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Explore SeaLLMs, innovative language models focusing on Southeast Asian languages. Learn about their superior performance in linguistic tasks and instruction-following capabilities for non-Latin languages.
Explore domain adaptation techniques for autonomous driving, from standard unsupervised setups to practical approaches addressing open-world challenges in semantic segmentation and scene understanding.
Explore cutting-edge AI innovations at VinAI, led by Dr. Hung Bui. Discover advancements in probabilistic models, deep learning, and human-machine understanding across various applications.
Explore implicit deep learning models, their state-space representation, training problems, and applications in parameter reduction, feature elimination, and mathematical reasoning tasks.
Explore an integrated framework for controllable text generation, addressing challenges in interpretability, data construction, and evaluation methods for natural language processing applications.
Explore Qualcomm's Cloud AI 100 accelerator for high-performance cloud-edge inferencing, featuring leadership-class performance, power efficiency, and comprehensive software tools for production deployment.
Explore probabilistic and machine learning approaches for autonomous robots and automated driving, focusing on robust perception and action execution in uncertain environments.
Explore ethical considerations and unexpected consequences of AI, including racism in AI assistants, robot rights, self-driving car accidents, and facial recognition limitations.
Explore conditional transport as a new divergence for comparing probability distributions, with applications in generative modeling and resistance to mode collapse.
Explore model-aware machine learning methods combining scientific computing and data-driven models. Learn about ROM-ML and Autoencoder-based Inversion approaches for PDE-constrained inverse problems.
Explore compositional learning for robot autonomy through modularity and abstraction. Discover neuro-symbolic planners for long-horizon manipulation tasks and state-action abstractions from raw signals.
Explore network regression and inference techniques for analyzing complex network data, focusing on estimating regression coefficients with network effects and addressing observational errors.
Explore the mean field limit in neural network learning, examining optimization dynamics, convergence to global optima, and data representation in autoencoders. Gain insights from physics analogies.
Explore key aspects of generalizable autonomy in robotics, including representational biases, causal inference, and interactive policy learning under uncertainty for efficient and adaptable AI systems.
Explore nonlinear feature selection methods for high-dimensional, small sample data. Learn about HSIC Lasso, kernel PSIs, and feature selection networks for advanced machine learning applications.
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