TinyML is the most impressive piece of software you can run on any ESP32
xda-developers.com·4d
🚀MLOps
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Augmenting learning in neuro-embodied systems through neurobiological first principles
arxiv.org·9h
🤖Transformers
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Information Gain-based Policy Optimization: A Simple and Effective Approach forMulti-Turn LLM Agents
Deep Learning Approach to Anomaly Detection in Enterprise ETL Processes with Autoencoders
arxiv.org·9h
🧠Machine Learning
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Self-Harmony: Learning to Harmonize Self-Supervision and Self-Play in Test-Time Reinforcement Learning
arxiv.org·9h
🚀MLOps
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Prompt Injection as an Emerging Threat: Evaluating the Resilience of Large Language Models
arxiv.org·9h
🚀MLOps
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MISA: Memory-Efficient LLMs Optimization with Module-wise Importance Sampling
arxiv.org·9h
🤖Transformers
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Multi-Representation Attention Framework for Underwater Bioacoustic Denoising and Recognition
arxiv.org·1d
👁️Computer Vision
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Physics-Informed Neural Network Frameworks for the Analysis of Engineering and Biological Dynamical Systems Governed by Ordinary Differential Equations
arxiv.org·9h
🧠Machine Learning
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Learning and Leveraging Anisotropy Parameters in ANOVA Approximation
arxiv.org·9h
🗄️Vector Databases
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Isotropic Curvature Model for Understanding Deep Learning Optimization: Is Gradient Orthogonalization Optimal?
arxiv.org·9h
🤖AI
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Optimizing Native Sparse Attention with Latent Attention and Local Global Alternating Strategies
arxiv.org·9h
🤖Transformers
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Beyond ImageNet: Understanding Cross-Dataset Robustness of Lightweight Vision Models
arxiv.org·9h
👁️Computer Vision
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Efficient Test-Time Retrieval Augmented Generation
arxiv.org·9h
🔍RAG
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Feature Importance Guided Random Forest Learning with Simulated Annealing Based Hyperparameter Tuning
arxiv.org·9h
🧠Machine Learning
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Saliency-Guided Domain Adaptation for Left-Hand Driving in Autonomous Steering
arxiv.org·9h
👁️Computer Vision
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Latent Domain Prompt Learning for Vision-Language Models
arxiv.org·9h
🤖Transformers
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