AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,544 papers
Molecular generative models can rapidly explore chemical space, but conventional metrics alone do not establish whether proposed structures possess application-relevant physical properties. To address this limitation, we developed an integrated generationvalidation framework coupling Reversible Junction Tree Reinforcem...
In reinforcement learning, an agent learns to map representations of the environment state to predictions of future reward. Most prior work in neuroscience has assumed a fixed representation and studied how reward prediction errors (thought to be conveyed by phasic dopamine signals) are used to update the mapping from...
The proposed physics-guided deep reinforcement learning framework based on a Physics-Guided Deep Q-Network for multi-objective EV energy management under varying grid operating conditions demonstrates the potential to improve energy efficiency, reduce degradation-related battery stress, and support stable grid interact...
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The utilization of artificial intelligence (AI) is transforming underwater surveillance. It has made underwater surveillance smart, autonomous, and real-time. This chapter discusses the ways that AI technologies such as machine learning, deep learning, computer vision, and reinforcement learning are being incorporated...
This study aims to determine and describe the role of classroom teachers in shaping students' positive learning habits at SDN 1 Kayangan. A descriptive qualitative research approach was deployed. Data collection was carried out through observation, interviews, and documentation involving ten informants who were selecte...
Although underwater ecosystems have many species and provide economic benefits, they are now being threatened by actions such as unregulated fishing, smuggling by sea, underwater attacks, and violations of boundaries. Older ways of surveillance find it tough to follow what happens in these big and complex waters due to...
Existing multi-agent reinforcement learning (MARL) approaches for traffic signal control often fail to capture two critical characteristics of real-world urban traffic: the dynamic spatio-temporal propagation of congestion across intersections and the frequent imperfection of sensor data. To address these fundamental g...
In reinforcement learning, an agent learns to map representations of the environment state to predictions of future reward. Most prior work in neuroscience has assumed a fixed representation and studied how reward prediction errors (thought to be conveyed by phasic dopamine signals) are used to update the mapping from...
This paper aims to develop FairViT-ShrikeNet , which is a hybrid system that combines attention motivated deep reinforcement learning, Vision Transformer workload encoding, and Shrike metaheuristic optimization to realise fairness sensitive and cost effective resource allocation in cloud computing systems. The approach...
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by NVIDIA on Hugging Face
A Blog post by IBM Research on Hugging Face
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
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