one/DataScienceC
https://t.iss.one/DataScienceC
Telegram
Udemy Coupons
ads: @HusseinSheikho
The first channel in Telegram that offers free
Udemy coupons
The first channel in Telegram that offers free
Udemy coupons
✨GroupEnsemble: Efficient Uncertainty Estimation for DETR-based Object Detection
📝 Summary:
DETR models lack spatial uncertainty and current estimation methods are too costly. GroupEnsemble efficiently estimates uncertainty by using independent query groups in a single forward pass with an attention mask. This outperforms Deep Ensembles at a fraction of the cost.
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01847
• PDF: https://arxiv.org/pdf/2603.01847
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#ObjectDetection #UncertaintyEstimation #DETR #ComputerVision #MachineLearning
📝 Summary:
DETR models lack spatial uncertainty and current estimation methods are too costly. GroupEnsemble efficiently estimates uncertainty by using independent query groups in a single forward pass with an attention mask. This outperforms Deep Ensembles at a fraction of the cost.
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01847
• PDF: https://arxiv.org/pdf/2603.01847
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#ObjectDetection #UncertaintyEstimation #DETR #ComputerVision #MachineLearning
✨InfinityStory: Unlimited Video Generation with World Consistency and Character-Aware Shot Transitions
📝 Summary:
This paper introduces InfinityStory, a novel framework, dataset, and model for long-form video generation. It tackles challenges in background consistency and seamless multi-subject transitions, achieving high consistency and smoother transitions on VBench.
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03646
• PDF: https://arxiv.org/pdf/2603.03646
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#VideoGeneration #GenerativeAI #DeepLearning #AIResearch #ComputerVision
📝 Summary:
This paper introduces InfinityStory, a novel framework, dataset, and model for long-form video generation. It tackles challenges in background consistency and seamless multi-subject transitions, achieving high consistency and smoother transitions on VBench.
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03646
• PDF: https://arxiv.org/pdf/2603.03646
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#VideoGeneration #GenerativeAI #DeepLearning #AIResearch #ComputerVision
❤2
✨BeamPERL: Parameter-Efficient RL with Verifiable Rewards Specializes Compact LLMs for Structured Beam Mechanics Reasoning
📝 Summary:
BeamPERL improved a compact LLM's beam statics performance by 66.7% using RL with verifiable rewards. However, it learned procedural solution patterns rather than true physical reasoning, failing at topological shifts. This shows verifiable rewards alone dont guarantee transferable scientific rea...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04124
• PDF: https://arxiv.org/pdf/2603.04124
• Project Page: https://huggingface.co/collections/lamm-mit/beamperl
• Github: https://github.com/lamm-mit/BeamPERL
🔹 Models citing this paper:
• https://huggingface.co/lamm-mit/BeamPERL
✨ Datasets citing this paper:
• https://huggingface.co/datasets/lamm-mit/BeamRL-TrainData
• https://huggingface.co/datasets/lamm-mit/BeamRL-EvalData
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLM #ReinforcementLearning #BeamMechanics #AIResearch #DeepLearning
📝 Summary:
BeamPERL improved a compact LLM's beam statics performance by 66.7% using RL with verifiable rewards. However, it learned procedural solution patterns rather than true physical reasoning, failing at topological shifts. This shows verifiable rewards alone dont guarantee transferable scientific rea...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04124
• PDF: https://arxiv.org/pdf/2603.04124
• Project Page: https://huggingface.co/collections/lamm-mit/beamperl
• Github: https://github.com/lamm-mit/BeamPERL
🔹 Models citing this paper:
• https://huggingface.co/lamm-mit/BeamPERL
✨ Datasets citing this paper:
• https://huggingface.co/datasets/lamm-mit/BeamRL-TrainData
• https://huggingface.co/datasets/lamm-mit/BeamRL-EvalData
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLM #ReinforcementLearning #BeamMechanics #AIResearch #DeepLearning
arXiv.org
BeamPERL: Parameter-Efficient RL with Verifiable Rewards...
Can reinforcement learning with hard, verifiable rewards teach a compact language model to reason about physics, or does it primarily learn to pattern-match toward correct answers? We study this...
✨Qwen Technical Report
📝 Summary:
Qwen is a series of large language models encompassing base, chat, coding, and mathematics variants. These models consistently achieve superior performance across diverse tasks, significantly outperforming open-source counterparts. Qwen-Chat models also feature advanced tool-use and planning capa...
🔹 Publication Date: Published on Sep 28, 2023
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2309.16609
• PDF: https://arxiv.org/pdf/2309.16609
• Github: https://github.com/QwenLM/Qwen-7B
🔹 Models citing this paper:
• https://huggingface.co/Qwen/Qwen-7B-Chat
• https://huggingface.co/Qwen/Qwen-7B
• https://huggingface.co/Qwen/Qwen-14B-Chat
✨ Datasets citing this paper:
• https://huggingface.co/datasets/huyxdang/qwen-medqa-tagged
• https://huggingface.co/datasets/huyxdang/qwen-math-predictions
✨ Spaces citing this paper:
• https://huggingface.co/spaces/pliny-the-prompter/obliteratus
• https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard
• https://huggingface.co/spaces/lhoestq/fake-data-generator-jsonl
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#Qwen #LLM #AI #NLP #DeepLearning
📝 Summary:
Qwen is a series of large language models encompassing base, chat, coding, and mathematics variants. These models consistently achieve superior performance across diverse tasks, significantly outperforming open-source counterparts. Qwen-Chat models also feature advanced tool-use and planning capa...
🔹 Publication Date: Published on Sep 28, 2023
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2309.16609
• PDF: https://arxiv.org/pdf/2309.16609
• Github: https://github.com/QwenLM/Qwen-7B
🔹 Models citing this paper:
• https://huggingface.co/Qwen/Qwen-7B-Chat
• https://huggingface.co/Qwen/Qwen-7B
• https://huggingface.co/Qwen/Qwen-14B-Chat
✨ Datasets citing this paper:
• https://huggingface.co/datasets/huyxdang/qwen-medqa-tagged
• https://huggingface.co/datasets/huyxdang/qwen-math-predictions
✨ Spaces citing this paper:
• https://huggingface.co/spaces/pliny-the-prompter/obliteratus
• https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard
• https://huggingface.co/spaces/lhoestq/fake-data-generator-jsonl
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#Qwen #LLM #AI #NLP #DeepLearning
arXiv.org
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this...
✨MIBURI: Towards Expressive Interactive Gesture Synthesis
📝 Summary:
MIBURI is an online, real-time framework generating expressive full-body gestures and facial expressions for spoken dialogue. It uses body-part aware codecs and LLM embeddings to create natural, diverse, and contextually aligned motions causally, overcoming limitations of prior methods.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03282
• PDF: https://arxiv.org/pdf/2603.03282
• Project Page: https://vcai.mpi-inf.mpg.de/projects/MIBURI/
• Github: https://github.com/m-hamza-mughal/miburi
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#GestureSynthesis #AI #HumanComputerInteraction #NLP #RealtimeTech
📝 Summary:
MIBURI is an online, real-time framework generating expressive full-body gestures and facial expressions for spoken dialogue. It uses body-part aware codecs and LLM embeddings to create natural, diverse, and contextually aligned motions causally, overcoming limitations of prior methods.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03282
• PDF: https://arxiv.org/pdf/2603.03282
• Project Page: https://vcai.mpi-inf.mpg.de/projects/MIBURI/
• Github: https://github.com/m-hamza-mughal/miburi
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#GestureSynthesis #AI #HumanComputerInteraction #NLP #RealtimeTech