Novel view synthesis has witnessed significant advancements recently, with Neural Radiance Fields (NeRF) pioneering 3D representation techniques through neural rendering. While NeRF introduced innovative methods for reconstructing scenes by accumulating RGB values along sampling rays using multilayer perceptrons (MLPs), it encountered substantial computational challenges. The extensive ray point sampling and large neural network volumes created…
The advancements in large language models (LLMs) have significantly enhanced natural language processing (NLP), enabling capabilities like contextual understanding, code generation, and reasoning. However, a key limitation persists: the restricted context window size. Most LLMs can only process a fixed amount of text, typically up to 128K tokens, which limits their ability to handle tasks…
Open Source LLM development is going through great change through fully reproducing and open-sourcing DeepSeek-R1, including training data, scripts, etc. Hosted on Hugging Face’s platform, this ambitious project is designed to replicate and enhance the R1 pipeline. It emphasizes collaboration, transparency, and accessibility, enabling researchers and developers worldwide to build on DeepSeek-R1’s foundational work. What…
Mixture-of-Experts (MoE) models utilize a router to allocate tokens to specific expert modules, activating only a subset of parameters, often leading to superior efficiency and performance compared to dense models. In these models, a large feed-forward network is divided into smaller expert networks, with the router—typically an MLP classifier—determining which expert processes each input. However,…
Reinforcement learning (RL) focuses on enabling agents to learn optimal behaviors through reward-based training mechanisms. These methods have empowered systems to tackle increasingly complex tasks, from mastering games to addressing real-world problems. However, as the complexity of these tasks increases, so does the potential for agents to exploit reward systems in unintended ways, creating new…
Generative modeling challenges in motion-controllable video generation present significant research hurdles. Current approaches in video generation struggle with precise motion control across diverse scenarios. The field uses three primary motion control techniques: local object motion control using bounding boxes or masks, global camera movement parameterization, and motion transfer from reference videos. Despite these approaches, researchers…
Advancements in multimodal intelligence depend on processing and understanding images and videos. Images can reveal static scenes by providing information regarding details such as objects, text, and spatial relationships. However, this comes at the cost of being extremely challenging. Video comprehension involves tracking changes over time, among other operations, while ensuring consistency across frames, requiring…
The artificial intelligence (AI) landscape is evolving rapidly, but this growth is accompanied by significant challenges. High costs of developing and deploying large-scale AI models and the difficulty of achieving reliable reasoning capabilities are central issues. Models like OpenAI’s GPT-4 and Anthropic’s Claude have pushed the boundaries of AI, but their resource-intensive architectures often make…
AI has entered an era of the rise of competitive and groundbreaking large language models and multimodal models. The development has two sides, one with open source and the other being propriety models. DeepSeek-R1, an open-source AI model developed by DeepSeek-AI, a Chinese research company, exemplifies this trend. Its emergence has challenged the dominance of…
As large language models (LLMs) continue to evolve, understanding their ability to reflect on and articulate their learned behaviors has become an important aspect of research. Such capabilities, if harnessed, can contribute to more transparent and safer AI systems, enabling users to understand the models’ decision-making processes and potential vulnerabilities. One of the biggest challenges…